Humans of Martech

What's up everyone, today we have the pleasure of sitting down with Austin Hay, Martech, Revtech, and GTM systems advisor, AND – AI builder, writer, and ex-founder. In This Episode:(00:00) - Austin-audio (01:16) - In This Episode (01:54) - Sponsor: RevenueHero (02:48) - Sponsor: Mammoth Growth (04:09) - How Code-Driven AI Workflows Outperform Chat-Based Prompting (14:55) - How to Start Building With Claude Code When You Have No Time (19:45) - The Programming Concepts Non-Developers Need to Build With Claude Code (23:49) - How to Turn Repeating Prompts Into Automations That Run Themselves (31:11) - Sponsor: MoEngage (32:07) - Sponsor: Knak (33:37) - Why Spending All Your Time in Meetings Is a Career Liability (36:28) - Why the Best First Claude Code Project Is the Task That Already Annoys You (40:22) - Why T-Shaped Marketers With Claude Code Will Cover the Work of Entire Teams (46:27) - Why Marketing Taste Matters More Than Technical Skill in the AI Era (49:43) - How Early-Career Professionals Build Judgment When Entry-Level Work Gets Automated (53:14) - How Austin Hay Runs His Career as a Flywheel Austin Hay has spent 15 years moving between the technical and strategic ends of marketing, starting as the 4th employee at Branch, building and selling a mobile growth consultancy that was acqui-hired by mParticle, and eventually rising to VP of Growth before moving on to Ramp as Head of Martech. He later co-founded Clarify, a CRM startup he took from zero to $100K+ ARR while completing a Wharton MBA. Today he works as a fractional advisor to scaling companies on martech, revtech, and GTM systems, teaches thousands of practitioners through his Martech course at Reforge, and writes the Growth Stack Mafia newsletter on Substack.Austin spent months as a chatbot skeptic before Claude Code changed his view entirely. In this conversation, he maps the gap between using AI through a chat interface and wielding it as code in your actual environment, explains why meeting-heavy schedules are a compounding career liability, and makes the case for a new class of professional he calls the white collar super saiyan.---## How Code-Driven AI Workflows Outperform Chat-Based PromptingMost marketers use AI the same way they used Google in 2005. Open the interface, type something in, read what comes back, copy it somewhere. Austin Hay did this for months. He was not an early Claude Code adopter. He says this upfront, almost as a confession. He thought it was another chatbot.What broke him was specific. He was querying financial data at his startup, Clarify, through Runway, an FP&A platform connected to QuickBooks. Every SQL change required the same round trip: write the query in terminal, copy it to Claude, get feedback, paste it back, run it. He built a folder just to manage the back-and-forth. The model couldn't see his local files. The chat UI had upload limits. He was stuck in what he calls a world of calling and answering. Functional. But slow. And bounded in a way you eventually stop ignoring.Claude Code gave him access. When you type claude in a terminal, the model reads your actual files — the data as it lives in your repository, not a paste you copied, not a summary you wrote. It runs commands against your system, observes what happens, and acts on the result. The round trip ends. You stop relaying information and start working in the same environment. That is a different thing than a smarter chatbot.The shift combined with several unlocks arriving at once: Opus as a model, MCPs that worked reliably, a Max plan that made unlimited credits economical, and an agent architecture built around memory files and commands. All of it hit critical mass for Austin in January. He says the last 6 months felt like 3 years. You can hear in how he talks about it that he means it.The 2 chasms he had written about in his newsletter turned out to be real and distinct. Adopting AI at all is chasm 1. Crossing from chat to code is chasm 2. Most practitioners have cleared the first. Almost none have cleared the second. And the view from the other side, Austin says, is unrecognizable.> "It's this culmination of many things that I think really hit this critical mass in about January of this year."Key takeaway: Install Claude Code, open a terminal, point it at a folder with files you actually work with — SQL queries, drafts, data exports, notes — and run a real task on them. The gap between giving AI access to your environment and describing your environment through a chat window is immediate and felt, and that feeling is what changes the mental model.---## How to Start Building With Claude Code When You Have No TimeThe time problem is real. You have a 9-to-5. Your weekends disappear. Nobody at your company is running AI hackathons. "Learn the command line" is not advice you can act on between your Thursday syncs.Austin doesn't dismiss this. But he points at the part most people miss: they know step 1 (chat interface) and they see step 3 (Clau

What’s up everyone, today we have the pleasure of sitting down with Dr. John Whalen, Cognitive Scientist, Author, and Founder at Brilliant Experience.Summary: John has spent his career studying how people actually think, and his conclusion is uncomfortable for anyone who believes their marketing decisions are more rational than they are. In this episode, John explores how synthetic users built from cognitive science principles can fill the massive research gap that most teams quietly ignore, and why removing the human interviewer from the room might be the fastest way to finally hear the truth.In this Episode…(00:00) - Intro (01:13) - In This Episode (04:31) - What Are Synthetic Users and Why Do They Matter? (10:00) - How Synthetic Users Make Stakeholders Hungry for Real Human Research (15:56) - Pre-Testing on Synthetic Users: Shortcut or Smart Step? (18:53) - How to Actually Build a Synthetic User: Tools, Layers, and Agentic Systems (40:51) - Is the Average Persona Dead? Scale, Diversity, and the World Model (43:01) - Asking the Uncomfortable Questions: What AI Agents Reveal That Humans Won't (49:30) - Ending the Quant vs. Qual Debate with Statistically Relevant Qualitative Data (56:37) - Mining the 'Why' Behind Silent Behavioral Data with Synthetic Users (01:02:31) - Designing for Agent Users: The Coming Shift to Human-and-Machine-Centered Design (01:05:28) - The Happiness Question: Dogs, Nature, and Staying Analog About JohnDr. John Whalen is a Cognitive Scientist, Author, and Founder of Brilliant Experience, where he applies cognitive science principles to help organizations design products and experiences that align with how people actually think and make decisions. He’s also an educator, teaching two AI customer research courses on Maven.His work explores the intersection of human psychology and marketing, including the emerging practice of pre-testing ideas on synthetic users to give brands a faster and more informed competitive edge. He is also the author of a book on the science of designing for the human mind, bringing academic rigor to practical business challenges.How Synthetic User Research Works and When to Trust ItSynthetic user research sounds like something creepy out of a dystopian science fiction film, and John is the first to admit the terminology does nobody any favors. When asked about what synthetic users actually are and what they mean for research, he admited: if he had been on the branding team, he would have pushed hard for something like “dynamic personas” instead. The name creates unnecessary friction before the conversation even starts. And that friction matters when you’re trying to get skeptical executives or methiculous researchers to take the whole thing seriously.Under the hood, specialized AI tools simulate how a defined audience segment would respond to a question, concept, or stimulus, without recruiting, scheduling, incentivizing, or waiting on real human participants. John runs a class where he collects genuine human data first, then feeds comparable inputs into these tools to benchmark accuracy head-to-head. The results are pretty wild. AI-generated responses align with real human findings somewhere between 85% and 100% of the time on major topics and consumer needs. That is not a peer-reviewed clinical trial, and John is not pretending otherwise. But 85% alignment is enough signal to stop reflexively dismissing the method and start asking harder, more specific questions about exactly where it fits into a research stack.So what does this mean for you and your company though? Think all the decisions that currently live in a black hole of zero structured input. How many product calls, campaign concepts, and messaging pivots happen with nothing more than a conference room full of people who all read the same talking heads on LinkedIn? John argues that low cost, round-the-clock accessibility, and minimal public exposure make these tools a natural fit for precisely those moments: pressure-checking a hypothesis at 11pm, testing whether a pitch direction even makes sense before it touches a client, or deciding whether a concept deserves the time and money required for proper validation.“If these are only going to keep getting better and better, which they are, then logically, what kinds of decisions right now go completely by gut and no research, and what could we use to help us frame that?”One of the more underappreciated angles John raises is global inclusivity. Large organizations routinely test in the US and Western Europe, then extrapolate those findings to markets in Southeast Asia, Latin America, or Sub-Saharan Africa because local research budgets simply do not exist. Big nono. Synthetic personas trained on broader, more representative data could at minimum provide directional signals for those markets, making research more geographically honest without a proportional spike in spend.The early AI bias problem, where models essentially mirrored the worldview of a narr

Summary: Tobi challenged marketing’s fixation on prediction. He has built highly accurate LTV models, but accuracy alone does not move revenue. Marketing is intervention. Correlation shows patterns; causality tells you what happens when you pull a lever. That shift reshapes experimentation, explains why dynamic allocation can outperform static A B tests, and highlights how self learning systems can backfire or get stuck in local maxima. It also fuels his skepticism of unleashing agentic AI on historical data without a causal layer. If you want to change outcomes instead of forecast them, your systems need to understand levers and log decisions you can actually audit.(00:00) - Intro (01:22) - In This Episode (04:07) - Why Predictive Models Fail Without Causal Inference (09:49) - How to Validate Causal Impact on Customer Lifetime Value (13:04) - Reducing Uncertainty Around Causal Effects by Optimizing Levers, Not Labels (17:01) - Why Dynamic Allocation Works Better Than Fixed Horizon A B Testing (31:54) - The Boomerang Effect and Why Uninformed AI Sabotages Early Results (40:15) - Escaping Local Maxima and The Failure of Randomly Initialized Decisioning (44:04) - Why Agentic AI Trained on Data Warehouse Correlations Reinforces Bias (49:00) - The Power of Composable Decisioning (53:06) - How Machine Decisioning Transcends Marketing (01:01:41) - Why Clear Priority Hierarchies Improve Executive Decision Making About TobiasTobias Konitzer, PhD is VP of AI at GrowthLoop, where he’s chasing closed-loop marketing powered by reinforcement learning, causality, and agentic systems. He’s spent the past decade focused on one core problem: moving beyond prediction to actually influencing outcomes.Previously, Tobi was Chief Innovation Officer at Fenix Commerce, helping major eCommerce brands modernize checkout and delivery with machine learning. He also founded Ocurate, a venture-backed startup that predicted customer lifetime value to optimize ad bidding in real time, raising $5.5M and scaling to $500K+ ARR before its acquisition. Earlier, he co-founded PredictWise, building psychographic and behavioral targeting models that drove over $2M in revenue.Tobi earned his PhD in Computational Social Science from Stanford and worked at Facebook Research on large-scale ML and bias correction. Originally from Germany and based in the Bay Area since 2013, he writes frequently about causal thinking, machine decisioning, and the future of marketing.Why Predictive Models Fail Without Causal InferencePrediction dominates most marketing roadmaps. Teams invest months refining churn models, tightening confidence intervals, and debating which threshold deserves a campaign. Tobi built an entire company on that logic. His team produced highly accurate lifetime value predictions using deep learning and granular event data. The forecasts were sharp. The lift curves were clean. Buyers were impressed.Then lifecycle marketers asked a more uncomfortable question: what action should follow the score?A predictive model encodes the current trajectory of a customer under existing policies. It describes what will likely happen if nothing changes. Marketing changes things constantly. The moment you intervene, you alter the system that generated the prediction. The forecast reflects yesterday’s conditions, not tomorrow’s strategy.> “Prediction tells you the future if you do nothing. Causation tells you how to change it.”Consider the Prediction Trap.On the left, the status quo labels a person as high churn risk. The function is observation. The outcome is a description of what happens if you leave the system untouched. On the right, a lever gets pulled. The function is intervention. The outcome is directional change.That shift in function changes how you work.Prediction thinking centers on segmentation:Who is likely to churn?Who is likely to buy?Who looks like high LTV?Causal thinking centers on levers:Which incentive reduces churn?Which sequence increases repeat purchase?Which offer raises lifetime value incrementally?Tobi often uses an LTV example to expose the trap. Suppose high LTV customers frequently viewed a specific product early in their journey. A team might redesign the onboarding flow to feature that product more aggressively. The correlation looks persuasive. The causal effect remains unknown.Several alternative explanations could drive the pattern:The product may correlate with a specific acquisition channel.The product may have been highlighted during a limited campaign.The product view may signal prior brand familiarity.Only an intervention test can estimate incremental impact. Correlation can guide hypothesis generation, but it cannot validate the lever itself.Tobi also highlights a deeper issue. Acting on predictions introduces compounding uncertainty across multiple layers:The predictive model carries statistical variance.The translation from model features to campaign strategy introduces interpretation bias.The experiment introduces

What’s up everyone, today we have the pleasure of chatting with Jenna Kellner, VP Marketing at Workleap.(00:00) - Intro (01:14) - In This Episode (04:30) - How to Manage Marketing Tech Debt During Rapid Growth (10:10) - How to Prioritize RevOps Tech Debt Without Perfect ROI Models (14:23) - Reasoning Through Broken Systems and Imperfect Data (19:23) - How High Performers Progress Anyway (24:28) - How to Build Confidence With AI Through Small Experiments (33:06) - How to Use Exit Planning and Cost Benefit Analysis for AI Tool Selection (35:57) - First principles matter more than tools (38:59) - Why Staying Close to Execution Improves Marketing Leadership (45:13) - Why Critical Thinking Skills Drive Marketing Career Growth (49:33) - How to Build Business Judgment in Technical Marketing Roles (53:03) - Why Confidence Without Humility is Dangerous (55:47) - How Revenue Leaders Prioritize Daily Energy (59:49) - Growing up (01:01:10) - Book rec Summary: Jenna is a VP of marketing that can talk about the weeds of messy systems, uncertain decisions, and personal growth. You can’t hide from it, every company accumulates tech debt as teams rush to hit revenue targets. She frames tech debt as a leadership responsibility and urges executives to reinvest in core systems when patchwork begins to outweigh building. If leadership doesn’t get it, the best way to prioritize it is to shape it as an opportunity cost and lost leverage that will drain revenue the longer we wait. In the face of AI uncertainty, she argues that judgment compounds faster than technical knowledge, and that the marketers who become indispensable blend business awareness, proximity to execution, and decisive action grounded in humility.About JennaJenna Kellner is Vice President of Marketing at Workleap and a revenue-focused marketing leader who has spent more than a decade building marketing teams and scaling companies. She brings experience across Enterprise, SMB, D2C, SaaS, two-sided marketplaces, venture studios, and other high-growth environments.Her career spans senior leadership roles at Minerva, On Deck, RBCx, and Ownr, where she led marketing, growth, and revenue functions inside complex, evolving organizations. At RBCx, she served as Chief Growth Officer for Ampli and directed marketing and growth initiatives within a large financial institution setting. She has also co-founded communities such as GrowthToronto and Little Traders, reflecting her commitment to building networks and businesses in parallel.Jenna operates with a strong sense of ownership and accountability, grounded in her belief that every challenge ultimately becomes her responsibility to solve. Recognized as a WXN Top 100 Women in Canada, she focuses on developing high-performing teams that connect strategy to execution and translate marketing into measurable revenue impact.The Frankenstein Reality of Managing Tech Debt: How to Manage Marketing Tech Debt During Rapid GrowthYou know it.. Most marketers are operating inside half-connected systems. No company has a pristine, perfectly synchronized tech stack. Even if they think they do, it doesn’t last. Growth creates pressure, and pressure produces shortcuts. Jenna has seen the same cycle in startups and enterprise environments. In the early days, teams build whatever gets the job done. They start in spreadsheets, layer on point solutions, wire tools together with lightweight integrations, and move fast because revenue matters more than architecture.Those early decisions never disappear. They compound. Years later, larger organizations inherit layers of systems that were added at different stages of maturity. Tools do not scale in sync. One platform gets upgraded. Another stays frozen because a team depends on it. Reporting becomes an exercise in orchestration. Jenna recalls walking into an organization where a sales leader pulled her weekly report from eight separate tools. That routine consumed time, drained energy, and normalized operational friction.“You have to Frankenstein your way through them to get the answers you need.”That sentence captures the daily reality inside many marketing and revenue teams. Quarter-end reporting still happens. Board decks still go out. The numbers get assembled through exports, CSV files, manual joins, and late-night reconciliation. Leadership often tolerates the strain because revenue continues to land. But the cost isn’t super visible:Reporting cycles stretch longer each quarter.Forecast confidence erodes.Team morale dips as manual work expands.Strategic decisions rely on partial or inconsistent data.So how do we get out of this mess? Jenna views this as a leadership obligation. Someone has to decide that cleaning house earns priority alongside pipeline generation. She describes working with a founder who paused other initiatives to repair core systems. The work moved slowly. It required budget discipline and uncomfortable trade-offs. It rebuilt trust in data and freed leaders from cobbled-to

What’s up folks, today we have the pleasure of sitting down with Ronald Gaines, Digital Transformation & Marketing Ops Leader at Sunbelt Rentals, Inc.(00:00) - Intro (01:12) - In This Episode (06:18) - 1. Learning to Operate Without Formal Authority (13:59) - 2. Stop Waiting for the Org to Define Your Marketing Ops Role (22:53) - 3. The Hidden Cost of Self Taught Ops and Minimum Viable Discipline (31:46) - 4. Thinking in Products Instead of Tasks (39:15) - 5. Data Discipline Outlasts Any Platform (48:38) - 6. How to Design a Marketing Ops Intake Process That Protects Team Capacity (52:18) - Personal Energy Allocation Framework For Marketing Ops Leaders Summary: Ronald shares a framework for marketing operations leaders to move from reactive support into proactive systems authority by building influence through measurable credibility, structured intake processes, and disciplined governance. It argues that operational work should be managed like a product with clear boundaries, documented standards, and strong data discipline, which protects team capacity, prevents burnout, and makes impact visible to the business. By defining their own role and communicating value in commercial terms, operators convert technical execution into durable strategic leverage.About RonaldRonald Gaines is a Digital Transformation and Marketing Operations leader who builds scalable revenue engines across complex enterprise environments. He combines strategic direction with hands-on expertise in marketing automation, data architecture, analytics, and customer experience optimization.As Senior Manager of MarTech and Data Analytics at Sunbelt Rentals, he leads the enterprise martech roadmap, governs lead management and data integrity, and aligns marketing technology with measurable revenue outcomes. His experience across Cisco, Dell, and global consulting engagements reflects a consistent focus on operational rigor, system design, and performance-driven growth.Outside of work, Ronald is a dedicated fan of comic books and graphic novels, with a particular appreciation for mech stories and towering kaiju battles. He is also launching a nonprofit focused on building youth leaders and strengthening communities, speaks at career days to introduce young people to digital marketing, and is committed to serving families and helping the next generation build a path toward a thriving, stable quality of life.1. Learning to Operate Without Formal AuthorityMarketing ops leaders operate at the center of execution. Campaigns depend on them for tracking, lifecycle depends on them for clean product data, and growth teams depend on them for accurate reporting. Work flows through their systems every day. Authority often sits somewhere else.We describe this tension as an authority paradox. You touch everything. You own very little. Influence becomes the mechanism that moves work forward.Ronald believes influence grows from operational credibility. Ops leaders who become indispensable demonstrate rigor and produce dependable outcomes with quantifiable business impact. They can show how their work reduces launch time, decreases system incidents, improves data accuracy, or drives measurable revenue lift. When the numbers are visible, stakeholders treat the function differently.“If you cannot quantify the work that you’re doing for the business and the impact that it is making, it becomes very hard to have the influence and authority you need to push back and protect your bandwidth.”That perspective shifts the conversation from personality to proof. Relational influence still matters. Cross functional trust smooths collaboration. Operational influence carries more weight because it compounds. When a team consistently delivers outcomes that are measured and shared, credibility grows with each cycle.Ronald points to structure as the starting point. A centralized intake process creates visibility and discipline. A mature intake process includes:A required business outcome for every request.An estimated level of effort based on real sizing.A defined metric tied to revenue, cost savings, risk reduction, or speed.A transparent prioritization rubric that stakeholders can review.When every request moves through this filter, conversations become sharper. Trade offs move from hallway debates to documented decisions. You protect capacity because the impact is visible. You prioritize high value work because the math supports it.He also encourages ops leaders to create formal deliverables that showcase impact. Publish a quarterly ops impact report. Share a dashboard that tracks launch velocity. Track incident reduction over time. Circulate a capability roadmap tied to revenue targets. These artifacts signal accountability. Accountability grants the authority to set priorities and allocate resources.Influence grows when stakeholders associate your involvement with consistent business gains. Teams start asking for your perspective earlier in the planning process. Leader

What’s up everyone, today we have the pleasure of sitting down with Maria Solodilova, Head of Business Development at Yango Ads.(00:00) - Intro (01:17) - In This Episode (04:23) - Mobile Ad Mediation Business Development Explained (09:58) - AI Credibility In Ad Tech Sales (18:42) - Why Adtech is Really a Marketplace With Its Own Economics (30:30) - Programmatic Ad Auctions And Inventory Dynamics (35:22) - Building Trust in Programmatic Advertising Transparency (43:39) - The Future of Contextual Advertising (46:47) - Buy-in Tip (48:03) - Books Recommendations (51:07) - Happiness System Summary: Maria takes us on a guided tour across the adtech landscape from a bird’s-eye view, describing a real-time marketplace where mobile ad mediation converts app usage into revenue through auctions that price every impression. She explains how supply-side work at Yango Ads centers on SDK integration, auction behavior, and performance tradeoffs that directly shape earnings once systems operate in production. The conversation frames adtech as a market governed by supply, demand, and incentives, which explains why performance shifts often outrun planning models and attribution frameworks. She grounds AI and transparency in observable mechanics, showing how reconciled data, clear ownership, and contextual execution support trust and durable monetization.About MariaMaria Solodilova leads global business development at Yango Ads, where she oversees revenue growth and strategic partnerships for an AI-driven mobile ad monetization platform. She manages distributed teams across the United States, China, Southeast Asia, and Latin America, with consistent delivery of seven-figure quarterly revenue and sustained performance above enterprise sales targets.Her career spans more than a decade across North America, Europe, and Latin America, with senior roles in AdTech, SaaS, and LegalTech. Before joining Yango Ads, Maria led international business development at Yandex, where she launched AI-based B2B products into APAC, LATAM, and MENA markets, shortened sales cycles through stronger qualification, and increased average contract value.Earlier roles at BrandMonitor and KidZania placed her in direct collaboration with Fortune 500 brands and executive leadership teams on complex, multi-market commercial partnerships. Her work consistently centers on enterprise sales execution, partner ecosystems, and monetization strategy in competitive mobile and platform-driven markets.Mobile Ad Mediation Business Development ExplainedMobile ad mediation explains how free apps generate revenue without charging users directly. The system converts attention into income through auctions that run inside apps every time an impression becomes available. Maria frames the work in plain terms when she talks to people outside adtech. Users open familiar apps, skip payment screens, and still participate in a transaction. Attention becomes the currency, and ads become the exchange mechanism.“When you are not paying for the product, chances are you might be one. You are paying with your attention.”Mediation platforms sit at the center of that exchange. Multiple ad networks bid for each impression in real time, and the highest bid wins access to a specific user. Maria’s role focuses on the supply side at Yango Ads, where her team works with mobile app developers and game studios. They integrate the SDK, tune performance, and make sure the auction behaves in ways that maximize revenue without degrading the app experience.The work demands technical fluency because developers expect concrete answers. A normal week includes discussions about factors that materially affect earnings, such as:SDK weight and its impact on app performance.Latency and how slow auctions affect fill rates.Competition density across ad networks.User experience tradeoffs that influence retention and ad tolerance.These conversations move quickly from high-level strategy to implementation details. Credibility depends on understanding how the auction behaves in production, not how it sounds in a pitch.The revenue dynamics often surprise people. Large payouts do not always come from enterprise publishers with recognizable logos. Maria has seen individual developers build a single game, monetize through ads, and generate seven-figure income. These outcomes come from timing, execution, and exposure to competitive bidding, rather than procurement cycles or brand recognition. That possibility keeps many operators engaged in the space, even as the vocabulary around ads grows tired and recycled.Business development in mediation operates as a bridge between market mechanics and human outcomes. The role connects developers who want predictable income with systems that price attention at scale. Clear explanations, technical competence, and realistic expectations shape long-term partnerships more than lofty promises ever could.Key takeaway: Mobile ad mediation monetizes attention through real-time auctions betw

What’s up folks, today we have the pleasure of sitting down with Anthony Rotio, Chief Data Strategy Officer at GrowthLoop.(00:00) - Intro (01:10) - In this episode (04:05) - Journeying From Robotics to Modern Marketing Systems (11:05) - Most Marketing Systems Don’t Learn Because They Lack Feedback Loops (16:10) - The Martech Engineering Talent Gap (19:51) - AI Will Amplify Whoever Has the Cleanest Causal Feedback Loop (29:17) - Agent Context Graphs for Drift Detection in Marketing Systems (31:51) - Humans Will Set Hypotheses, AI Will Accelerates Iteration (35:50) - The Evolution of Retail Media Networks (45:07) - How Commerce Networks Redefine Targeting With Governed Data (48:26) - How Agent to Agent Commerce Operates Inside Marketing Funnels (53:04) - Google Universal Commerce Protocol Explained (54:43) - Personal Happiness System (56:30) - Favorite Books Summary: Anthony traces a path from robotics and computer science to his current role where he approaches marketing as an engineering system. He explains how execution-first marketing stacks weaken feedback loops and fragment data, which slows learning and iteration. He introduces the agent context graph as a causality model that lets AI simulate and predict customer behavior with greater confidence. The conversation also covers retail media networks, first-party data monetization through governed access, and a shift toward zero-to-zero marketing driven by agent-to-agent transactions. He closes by stressing that strong data foundations determine who can compete as marketing becomes more automated and agent-driven.About AnthonyAnthony Rotio is the Chief Data Strategy Officer at GrowthLoop, where he leads partnerships and builds generative AI product features for marketers, including multi-agent systems, AI-driven audience building, and benchmarking and evaluation work. He previously served as GrowthLoop’s Chief Customer Officer, where he built and led teams across data engineering, data science, and solutions architecture while supporting product development and strategic sales efforts.Before GrowthLoop, Anthony spent nearly six years at AB InBev, where he led a $100M owned retail business unit with full P&L responsibility and drove major growth through operational and digital transformation work. He also led U.S. marketing for Budweiser, Bud Light, Michelob Ultra, Stella Artois, and other brands across music, food, and related consumer programs. He earned a B.A. in computer science from Harvard, played linebacker on the Harvard football team, founded the consumer product Pizza Shelf, and holds a Google Professional Cloud Architect certification.Journeying From Robotics to Modern Marketing SystemsAnthony’s career started far away from marketing. He trained as a computer scientist and spent his early years working with robotics and reinforcement learning. His first exposure to a learning agent left a lasting impression because the system behaved less like traditional software and more like something adaptive. That experience shaped how he would later think about work, systems, and feedback. He learned early that progress comes from loops that learn, not static instructions.That mindset followed him into an unexpected chapter at AB InBev. Anthony entered a world defined by scale, brands, and operational complexity. He treated his technical background like a carpenter treats tools, useful only when applied to real problems. Running marketing across major beer brands taught him how value is created inside large organizations. It also exposed a recurring issue. Marketing teams had ambition and data, but execution moved slowly because ideas had to travel through layers of translation before anything reached customers.That friction became impossible to ignore. Audience definitions moved through tickets. Campaigns waited on queries. Data teams became bottlenecks through no fault of their own. Anthony felt the pull back toward technology, where systems could shorten the distance between intent and action. That pull led him to GrowthLoop, where he joined early and worked directly with customers. The appeal was immediate. The product connected straight to cloud data and removed several layers of mediation that marketing teams had accepted as normal.As language models improved, Anthony recognized a familiar pattern. Audience building behaved like a translation problem. Marketers described people and intent in natural language, while systems demanded structured logic. Early experiments showed that natural language models could close that gap. Anthony framed the idea clearly.“Audience building is a translation problem. You start with a business idea and you end with a query on top of data.”Momentum followed quickly. Customers like Indeed and Google responded because speed changed behavior. Teams experimented more often and refined audiences based on results instead of assumptions. Conversations with Sam Altman and collaboration with OpenAI reinforced that this capab

"Hey – So what do you do?” Why is it that we always default to work when we get this question. its like many of us have let our jobs become the center of how we see ourselves. This slowly happens to many of us, as work occupies more mental and emotional space.I asked 50 people in martech and operations how they stay happy under sustained pressure. This 3 part series – titled “50 Operators share the systems that keep them happy” explores each of these layers through the lived experience of operators who feel the same pressure you probably feel right now.Today we close out the series with part 3: meaning. We’ll hear from 19 people and we’ll cover:(00:00) - Teaser (01:08) - Intro / In This Episode (04:27) - Rich Waldron: Auditing Whether Work Is Actually Moving (06:49) - Samia Syed: Tracking Personal Growth (08:33) - Jonathan Kazarian: Tracking Growth Across Life Health and Work (10:11) - Kim Hacker: Choosing Roles With Daily Visible Impact (12:21) - Mac Reddin: Checking Work Against 3 Personal Conditions (14:11) - Chris Golec: Choosing Early Stage Building Work (15:19) - Hope Barrett: Feeding curiosity across multiple domains (17:45) - Simon Lejeune: Treating work like a game (19:52) - Ana Mourão: A mental buffer between noticing and doing (21:46) - Tiankai Feng: Anticipation planning (25:30) - István Mészáros: Choosing Who You Are When Work Ends (29:52) - Danielle Balestra: Feeding Interests Unrelated to Work (31:42) - Jeff Lee: Continuing to Build Personal Projects After the Workday Ends (33:23) - John Saunders: Keeping a builder practice outside of work (34:41) - Ashley Faus: Group Creative Rituals Outside of work (37:40) - Anna Aubucho: Maintaining a second self through solo creative practice (39:56) - Ruari Baker: Preserving Identity Through Regular Travel (42:15) - Guta Tolmasquim: Building a personal product roadmap (45:37) - Pam Boiros: Feeding identities that have nothing to do with work (47:52) - Outro All that and a bunch more stuff after a quick word from 2 of our awesome partners.A lot of the operators I chatted with don’t talk about happiness like it suddenly arrives. They describe it as something you feel when things actually start to move. Our first guest gets there right away by tying happiness directly to progress, the kind that tells you you’re not stuck.Rich Waldron: Auditing Whether Work Is Actually MovingFirst up is Rich Waldron, Co-founder and CEO at Tray.ai. He’s also a dad, and a mediocre golfer.Progress sits at the center of Rich’s definition of career happiness. He treats it as a felt sense rather than a dashboard metric. When work advances in a direction that makes sense to him, his energy steadies. When that movement slows or stalls, frustration surfaces quickly and spreads into everything else. That feeling becomes a cue to examine direction rather than effort.“Happiness is mostly driven by progress.”That framing resonates because it names something many operators struggle to articulate. Long hours can feel sustainable when the work moves forward. Light workloads can feel draining when days repeat without traction. Progress gives work narrative weight. It answers a quiet internal question about whether today connects to something that matters tomorrow.Rich also points to patterns that erode meaning over time.Roles with little challenge dull attention, even when the pay is generous.Constant activity without visible change breeds irritation that lingers after work ends.Both conditions interrupt momentum. The mind keeps searching for movement that never arrives. Rest stops working because unresolved motion occupies every quiet moment.Progress also shapes identity beyond work. When things move in the right direction, attention releases its grip on unfinished problems. Rich links that release to showing up better at home. He describes being more present as a parent because mental energy is no longer trapped in work that feels stuck. Forward motion restores proportion. Work keeps its place as one part of a full life rather than the dominant one.Balance emerges as a byproduct of this orientation. You choose problems that move. You notice when progress fades. You adjust before frustration hardens into burnout. That rhythm preserves meaning over long career arcs and keeps work aligned with the person you want to remain.Key takeaway: Track progress as a signal of meaning. When your work moves in a direction you respect, it stays contained, your identity stays intact, and the rest of your life receives the attention it deserves.Samia Syed: Tracking Personal GrowthThat’s Samia Syed, Director of Growth Marketing at Dropbox. She’s also a mother, outdoor fanatic, and an avid hiker.Progress became the scorecard Samia relies on to keep her career from consuming her sense of self. Early professional years trained her to chase perfection, because perfection looked measurable, respectable, and safe. That mindset quietly tightened the frame around what counted as a good day. Effort increased, expecta

Pressure at work rarely stays contained within the job. It spills into family life, friendships, and daily relationships. I asked 50 operators how they stay happy while managing responsibility at work and at home. This 3 part series – titled “50 Operators share the systems that keep them happy” explores each of these layers through the lived experience of operators who feel the same pressure you probably feel right now. Today we continue with part 2: connection, the relationships that recharge you and keep you standing when the work would otherwise knock you sideways.We’ll hear from 17 people and we’ll cover:(00:00) - Teaser (02:00) - In This Episode (04:30) - Eric Holland: Limiting Slack and Prioritizing Family Time (05:33) - Meg Gowell: Shared Family Routines (08:31) - David Joosten: Filtering Reactive Work So Time Stays With Family (10:30) - Aboli Gangreddiwar: Designing Work to Enable Family Travel (12:01) - Kevin White: Separating Career Drive From Family Identity (13:42) - Joshua Kanter: Daily Family Rituals (18:07) - Gab Bujold: Daily Check-Ins With a Trusted Work Partner (22:30) - Anna Leary: Treating Workload Stress as a Shared Problem (24:31) - Angela Rueda: Shared Problem Solving Conversations (26:50) - Blair Bendel: Using In Person Conversations to Stay Grounded (29:28) - Matthew Castino: Work Satisfaction Correlates Strongly With Team Relationships (33:17) - Aditi Uppal: Connection as a Feedback Loop (35:48) - Alison Albeck Lindland: One Social System Across Work and Life (37:34) - Rajeev Nair: Human Bonds Absorb Pressure Before Burnout (40:12) - Chris O’Neil: Filtering Work Through People and Problems That Matter (42:24) - Rebecca Corliss: Creativity as a Shared Emotional Outlet (44:24) - Moni Oloyede: Teaching as a Living Relationship (45:50) - Outro Connection starts with who you protect time for. Our first guest begins there, shaping his work around people who refill him and drawing hard lines around anything that steals those moments away.Eric Holland: Limiting Slack and Prioritizing Family TimeFirst up is Eric Holland, a fractional PMM based in Pennsylvania, and the co-host of the We’re not Marketers Podcast. He’s also a dad and runs a retail apparel startup. Eric shapes his happiness around people before tasks. He pares his work down to projects shared with colleagues he enjoys being around, and that choice changes the texture of his days. Conversations feel easier. Meetings end with momentum instead of fatigue. You can hear a quiet confidence in how he describes work that feels relational rather than transactional.Family anchors that perspective in a very physical way. Nearly every weekend, from late November through Christmas, belongs to his ten-month-old son. These are not abstract intentions. They are mornings that smell like coffee and pine needles, afternoons on cold sidewalks, and evenings defined by routine rather than inboxes. Time with his son creates emotional weight that carries into the workweek and keeps priorities visible when deadlines start to blur.Eric also draws a firm boundary around digital proximity. Slack does not live on his phone, and that decision protects the moments where connection needs full attention. The habit most people recognize, checking messages during dinner or while holding a child, never has a chance to form. Presence becomes simpler when tools stay in their place.The system he describes comes together through a few concrete moves that many people quietly avoid:He limits work to collaborators who feel generous with energy.He reserves weekends for repeated family rituals that mark time.He removes communication tools from personal spaces where they dilute focus.Eric captures the point with a line that carries practical weight.“Delete Slack off your phone.”That sentence signals care for the relationships that actually hold you upright. Attention stays where your body is, and connection grows from that consistency.Key takeaway: Strong connections protect long-term happiness at work. Choose collaborators who give energy, protect repeated time with family and friends, and keep work tools out of moments that deserve your full presence.Meg Gowell: Shared Family RoutinesNext up is Meg Gowell, Head of Marketing at Elly and former Director of Growth Marketing at Typeform and Appcues. She’s also a mom of 3.Remote work compresses everything into the same physical space. Meetings happen steps away from the kitchen. Notifications follow you into the evening. Meg treats that compression as something that requires active design. She and her husband both work remotely, so separation never happens by accident. It happens because they decide when work stops and family time starts, and they repeat that decision every day.That discipline shows up in how she leads at Typeform. An international team creates constant overlap and constant absence at the same time. Someone is always offline. Someone is always mid-day. Ideas surface at inconvenient hours. Meg sends messages w

Careers place a ton of demand on energy and attention way before results start to stabilize. Many operators discover that health and routine determine how long they can operate at a high level.I spoke with 50 people working in martech and operations about how they stay happy under pressure. This 3 part series – titled “50 Operators share the systems that keep them happy” explores each of these layers through the lived experience of operators who feel the same pressure you probably feel right now.Today we start with part 1: stability through routines, boundaries, and systems that protect the body and mind. We’ll hear from 15 people:(00:00) - Teaser (01:05) - Intro (01:30) - In This Episode (04:09) - Austin Hay: Building Non Negotiables (08:06) - Sundar Swaminathan: Systems That Prevent Stress (12:33) - Elena Hassan: Normalizing Stress (14:32) - Sandy Mangat: Managing Energy (16:31) - Constantine Yurevich: Designing Work That Matches Personal Energy (19:05) - Keith Jones: Intentional Work Rhythms (23:58) - Olga Andrienko: Daily Health Routines (26:06) - Sarah Krasnik Bedell: Outdoor Routines (27:21) - Zach Roberts: Physical Reset Rituals Outside Work (28:57) - Jane Menyo: Recovery Cycles (31:56) - Angela Vega: Chosen Challenges and Recovery Cycles (36:09) - Megan Kwon: Presence Built Into the Day (37:50) - Nadia Davis: Calendar Discipline (39:36) - Henk-jan ter Brugge: Planning the Week as a Constraint System (43:15) - Ankur Kothari: Personal Metrics (44:07) - Outro Austin Hay: Building Non NegotiablesOur first guest is Austin Hay, he’s a co-founder, a teacher, a martech advisor, but he’s also a husband, a dog dad, a student, water skiing fanatic, avid runner, a certified financial planner, and a bunch more stuff... Daily infrastructure shows up through repetition, discipline, and choices that protect energy before anything else competes for it. Austin grounds happiness in curiosity, but that curiosity only thrives when supported by sleep, movement, and time that belongs to no employer. Learning stays fun because it is not treated as another performance metric. It remains part of who he is rather than something squeezed into the margins of an already crowded day.Mental and physical health shape his schedule in visible ways. Austin treats them as operating requirements rather than aspirations. His days include a short list of behaviors that carry disproportionate impact:Regular sleep with a consistent bedtime.Exercise that creates physical fatigue and mental quiet.Relationships that exist entirely outside work.Hobbies and games that feel restorative rather than productive.These habits rarely earn praise, which explains why they erode first under pressure. In his twenties, Austin chased work, clients, and money with intensity. He told himself the rest would come later. That promise held eventually, but the gap years carried a cost. He remembers moments of looking in the mirror and feeling uneasy about the life he was assembling, despite checking every external box.Trade-offs now anchor his thinking. Austin frames decisions as equations involving time, energy, and outcomes. Goals demand inputs, and inputs consume limited resources. Avoiding that math leads to exhaustion and resentment. Facing it creates clarity. Many people resist this step because it forces hard choices into daylight. The industry rewards the appearance of doing everything, even when the math never works.“I view a lot of decisions and outcomes in life as trade-offs. At the end of the day, that’s what most things boil down to.”Sleep makes the equation tangible. Austin aims for bed around 9 or 9:30 each night because his mornings require focus, training, and sustained energy. He needs seven and a half hours of sleep to function well. That requirement dictates the rest of the day. Social plans adjust. Work compresses. Goals remain achievable because the system supports them.He defines what he wants to pursue.He calculates the energy required.He locks in non negotiables that keep the math honest.That structure removes constant negotiation with himself. The system holds even when motivation dips or distractions multiply.Key takeaway: Daily infrastructure depends on non negotiables that protect sleep, health, and energy. Clear priorities, visible trade-offs, and repeatable routines create careers that stay durable under pressure.Sundar Swaminathan: Systems That Prevent StressNext up is Sundar Swaminathan, Former Head of Marketing Science at Uber, Author & Host of the experiMENTAL Newsletter & Podcast. He’s also a husband, a father and a well traveled home chef, amateur chess master.Stress prevention sits at the center of Sundar’s daily system for staying happy and effective at work. A concentrated period of personal loss collapsed any illusion that stress deserved patience or tolerance. Three deaths in three weeks compressed time, sharpened perspective, and forced a reassessment of what stress actually costs. Stress drains energy first, then

What’s up everyone, today we have the pleasure of sitting down with Phyllis Fang, Head of Marketing at Transcend.(00:00) - Intro (01:23) - In This Episode (04:13) - Uber Safety Marketing Shaped A Trust First Marketing Playbook (10:12) - How Permissioned Data Systems Power Personalization at Scale (15:22) - How Consent Infrastructure Improves Personalization Performance (19:20) - How to Audit Consent and Compliance in Marketing Data (23:24) - What Consent Management Does Across AI Data Lifecycles (28:29) - How to Build a Marketing Trust Stack (30:49) - Consent Management as a Revenue Lever (35:10) - Designing Marketing Teams for Freakish Curiosity (41:19) - Skills That Define Great Marketing Operations (45:33) - Why System Level Marketing Experience Builds Career Leverage (50:13) - System for Happiness Summary: Phyllis learned how fragile marketing becomes when systems move faster than trust while working between lifecycle execution and product marketing at Uber. Safety work around emergencies, verification, and COVID forced messages to withstand scrutiny from riders, drivers, regulators, and the public. That experience shapes how she approaches consent and personalization today. Permission signals decide what data moves and how confidently teams can act. When those signals stay connected, work holds. When they drift, confidence erodes across systems, teams, and careers.About PhyllisPhyllis Fang leads marketing at Transcend, where enterprise growth depends on clear choices about data, consent, and accountability. Her work shapes how privacy becomes part of how companies operate, communicate, and earn confidence at scale.Earlier in her career, she spent several years at Uber, working on global product marketing for safety during periods of intense public scrutiny. She helped bring new safety features to market at moments when user behavior, policy decisions, and brand credibility were tightly linked. The work required precision, restraint, and an understanding of how people respond when stakes feel personal.Across roles in e-commerce, lifecycle marketing, and platform strategy, a pattern holds. Fang gravitates toward systems that must work under pressure and messages that must hold up in practice. Her career reflects a belief that marketing earns its place when it reduces uncertainty and helps people move forward with confidence.Uber Safety Marketing Shaped A Trust First Marketing PlaybookTrust-focused marketing depends on people who can move between systems work and narrative work without losing credibility in either space. Phyllis built that fluency by operating inside lifecycle programs while also leading product marketing initiatives at Uber. One side of that work lived in tools, triggers, and delivery logic. The other side lived in rooms where progress depended on persuasion, alignment, and patience. That dual exposure trained her to see how fragile big ideas become when they cannot survive real execution.Lifecycle and marketing operations reward control and repeatability. Product marketing inside a global organization rewards influence and restraint. Phyllis describes moments where moving a single initiative forward required negotiation across regions, channels, and internal politics. Every message faced review from people who owned distribution and reputation in their markets. Messaging tightened quickly because weak logic did not survive long. Campaigns became sharper because every assumption had to hold up under pressure.“We were all in the same company, but I still had to convince people to resource things differently or prioritize a message.”Safety marketing pushed that pressure even further. The work focused on features designed for rare, high-stakes moments, including emergency assistance and large-scale verification during COVID. Measurement shifted away from habitual usage and toward confidence and credibility. The audience expanded well beyond active users. Phyllis had to speak clearly to riders, drivers, regulators, and the general public at the same time. Each group carried different fears, incentives, and consequences. Messaging succeeded only when it respected those differences without creating confusion.That mindset carries directly into her work at Transcend. Privacy and consent buyers often sit in legal or compliance roles where personal and professional risk overlap. These buyers read closely and remember details. Phyllis explains that proof needs to operate on two levels at once. It must withstand careful review, and it must connect to human motivation. Career safety, internal credibility, and long-term reputation shape decisions more than feature depth ever will.“You have to understand the human behind the role, because their motivation usually has very little to do with your product.”Many martech teams still lean on urgency and fear to move deals forward. That habit collapses quickly in trust-driven categories. Buyers trained to manage risk respond to clarity, evidence, and empathy.

What’s up everyone, today we have the pleasure of sitting down with Jordan Resnick, Senior Director, Marketing Operations at CHEQ.(00:00) - Intro (01:10) - In This Episode (03:47) - Demystifying Go-to-Market Security (06:14) - The Fake Traffic Surge (08:14) - How the Dead Internet Theory Connects to Bot Traffic Growth (12:31) - How to Detect Bot Traffic Through Behavioral Patterns (16:13) - How Go To Market Teams Reduce Fake Traffic And Lead Pollution (30:03) - Preventing Fake Leads From Reaching Sales (34:17) - How to Calculate Revenue Impact of Fake Traffic (38:09) - How to Report Marketing Performance When Bot Traffic Skews Metrics (43:58) - Trust Erosion From Fake Traffic (49:49) - How Marketing Ops Should Adapt Systems for Machine Customers (53:59) - Funnel Audits With Security Teams to Reduce Bot Traffic (57:47) - Detachment as a Career Survival Skill Summary: Distinguishing fake traffic from real machine customers starts where metrics break down. Jordan shows how AI-driven bots now scroll, click, submit forms, and pass validation while quietly filling dashboards with activity that never turns into revenue. The tell is behavioral texture. Sessions move too fast. Paths skip learning. Engagement appears without intent. Real machine customers behave with rhythm and purpose, returning, evaluating, integrating. Teams that recognize the difference lock down the conversion point, block synthetic demand before it reaches core systems, keep sales calendars clean, and only report once traffic has earned trust.About JordanJordan Resnick is Senior Director of Marketing Operations at CHEQ, where he leads the systems, data, and workflows that support go-to-market security across a global customer base. His work sits at the intersection of marketing operations, revenue operations, attribution, automation, and analytics, with a clear focus on building infrastructure that holds up under scale and scrutiny.Before CHEQ, Jordan led marketing operations at Atlassian, where he supported complex GTM motions across multiple business units and global markets. Earlier roles at Perkuto and MERGE combined hands-on execution with customer-facing leadership, integration design, and process ownership. His career also includes more than a decade as an independent operator, delivering marketing operations, automation, content, and technical solutions across a wide range of organizations. Jordan brings a deeply practical, execution-driven perspective shaped by years of building, fixing, and maintaining real systems in production environments.Demystifying Go-to-Market SecurityGo-to-market security shows up when growth metrics look strong and revenue outcomes feel weak. Marketing operations teams live in that gap every day. Jordan describes GTM security as a business-facing discipline that protects the integrity of acquisition, funnel data, and downstream decisions that depend on clean signals. The work sits inside marketing operations because that is where traffic quality, lead flow, and revenue attribution converge.When asked about how GTM security differs from traditional fraud prevention, Jordan frames the difference through decision-making pressure. Security teams historically focus on defending infrastructure and minimizing exposure. Marketing ops teams focus on maintaining momentum while spending real budget. GTM security evaluates risk in context, with an eye toward revenue impact, forecasting accuracy, and operational trust across teams that rely on shared data.Jordan grounds the concept in specific failure points that operators recognize immediately. GTM security examines where bad inputs quietly enter systems and multiply.Paid traffic that inflates sessions without creating buyers.Analytics skewed by automated interactions that look legitimate.Form submissions that pass validation and still never convert.Sales pipelines filled with activity that drains time and morale.Each issue compounds because systems assume the data is real. Teams keep optimizing against numbers that feel precise and still point in the wrong direction.“You are putting money into driving people to your website, and the first question should be how many of those people are real and able to buy.”Invalid traffic behaves like a contaminant. It flows from acquisition into attribution models, forecasting tools, CRMs, and revenue dashboards. Marketing celebrates growth, sales chases shadows, and finance questions confidence in the entire funnel. The problem rarely announces itself as a security incident. It surfaces as confusion, missed targets, and internal friction.GTM security matters because it gives marketing ops teams a framework to protect the inputs that shape every downstream decision. The work runs alongside traditional security while staying anchored in go-to-market outcomes. That way you can spend with confidence, trust your reporting, and hand sales teams signals grounded in real buying behavior.Key takeaway: Treat go-to-market security as part of yo

What’s up everyone, today we have the honor of sitting down with Aleyda Solís, SEO and AI search consultant. (00:00) - Intro (01:17) - In This Episode (04:55) - Crawlability Requirements for AI Search Engines (12:21) - LLMs As A New Search Channel In A Multi Platform Discovery System (18:42) - AI Search Visibility Analysis for SEO Teams (29:17) - Creating Brand Led Informational Content for AI Search (35:51) - Choosing SEO Topics That Drive Brand-Aligned Demand (45:50) - How Topic Level Analysis Shapes AI Search Strategy (50:01) - LLM Search Console Reporting Expectations (52:09) - Why LLM Search Rewards Brands With Real Community Signals (55:12) - Prioritizing Work That Matches Personal Purpose Summary: AI search is rewriting how people find information, and Aleyda explains the shift with clear, practical detail. She has seen AI crawlers blocked without anyone noticing, JavaScript hiding full sections of sites, and brands interpreting results that were never based on complete data. She shows how users now move freely between Google, TikTok, Instagram, and LLMs, which pushes teams to treat discovery as a multi-platform system. She encourages you to verify your AI visibility, publish content rooted in real customer language, and use topic clusters to anchor strategy when prompts scatter. Her closing point is simple. Community chatter now shapes authority, and AI models pay close attention to it.About AleydaAleyda Solís is an international SEO and AI search optimization consultant, speaker, and author who leads Orainti, the boutique consultancy known for solving complex, multi-market SEO challenges. She’s worked with brands across ecommerce, SaaS, and global marketplaces, helping teams rebuild search foundations and scale sustainable organic growth.She also runs three of the industry’s most trusted newsletters; SEOFOMO, MarketingFOMO, and AI Marketers, where she filters the noise into the updates that genuinely matter. Her free roadmaps, LearningSEO.io and LearningAIsearch.com, give marketers a clear, reliable path to building real skills in both SEO and AI search.Crawlability Requirements for AI Search EnginesCrawlability shapes everything that follows in AI search. Aleyda talks about it with the tone of someone who has seen far too many sites fail the basics. AI crawlers behave differently from traditional search engines, and they hit roadblocks that most teams never think about. Hosting rules, CDN settings, and robots files often permit Googlebot but quietly block newer user agents. You can hear the frustration in her voice when she describes audit after audit where AI crawlers never reach critical sections of a site."You need to allow AI crawlers to access your content. The rules you set might need to be different depending on your context."AI crawlers also refuse to process JavaScript. They ingest raw markup and move on. Sites that lean heavily on client-side rendering lose entire menus, product details, pricing tables, and conversion paths. Aleyda describes this as a structural issue that forces marketers to confront their technical debt. Many teams have spent years building front-ends with layers of JavaScript because Google eventually figured out how to handle it. AI crawlers skip that entire pipeline. Simpler pages load faster, reveal hierarchy immediately, and give AI models a complete picture without extra processing.Search behavior adds new pressure. Aleyda points to OpenAI’s published research showing a rise in task-oriented queries. Users ask models to complete goals directly and skip the page-by-page exploration we grew up optimizing for. You need clarity about which tasks intersect with your offerings. You need to build content that satisfies those tasks without guessing blindly. Aleyda urges teams to validate this with real user understanding because generic keyword tools cannot describe these new behaviors accurately.Authority signals shift too. Mentions across credible communities carry weight inside AI summaries. Aleyda explains it as a natural extension of digital PR. Forums, newsletters, podcasts, social communities, and industry roundups form a reputation map that AI crawlers use as context. Backlinks still matter, but mentions create presence in a wider set of conversations. Strong SEO programs already invest in this work, but many teams still chase link volume while ignoring the broader network of references that shape brand perception.Measurement evolves alongside all of this. Aleyda encourages operators to treat AI search as both a performance channel and a visibility channel. You track presence inside responses. You track sentiment and frequency. You monitor competitors that appear beside you or ahead of you. You map how often your brand appears in summaries that influence purchase decisions. Rankings and click curves do not capture the full picture. A broader measurement model captures what these new systems actually distribute.Key takeaway: Build crawlability for AI search with i

What’s up everyone, today we have the honor of sitting down with the legendary Scott Brinker, a rare repeat guest, the Martech Landscape creator, the Author of Hacking Marketing, The Godfather of Martech himself.(00:00) - Intro (01:12) - In This Episode (05:09) - Scott Brinker’s Guidance For Marketers Rethinking Their Career Path (11:27) - If You Started Over in Martech, What Would You Learn First (16:47) - People Side (21:13) - Life Long Learning (26:20) - Habits to Stay Ahead (32:14) - Why Deep Specialization Protects Marketers From AI Confusion (37:27) - Why Technical Skills Decide the Future of Your Marketing Career (41:00) - Why Change Leadership Matters More Than Technical AI Skills (47:11) - How MCP Gives Marketers a Path Out of Integration Hell (52:49) - Why Heterogeneous Stacks are the Default for Modern Marketing Teams (54:51) - How To Build A Martech Messaging BS Detector (59:37) - Why Your Energy Grows Faster When You Invest in Other People Summary: Scott Brinker shares exactly where he would focus if he reset his career today, and his answer cuts through the noise. He’d build one deep specialty to judge AI’s confident mistakes, grow cross-functional range to bridge marketing and engineering, and lean into technical skills like SQL and APIs to turn ideas into working systems. He’d treat curiosity as a steady rhythm instead of a rigid routine, learn how influence actually moves inside companies, and guide teams through change with simple, human clarity. His take on composability, MCP, and vendor noise rounds out a clear roadmap for any marketer trying to stay sharp in a chaotic industry.About ScottScott has spent his career merging the world of marketing and technology and somehow making it look effortless. He co-founded ion interactive back when “interactive content” felt like a daring experiment, then opened the Chief Marketing Technologist blog in 2008 to spark a conversation the industry didn’t know it needed. He sketched the very first Martech Landscape when the ecosystem fit on a single page with about 150 vendors, and later brought the MarTech conference to life in 2014, where he still shapes the program. Most recently, he guided HubSpot’s platform ecosystem, helping the company stay connected to a martech universe that’s grown to more than 15,000 tools. Today, Scott continues to helm chiefmartec.com, the well the entire industry keeps returning to for clarity, curiosity, and direction.Scott Brinker’s Guidance For Marketers Rethinking Their Career PathMid career marketers keep asking themselves whether they should stick with the field or throw everything out and start fresh. Scott relates to that feeling, and he talks about it with a kind of grounded humor. He describes his own wandering thoughts about running a vineyard, feeling the soil under his shoes and imagining the quiet. Then he remembers the old saying about wineries, which is that the only guaranteed outcome is a smaller bank account. His story captures the emotional drift that comes with burnout. People are not always craving a new field. They are often craving a new relationship with their work.Scott moves quickly to the part that matters. He directs his attention to AI because it is reshaping the field faster than many teams can absorb. He explains that someone could spend every hour of the week experimenting and still only catch a fraction of what is happening. He sees that chaos as a signal. Overload creates opportunity, and the people who step toward it gain an advantage. He urges mid career operators to lean into the friction and build new muscle. He even calls out how many people will resist change and cling to familiar workflows. He views that resistance as a gift for the ones willing to explore.“People who lean into the change really have the opportunity to differentiate themselves and discover things.”Scott brings back a story from a napkin sketch. He drew two curves, one for the explosive pace of technological advancement and one for the slower rhythm of organizational change. The curves explain the tension everyone feels. Teams operate on slower timelines. Tools operate on faster ones. The gap between those curves is wide, and professionals who learn to navigate that space turn themselves into catalysts inside their companies. He sees mid career marketers as prime candidates for this role because they have enough lived experience to understand where teams stall and enough hunger to explore new territory.Scott encourages people to channel their curiosity into specific work. He suggests treating AI exploration like a practice and not like a trend. A steady rhythm of experiments helps someone grow their internal influence. Better experiments produce useful artifacts. These artifacts often become internal proof points that accelerate change. He believes the next wave of opportunity belongs to people who document what they try, translate what they learn, and help their companies adapt at a pace that competitors cannot eas

What’s up everyone, today we have the pleasure of sitting down with Matthew Castino, Marketing Measurement Science Lead @ Canva.(00:00) - Intro (01:10) - In This Episode (03:50) - Canva’s Prioritization System for Marketing Experiments (11:26) - What Happened When Canva Turned Off Branded Search (18:48) - Structuring Global Measurement Teams for Local Decision Making (24:32) - How Canva Integrates Marketing Measurement Into Company Forecasting (31:58) - Using MMM Scenario Tools To Align Finance And Marketing (37:05) - Why Multi Touch Attribution Still Matters at Canva (42:42) - How Canva Builds Feedback Loops Between MMM and Experiments (46:44) - Canva’s AI Workflow Automation for Geo Experiments (51:31) - Why Strong Coworker Relationships Improve Career Satisfaction Summary: Canva operates at a scale where every marketing decision carries huge weight, and Matt leads the measurement function that keeps those decisions grounded in science. He leans on experiments to challenge assumptions that models inflate. As the company grew, he reshaped measurement so centralized models stayed steady while embedded data scientists guided decisions locally, and he built one forecasting engine that finance and marketing can trust together. He keeps multi touch attribution in play because user behavior exposes patterns MMM misses, and he treats disagreements between methods as signals worth examining. AI removes the bottlenecks around geo tests, data questions, and creative tagging, giving his team space to focus on evidence instead of logistics. About MatthewMatthew Castino blends psychology, statistics, and marketing intuition in a way that feels almost unfair. With a PhD in Psychology and a career spent building measurement systems that actually work, he’s now the Marketing Measurement Science Lead at Canva, where he turns sprawling datasets and ambitious growth questions into evidence that teams can trust.His path winds through academia, health research, and the high-tempo world of sports trading. At UNSW, Matt taught psychology and statistics while contributing to research at CHETRE. At Tabcorp, he moved through roles in customer profiling, risk systems, and US/domestic sports trading; spaces where every model, every assumption, and every decision meets real consequences fast. Those years sharpened his sense for what signal looks like in a messy environment.Matt lives in Australia and remains endlessly curious about how people think, how markets behave, and why measurement keeps getting harder, and more fun.Canva’s Prioritization System for Marketing ExperimentsCanva’s marketing experiments run in conditions that rarely resemble the clean, product controlled environment that most tech companies love to romanticize. Matthew works in markets filled with messy signals, country level quirks, channel specific behaviors, and creative that behaves differently depending on the audience. Canva built a world class experimentation platform for product, but none of that machinery helps when teams need to run geo tests or channel experiments across markets that function on completely different rhythms. Marketing had to build its own tooling, and Matthew treats that reality with a mix of respect and practicality.His team relies on a prioritization system grounded in two concrete variables.SpendUncertaintyLarge budgets demand measurement rigor because wasted dollars compound across millions of impressions. Matthew cares about placing the most reliable experiments behind the markets and channels with the biggest financial commitments. He pairs that with a very sober evaluation of uncertainty. His team pulls signals from MMM models, platform lift tests, creative engagement, and confidence intervals. They pay special attention to MMM intervals that expand beyond comfortable ranges, especially when historical spend has not varied enough for the model to learn. He reads weak creative engagement as a warning sign because poor engagement usually drags efficiency down even before the attribution questions show up.“We try to figure out where the most money is spent in the most uncertain way.”The next challenge sits in the structure of the team. Matthew ran experimentation globally from a centralized group for years, and that model made sense when the company footprint was narrower. Canva now operates in regions where creative norms differ sharply, and local teams want more authority to respond to market dynamics in real time. Matthew sees that centralization slows everything once the company reaches global scale. He pushes for embedded data scientists who sit inside each region, work directly with marketers, and build market specific experimentation roadmaps that reflect local context. That way experimentation becomes a partner to strategy instead of a bottleneck.Matthew avoids building a tower of approvals because heavy process often suffocates marketing momentum. He prefers a model where teams follow shared principles, run experiments respo

What’s up everyone, today we have the pleasure of sitting down with Anna Aubuchon, VP of Operations at Civic Technologies.(00:00) - Intro (01:15) - In This Episode (04:15) - How AI Flipped the Build Versus Buy Decision (07:13) - Redrawing What “Complex” Means (12:20) - Why In House AI Provides Better Economics And Control (15:33) - How to Treat AI as an Insourcing Engine (21:02) - Moving BI Workloads Out of Dashboards and Into LLMs (31:37) - Guardrails That Keep AI Querying Accurate (38:18) - Using Role Based AI Guardrails Across MCP Servers (44:43) - Ops People are Creators of Systems Rather Than Maintainers of Them (48:12) - Why Natural Language AI Lowers the Barrier for First-Time Builders (52:31) - Technical Literacy Requirements for Next Generation Operators (56:46) - Why Creative Practice Strengthens Operational Leadership Summary: AI has reshaped how operators work, and Anna lays out that shift with the clarity of someone who has rebuilt real systems under pressure. She breaks down how old build versus buy habits hold teams back, how yearly AI contracts quietly drain momentum, and how modern integrations let operators assemble powerful workflows without engineering bottlenecks. She contrasts scattered one-off AI tools with the speed that comes from shared patterns that spread across teams. Her biggest story lands hard. Civic replaced slow dashboards and long queues with orchestration that pulls every system into one conversational layer, letting people get answers in minutes instead of mornings. That speed created nerves around sensitive identity data, but tight guardrails kept the team safe without slowing anything down. Anna ends by pushing operators to think like system designers, not tool babysitters, and to build with the same clarity her daughter uses when she describes exactly what she wants and watches the system take shape.About AnnaAnna Aubuchon is an operations executive with 15+ years building and scaling teams across fintech, blockchain, and AI. As VP of Operations at Civic Technologies, she oversees support, sales, business operations, product operations, and analytics, anchoring the company’s growth and performance systems.She has led blockchain operations since 2014 and built cross-functional programs that moved companies from early-stage complexity into stable, scalable execution. Her earlier roles at Gyft and Thomson Reuters focused on commercial operations, enterprise migrations, and global team leadership, supporting revenue retention and major process modernization efforts.How AI Flipped the Build Versus Buy DecisionAI tooling has shifted so quickly that many teams are still making decisions with a playbook written for a different era. Anna explains that the build versus buy framework people lean on carries assumptions that no longer match the tool landscape. She sees operators buying AI products out of habit, even when internal builds have become faster, cheaper, and easier to maintain. She connects that hesitation to outdated mental models rather than actual technical blockers.AI platforms keep rolling out features that shrink the amount of engineering needed to assemble sophisticated workflows. Anna names the layers that changed this dynamic. System integrations through MCP act as glue for data movement. Tools like n8n and Lindy give ops teams workflow automation without needing to file tickets. Then ChatGPT Agents and Cloud Skills launched with prebuilt capabilities that behave like Lego pieces for internal systems. Direct LLM access removed the fear around infrastructure that used to intimidate nontechnical teams. She describes the overall effect as a compression of technical overhead that once justified buying expensive tools.She uses Civic’s analytics stack to illustrate how she thinks about the decision. Analytics drives the company’s ability to answer questions quickly, and modern integrations kept the build path light. Her team built the system because it reinforced a core competency. She compares that with an AI support bot that would need to handle very different audiences with changing expectations across multiple channels. She describes that work as high domain complexity that demands constant tuning, and the build cost would outweigh the value. Her team bought that piece. She grounds everything in two filters that guide her decisions: core competency and domain complexity.Anna also calls out a cultural pattern that slows AI adoption. Teams buy AI tools individually and create isolated pockets of automation. She wants teams to treat AI workflows as shared assets. She sees momentum building when one group experiments with a workflow and others borrow, extend, or remix it. She believes this turns AI adoption into a group habit rather than scattered personal experiments. She highlights the value of shared patterns because they create a repeatable way for teams to test ideas without rebuilding from scratch.She closes by urging operators to update their decision cycle. T

What’s up everyone, today we have the pleasure of sitting down with Pam Boiros, Fractional CMO and Marketing advisor, and Co-Founder Women Applying AI.(00:00) - Intro (01:13) - In This Episode (03:49) - How To Audit Data Fingerprints For AI Bias In Marketing (07:39) - Why Emotional Intelligence Improves AI Prompting Quality (10:14) - Why So Many Women Hesitate (15:40) - Why Collaborative AI Practice Builds Confidence In Marketing Ops Teams (18:31) - How to Go From AI Curious to AI Confident (24:32) - Joining The 'Women Applying AI' Community (27:18) - Other Ways to Support Women in AI (28:06) - Role Models and Visibility (32:55) - Leadership’s Role in Inclusion (35:57) - Mentorship for the AI Era (38:15) - Why Story Driven Communities Strengthen AI Adoption for Women (42:17) - AI’s Role in Women’s Worklife Harmony (45:22) - Why Personal History Strengthens Creative Leadership Summary: Pam delivers a clear, grounded look at how women learn and lead with AI, moving from biased datasets to late-night practice sessions inside Women Applying AI. She brings sharp examples from real teams, highlights the quiet builders shaping change, and roots her perspective in the resilience she learned from the women in her own family. If you want a straightforward view of what practical, human-centered AI adoption actually looks like, this episode is worth your time.About PamPam Boiros is a consultant who helps marketing teams find direction and build plans that feel doable. She leads Marketing AI Jump Start and works as a fractional CMO for clients like Reclaim Health, giving teams practical ways to bring AI into their day-to-day work. She’s also a founding member of Women Applying AI, a new community that launched in Sep 2025 that creates a supportive space for women to learn AI together and grow their confidence in the field.Earlier in her career, Pam spent 12 years at a fast-growing startup that Skillsoft later acquired, then stepped into senior marketing and product leadership there for another three and a half years. That blend of startup pace and enterprise structure shapes how she guides her clients today.How To Audit Data Fingerprints For AI Bias In MarketingAI bias spreads quietly in marketing systems, and Pam treats it as a pattern problem rather than a mistake problem. She explains that models repeat whatever they have inherited from the data, and that repetition creates signals that look normal on the surface. Many teams read those signals as truth because the outputs feel familiar. Pam has watched marketing groups make confident decisions on top of datasets they never examined, and she believes this is how invisible bias gains momentum long before anyone sees the consequences.Pam describes every dataset as carrying a fingerprint. She studies that fingerprint by zooming into the structure, the gaps, and the repetition. She looks for missing groups, inflated representation, and subtle distortions baked into the source. She builds this into her workflow because she has seen how quickly a model amplifies the same dominant voices that shaped the data. She brings up real scenarios from her own career where women were labeled as edge cases in models even though they represented half the customer base. These patterns shape everything from product recommendations to retention scores, and she believes many teams never notice because the numbers look clean and objective."Every dataset has a fingerprint. You cannot see it at first glance, but it becomes obvious once you look for who is overrepresented, who is underrepresented, or who is misrepresented."Pam organizes her process into three cycles that marketers can use immediately.The habit works because it forces scrutiny at every stage, not just at kickoff.Before building, trace the data source, the people represented, and the people missing.While building, stress test the system across groups that usually sit at the margins.After launch, monitor outputs with the same rhythm you use for performance analysis.She treats these cycles as an operational discipline. She compares the scale of bias to a compounding effect, since one flawed assumption can multiply into hundreds of outputs within hours. She has seen pressure to ship faster push teams into trusting defaults, which creates the illusion of objectivity even when the system leans heavily toward one group’s behavior. She wants marketers to recognize that AI audits function like quality control, and she encourages them to build review rituals that continue as the model learns. She believes this daily maintenance protects teams from subtle drift where the model gradually leans toward the patterns it already prefers.Pam views long term monitoring as the part that matters most. She knows how fast AI systems evolve once real customers interact with them. Bias shifts as new data enters the mix. Entire segments disappear because the model interprets their silence as disengagement. Other segments dominate because they participate

What’s up everyone, today we have the pleasure of sitting down with Anna Leary, Director of Marketing Operations at Alma.(00:00) - Intro (01:15) - In This Episode (04:38) - How to Prevent Burnout (05:46) - What Companies Can Do Better (07:50) - Agility of Planning (08:53) - Why Saying No Strengthens Marketing Operations (13:48) - How to Decide When to Push Back (18:03) - Hill To Die On (20:03) - How to Handle Constant Pushback (29:55) - Wishlist (37:06) - How to Use Asynchronous Communication to Reduce Stress (44:24) - How To Evaluate Martech Tools Based On Real Business Impact (48:45) - Why Marketing Ops Needs Visible Work Systems (51:24) - Health Awareness (52:56) - How to Recognize and Prevent Burnout in Marketing Operations Summary: Anna built systems to keep marketing running smoothly, but the real lesson came when those same systems failed to protect her. In this episode, she shares how saying no became her survival skill, why visibility is the antidote to burnout, and how calm structure (not constant hustle) keeps teams sharp and human. It’s a story about boundaries, balance, and learning to lead without losing yourself.About AlmaAnna Leary is the Director of Marketing Operations at Alma, where she builds scalable systems that help marketing teams work smarter. With a focus on lead flow, data architecture, and enablement, she’s known for creating centers of excellence that turn fragmented operations into cohesive, measurable programs. As a Marketo Certified Solutions Architect and Marketo Measure (Bizible) specialist, Anna brings a rare balance of technical depth and strategic clarity to every initiative she leads.Before joining Alma, Anna spent more than a decade shaping marketing operations strategies for brands like Uber, Teamwork, Sauce Labs, and Bitly. Whether optimizing attribution models or training teams to adopt new workflows, Anna’s work always centers on efficiency, empowerment, and driving impact across the full marketing ecosystem.Burnout and BalanceMarketing ops work demands constant precision. Teams juggle system integrations, data cleanups, and new tech rollouts, often all before lunch. The job requires mental endurance and a tolerance for chaos. Anna understands this well. “Everyone’s trying to be the person who knows the newest tech,” she said. “It’s hard to keep up, and that adds to the mental load.” The competition to stay relevant has turned into a quiet stress test that too many operators fail without noticing.The strange part is that ops teams often create systems designed to protect their organizations but rarely use those same systems to protect themselves. Anna explained how Service Level Agreements (SLAs) can lose their meaning when teams treat them as flexible. Urgent requests push through, exceptions pile up, and structure dissolves. Each “quick favor” chips away at the purpose of having defined processes. She put it plainly:“If we’re making an exception for everything, then we’re not respecting the process.”When teams stop respecting their own boundaries, burnout follows quickly. SLAs exist to create stability, and stability is what keeps people sane. Following process is not bureaucracy; it is protection. It gives operators time to think clearly, plan ahead, and make fewer reactive decisions. That way you can build predictability into your week instead of letting other people’s emergencies define it.Anna also shared how her team reworked its entire planning system to reduce stress. “We used to do quarterly capacity planning,” she said, “but half the projects fell apart by week four.” She scrapped the process and replaced it with smaller, rolling cycles that fit the unpredictable nature of marketing requests. For someone who identifies as Type A, letting go of that much structure felt risky, but the tradeoff was worth it. Her team now works with more energy, less anxiety, and a better sense of control.“Giving up some of that control is actually good in the end because it’s less stressful.”Her story shows how burnout prevention depends on structure that adapts. Ops professionals do their best work when their systems reflect real life, not an idealized version of it. Boundaries, planning, and discipline should support humans, not box them in.Key takeaway: Protect your team’s mental health by enforcing the systems you build. Treat SLAs as promises, not preferences. Review your planning cycles regularly and adjust them to match the actual pace of work. Stability in ops comes from building rules that people respect and structures that evolve as the business changes.The Power of NoSaying no is one of the hardest and most necessary skills in marketing operations. Every week brings a new request, a “quick fix,” or a last-minute idea that someone swears will only take five minutes. Anna treats these moments as boundary checks. They test whether her team can protect their focus without losing trust or influence across the company.“Boundaries in your personal life mirror boundaries

What’s up everyone today we have the pleasure of chatting with Blair Bendel, Senior Vice President of Marketing at Foxwoods Resort Casino.(00:00) - Intro (00:49) - In This Episode (03:39) - Evolution of Casino Martech (06:11) - Customer Loyalty & Personalization (09:36) - Using the Right Marketing Channel for the Right Goal in Hospitality (12:38) - Foxwood’s Martech and Customer Data Migration to MoEngage (15:05) - Picking MoEngage (20:07) - Why Change Tools?? (22:46) - Implementing a New Platform (24:58) - Building Structure for 24/7/365 Casino Marketing (31:20) - Key Things to Track (33:15) - Fail Fast, Learn Faster (37:25) - Balancing Big Data with Privacy (40:23) - Why AI Will Not Fix Casino Marketing Overnight (43:23) - Exploring AI (46:59) - Human Experience Drives Long-Term Casino Revenue (49:05) - Human Side (52:12) - Why Face-to-Face Conversations Strengthen Marketing Teams Summary: The casino floor never sleeps. Lights hum, cards shuffle, and people come not just to gamble but to feel alive. While other industries went digital overnight, casinos stayed grounded in human moments, and Blair’s mission has been to connect those experiences through smarter tech. At Foxwoods, he replaced a maze of disconnected martech with a single platform, giving his team one clear view of every guest. Push messages became quick nudges, emails carried depth, and silence built trust. In a business that runs 24/7/365, his team moves fast, learns constantly, and protects what matters most: guest privacy. About BlairBlair Bendel has spent nearly two decades shaping brands that make casinos feel alive. As SVP of Marketing at Foxwoods Resort Casino, one of the world’s largest gaming and entertainment destinations, he leads strategy across brand, digital, loyalty, and guest experience for a property owned by the Mashantucket Pequot Tribal Nation.Before Foxwoods, Blair drove marketing for Boyd Gaming and Pinnacle Entertainment, guiding multi-property teams through high-stakes launches and rebrands. Known for blending data and instinct, he’s built campaigns that turn foot traffic into fandom and moments into measurable growth.The Evolution of Casino MartechCasinos thrive on the energy of real people in real spaces. Blair has spent his career in that environment, where the hum of slot machines and the rhythm of foot traffic define success. He points out that while other industries rushed to digitize, gaming and hospitality focused on the on-property experience that drives most of their revenue. Technology in this world serves the guest standing in front of you, not a distant audience online.“There’s a lot of innovation, but it’s all centered around that customer and that on-property experience,” Blair said.Walk across a modern casino floor and you see how far that innovation has gone. Slot machines now reach twelve feet high, lit by curved screens that feel more like immersive art installations than games. Even bingo, once a paper-and-pen ritual, lives on tablets. These changes reflect more than aesthetic upgrades. They mark the blending of digital personalization with in-person entertainment. Each new machine and experience collects data, interprets patterns, and helps casinos understand what keeps players coming back.Blair sees the next phase of progress in the pairing of martech systems and artificial intelligence. Casinos have long collected data on player habits, but much of it stayed locked in isolated databases. AI now connects those dots, linking preferences, visit frequency, and loyalty activity into one living profile. That way you can predict what a guest wants before they ask for it. Personalized dining offers, targeted game promotions, or well-timed follow-up messages all become part of a continuous loop that strengthens engagement.Still, Blair focuses on the human side of this transformation. “People assume tech makes everything easier, and it doesn’t,” he said. Each new platform requires training, integration, and trust. Martech without people who know how to use it becomes clutter. Blair spends much of his time ensuring his team understands the technology deeply enough to keep the guest experience effortless. The strategy depends on teams who can think like data analysts and act like hosts.Key takeaway: Martech and AI can elevate on-property hospitality when used to deepen human connection instead of replacing it. Integrate systems that unify guest data, but prioritize training and comfort among your team. When your people trust the tools and your guests feel known, technology quietly fades into the background while loyalty takes center stage.Customer Loyalty and Personalization in Casino MarketingCasino marketing has operated on autopilot for too long. Guests still get dropped into massive segmentation buckets, treated as if their weekend habits, entertainment tastes, and spending patterns are interchangeable. Blair describes it bluntly: “We still send show offers to guests who’ve never been to a concert in

What’s up folks, today we have the pleasure of sitting down with Megan Kwon, Director, Digital Customer Communications at Loblaw Digital.(00:00) - Intro (01:26) - In This Episode (04:11) - Building a Career Around Conversations That Scale (06:25) - Customer Journey Pods and Martech Team Structures (09:08) - Martech Team Structures (11:23) - Customer Journey Martech Pods (12:54) - How to Assign Martech Tool Ownership and Drive Real Adoption (14:54) - Martech Training and Onboarding (17:30) - How To Integrate New Martech Into Daily Habits (19:59) - Why Change Champions Work in Martech Transformation (24:11) - Change Champion Example (28:25) - How To Manage Transactional Messaging Across Multiple Brands (32:35) - Frequency and Recency Capping (35:59) - Why Shared Ownership Improves Transactional Messaging (41:50) - Why Human Governance Still Matters in AI Messaging (47:11) - Why Curiosity Matters in Adapting to AI (53:08) - Creating Sustainable Energy in Marketing Leadership Summary: Megan leads digital customer communications at Loblaw Digital, turning enterprise-scale messaging into something that feels personal. She built her teams around the customer journey, giving each pod full creative and data ownership. The people driving results also own the tools, learning by building and celebrating small wins. Her “change champions” make new ideas stick, and her view on AI is grounded; use it to go faster, not think for you. Curiosity, she says, is what keeps marketing human.About MeganMegan Kwon runs digital customer communications at Loblaw Digital, the team behind how millions of Canadians hear from brands like Loblaws, Shoppers Drug Mart, and President’s Choice. She’s part strategist, part systems thinker, and fully obsessed with how data can make marketing feel more human, not less.Before returning to Loblaw, Megan helped reshape how people discover and trust local marketplaces at Kijiji, and before that, she built growth engines in the fintech world at NorthOne. Her career has been a study in scale; from scrappy e-commerce tests to national lifecycle programs that touch nearly every Canadian household. What sets her apart is the way she leads: with deep curiosity, radical ownership, and a bias for collaboration. She believes numbers tell stories, and that the best marketing teams build movements around insight, empathy, and accountability.Building a Career Around Conversations That ScaleRunning digital messaging at Loblaw means coordinating communication at a scale that few marketers ever experience. Megan oversees the systems that deliver millions of emails and texts across brands Canadians interact with daily, including Loblaws, Shoppers Drug Mart, and President’s Choice. Her team manages both marketing and transactional messages, making sure each one aligns with a specific stage in the customer journey. The workload is immense. Each division has its own priorities, and every campaign needs to fit within a shared infrastructure that still feels personal to the customer.“We work with a lot of different business divisions across the entire organization. Our job is to make sure their strategies and programs come to life through the customer lifecycle.”Megan’s team operates more like a connective tissue than a broadcast engine. They bridge the gaps between marketing, product, and data teams, translating disconnected strategies into a unified experience. That work involves building systems capable of:Managing multiple brand voices while keeping messaging consistentTriggering real-time communications that respond to customer behaviorIntegrating old and new technologies without breaking operational flowEvery campaign becomes part of a continuous conversation with the customer. Each message is one step in a long dialogue, not a one-off announcement.Megan’s perspective comes from experience earned in very different industries. She began her career at Loblaw during the early days of online grocery, a time when digital operations were experimental and resourceful. She later worked across fintech, marketplaces, and paid media before returning to Loblaw. That journey helped her understand every layer of the customer funnel, from acquisition through retention. It also taught her how to combine growth marketing tactics with enterprise-level communication systems, that way she can scale personalization without losing humanity.Most large organizations still treat messaging as a collection of isolated programs. Megan treats it as an ecosystem. Her work shows that when lifecycle and acquisition efforts operate within a shared framework, communication becomes more coherent and far more effective. Alignment between data, channels, and teams reduces noise and builds trust with customers who engage across multiple brands.Key takeaway: Building a unified messaging ecosystem starts with structure, not volume. Create systems that connect channels, data, and brand voices into one coordinated experience. Treat messaging as a relation

What’s up everyone today we have the pleasure of sitting down with Jane Menyo, Sr. Director, Solutions & Customer Marketing @ Gong.(00:00) - Jane-audio (01:01) - In This Episode (04:43) - How Solutions Marketing Turns Customer Insights Into Strategy (09:22) - Using AI to Mine Real Customer Intelligence from Conversations (13:18) - Why Stitching Research Sequences Works in Customer Marketing (17:09) - Using AI Trackers to Uncover Buyer Behavior in Sales Conversations (23:21) - How Standardized Prompts Improve Sales Enablement Systems (29:43) - Building Messaging Systems That Scale Across Industries (34:15) - How Gong’s Research Assistant Slack Bot Delivers Instant Customer Proof (38:26) - Avoiding Mediocre AI Marketing Research (43:42) - Why Customer Proof Outperforms AI-Generated Marketing (45:41) - Why Rest Strengthens Creative Output in Marketing Summary: Jane built her marketing practice around listening. At Gong, she turned raw customer conversations into a live feedback system that connects sales calls, product strategy, and messaging in real time. Her team uses AI to surface patterns from the field and feed them back into content that actually reflects how people buy. She runs on curiosity and recovery, finding her best ideas mid-run. In a world obsessed with producing more, Jane’s work reminds marketers to listen better. The smartest strategies start in the quiet moments when someone finally hears what the customer’s been saying all along.About JaneJane Menyo leads Solutions and Customer Marketing at Gong, where she’s known for fusing strategy with storytelling to turn customers into true advocates. She built Gong’s customer marketing engine from the ground up, scaling programs that drive adoption, retention, and community impact across the company’s revenue intelligence ecosystem.Before Gong, Jane led customer and solutions marketing at ON24, where she developed go-to-market playbooks and launched large-scale advocacy initiatives that connected customer voice to product innovation. Earlier in her career, she helped shape demand generation and brand strategy at Comprehend Systems (a Y Combinator and Sequoia-backed life sciences startup) laying the operational groundwork that fueled growth.A former NCAA All-American and U.S. Olympic Trials contender, Jane brings a rare blend of discipline, creativity, and competitive energy to her leadership. Her approach to marketing is grounded in empathy and powered by data; a balance that turns customer stories into growth engines.How Solutions Marketing Turns Customer Insights Into StrategyJane’s role at Gong evolved from building customer advocacy programs to leading both customer and solutions marketing. What began as storytelling and adoption work expanded into shaping how Gong positions its products for different personas and industries. The shift moved her from celebrating customer wins to architecting how those wins inform the company’s broader go-to-market strategy.Persona marketing only works when it goes beyond demographics and titles. Jane treats it as an operational system that connects customer understanding with product truth. Her team studies how real people use Gong, where they get stuck, what outcomes they care about, and how their teams actually make buying decisions. Those details guide every message Gong sends into the market. It is a constant feedback loop that keeps the company close to how customers think and work.Her solutions marketing team functions like a mirror to product marketing. Product marketers focus on what the product can do, while Jane’s team translates that into why it matters to specific audiences. They do not write from feature lists. They write from the field. When a sales manager spends half her day in Gong but still struggles to coach reps efficiently, Jane’s team crafts stories and materials that speak directly to that pain. The goal is to make every communication feel like it was written from inside the customer’s daily workflow.“Our work is about meeting customers where they are and helping them get to outcomes faster,” Jane said.That perspective only works when every team in the company has equal access to the customer’s voice. Gong’s own technology makes that possible. Conversations, feedback, and usage patterns are captured and shared automatically, so customer knowledge is no longer limited to those on the front lines. Jane’s group uses that visibility to deepen persona profiles, test new positioning, and identify emerging trends before they reach scale. It makes the company more responsive and keeps messaging grounded in real behavior instead of assumption.For anyone building customer marketing systems, the lesson is practical. Treat persona development as a live system, not a static report. Use customer data to update your understanding regularly. Create tools that let everyone in your company hear what customers say in their own words. That way you can write content, sales materials, and product messaging th

What’s up everyone, today we have the pleasure of sitting down with David Joosten, Co-Founder and President at GrowthLoop and the co-author of ‘First-Party Data Activation’.(00:00) - Intro (01:02) - In This Episode (03:47) - Earning The Right To Transform Martech (08:17) - Why Internal Roadshows Make Martech Wins Stick (10:52) - Architecture Shapes How Teams Move and What They Believe (16:25) - Bring Order to Customer Data With the Medallion Framework (21:33) - The Real Enemy of Martech is Fragmented Data (28:39) - Stop Calling Your CRM the Source of Truth (34:47) - Building the Tech Stack People Rally Behind (38:18) - Why Most CDP Failures Start With Organizational Misalignment (44:18) - Why Tough Conversations Strengthen Lifecycle Marketing (55:15) - Why Experimentation Culture Strengthens Martech Leadership (01:00:00) - How to Use a North Star to Stay Focused in Leadership Summary: David learned that martech transformation begins with proof people can feel. Early in his career, he built immaculate systems that looked impressive but delivered nothing real. Everything changed when a VP asked him to show progress instead of idealistic roadmaps. From that moment, David focused on momentum and quick wins. Those early victories turned into stories that spread across the company and built trust naturally. Architecture became his silent advantage, shaping how teams worked together and how confidently they moved. About DavidDavid is the co-founder of GrowthLoop, a composable customer data platform that helps marketers connect insights to action across every channel. He previously worked at Google, where he led global marketing programs and helped launch the Nexus 5 smartphone. Over the years, he has guided teams at Indeed, Priceline, and Google in building first-party data strategies that drive clarity, collaboration, and measurable growth.He is the co-author of First-Party Data Activation: Modernize Your Marketing Data Platform, a practical guide for marketers who want to understand their customers through direct, consent-based interactions. David helps teams move faster by removing data friction and building marketing systems that adapt through experimentation. His work brings energy and empathy to the challenge of modernizing data-driven marketing.Earning The Right To Transform MartechEvery marketing data project starts with ambition. Teams dream of unified dashboards, connected pipelines, and a flawless single source of truth. Then the build begins, and progress slows to a crawl. David remembers one project vividly. His team at GrowthLoop had connected more than 200 data fields for a global tech company, yet every new campaign still needed more. The setup looked impressive, but nothing meaningful was shipping.“We spent quarters building the perfect setup,” David said. “Then the VP of marketing called me and said, ‘Where are my quick wins?’”That question changed his thinking. The VP wasn’t asking for reports or architecture diagrams. He wanted visible proof that the investment was worth it. He needed early wins he could show to leadership to keep momentum alive. David realized that transformation happens through demonstration, not design. Theoretical perfection means little when no one in marketing can point to progress.From then on, he started aiming for traction over theory. That meant focusing on use cases that delivered impact quickly. He looked for under-supported teams that were hungry to try new tools, small markets that moved fast, and forgotten product lines desperate for attention. Those early adopters created visible success stories. Their enthusiasm turned into social proof that carried the project forward.Momentum built through results is what earns the right to transform. When others in the organization see evidence of progress, they stop questioning the system and start asking how to join it.Key takeaway: Martech transformations thrive on proof, not perfection. Target high-energy teams where quick wins are possible, deliver tangible outcomes fast, and use that momentum to secure organizational buy-in. Transformation is granted to those who prove it works, one visible success at a time.Why Internal Roadshows Make Martech Wins StickAn early martech win can disappear as quickly as it arrives. A shiny dashboard, a clean sync, or a new workflow can fade into noise unless you turn it into something bigger. David explains that the real work begins when you move beyond Slack celebrations and start building visibility across the company. The most effective teams bring their success to where influence actually happens. They show up in weekly leadership meetings for sales, data, and marketing, and they connect their progress to the company’s larger mission. That connection transforms an isolated result into shared purpose.“If you can get invited to those regular meetings and actually tie the win back to the larger vision, you’ll bring people along in a much bigger way,” David said.The mechanics of this mat

What’s up everyone, today we have the pleasure of sitting down with Angela Vega, Director, Capabilities and Operations at Expedia Group.(00:00) - ‌Intro (01:18) - In This Episode (04:55) - Building an ADHD Techstack (11:11) - Why ADHD Shapes Better Decision-Making in Marketing Operations (19:06) - How to Turn ADHD Patterns Into Martech Leadership Strengths (23:38) - Why ADHD Helps Marketers Build Better Systems (31:25) - Building a Bridge Between Strategy and Execution in Marketing Ops (37:21) - Execution Defines Whether Ideas Live or Die (41:19) - Why Recent Execution Experience Builds Better Marketing Leaders (46:09) - How to Build Discernment in Martech Leadership (52:52) - Energy Economics for Marketing Ops Leaders (01:00:39) - How to Build a Personal Growth Formula in Marketing Leadership Summary: Angela built her ADHD tech stack as a way to survive the noise in her own head, turning distraction into design. Her workflow (Offload, Shape, Prototype, Loop, and Anchor) channels restless thought into motion through AI tools like Whisper and GPT. After her second pregnancy and a diagnosis that reframed her chaos, Angela stopped fighting her wiring and built systems that worked with it. Her fast, pattern-driven brain now thrives in marketing operations, where complexity rewards connection. She reads emotion like data, earning trust through clarity and transparency, and reminds leaders that execution, not strategy decks, moves companies forward. These days she measures success in energy and her mantra is “It’s just marketing, we’re not in the ER”.About AngelaAngela Vega has spent over 13 years in FinTech, health, and travel, she has unified global martech stacks, accelerated execution ninefold, and led CRM for Expedia, Vrbo, and Hotels.com, supporting over a billion monthly customer interactions. Her leadership grows both teams and ideas. She blends creative intuition with operational rigor, translating insight into systems that last. As a late-diagnosed ADHD professional, she experiments with AI to help neurodivergent leaders thrive, proving that marketing can be both human and scalable.Building an ADHD TechstackAngela built her ADHD Tech Stack to make her brain an ally instead of a hurdle. The system blends ADHD science with AI practicality, turning common executive function challenges into structured momentum. Each part of her workflow (Offload, Shape, Prototype, Loop, and Anchor) acts as a circuit for channeling mental noise into clarity. It is both a workflow and a survival strategy for people who juggle too many tabs at once, whether they are digital or mental.Her starting point came from frustration. Lists, sticky notes, and phone alarms worked for a while but always hit a ceiling. The real struggle was never remembering to do things but figuring out where to start. Executive function is about getting from idea to action, and for ADHD professionals, that gap can feel massive. Angela found her bridge in AI tools that could listen, capture, and organize her thinking in real time. Whisper transcribes her thoughts. GPT shapes them into frameworks. Gemini helps her plan and communicate with clarity.“I talk out loud all the time. Instead of saying things into the abyss, I say them into AI,” Angela said. “One system holds my to-dos, another handles updates for my boss, and another helps me break big goals into smaller steps.”Her stack follows five steps that anyone can adapt:Offload: Speak or type ideas into AI to clear mental clutter.Shape: Ask AI to sort and group ideas into logical categories.Prototype: Turn thoughts into quick drafts or mockups to trigger dopamine and action.Loop: Use AI for feedback, reflection, and gentle nudges that replace self-criticism.Anchor: Set reminders, templates, and adaptive systems that help you return to projects smoothly.Angela’s framework works because it aligns tools with real human behavior instead of forcing people into rigid systems. The design rewards momentum over perfection. It gives permission to think out loud, change direction, and experiment without shame. Every ADHD brain operates differently, so every system should too. AI’s flexibility makes that possible by turning scattered thoughts into structured workflows without losing the spark that drives creativity.Key takeaway: Treat productivity as a design challenge, not a discipline test. Use AI to capture ideas before they vanish, shape them into usable form, and build adaptive anchors that forgive interruptions. That way you can create a personal martech system that channels ADHD energy into consistent output, steady progress, and fewer moments of paralysis.Why ADHD Shapes Better Decision-Making in Marketing OperationsADHD rewires how people handle complexity, and marketing operations thrive on complexity. Angela discovered that her diagnosis reframed everything about her work and leadership. Years of restless multitasking, late-night thought spirals, and endless side projects suddenly made sense. Her mind was

What’s up everyone, today we have the pleasure of sitting down with Aboli Gangreddiwar, Senior Director of Lifecycle and Product Marketing at Credible. (00:00) - Intro (01:10) - In This Episode (04:54) - Agentic Infrastructure Components in Marketing Operations (09:52) - Self Healing Data Quality Agents (16:36) - Data Activation Agents (26:56) - Campaign QA Agents (32:53) - Compliance Agents (39:59) - Hivemind Memory Curator (51:22) - AI Browsers Could Power Living Documentation (58:03) - How to Stay Balanced as a Marketing Leader Summary: Aboli and Phil explore AI agent use cases and the operational efficiency potential of AI for marketing Ops teams. Data quality agents promise self-healing pipelines, though their value depends on strong metadata. QA agents catch broken links, design flaws, and compliance issues before launch, shrinking review cycles from days to minutes. An AI hivemind memory curator that records every experiment and outcome, giving teams durable knowledge instead of relying on long-tenured employees. Documentation agents close the loop, with AI browsers hinting at a future where SOPs and playbooks stay accurate by default. About AboliAboli Gangreddiwar is the Senior Director of Lifecycle and Product Marketing at Credible, where she leads growth, retention, and product adoption for the personal finance marketplace. She has previously led lifecycle and product marketing at Sundae, helping scale the business from Series A to Series C, and held senior roles at Prosper Marketplace and Wells Fargo. Aboli has built and managed high-performing teams across acquisition, lifecycle, and product marketing, with a track record of driving customer growth through a data-driven, customer-first approach.Agentic Infrastructure Components in Marketing OperationsAgentic infrastructure depends on layers that work together instead of one-off experiments. Aboli starts with the data layer because every agent needs the same source of truth. If your data is fragmented, agents will fail before they even start. Choosing whether Snowflake, Databricks, or another warehouse becomes less about vendor preference and more about creating a system where every agent reads from the same place. That way you can avoid rework and inconsistencies before anything gets deployed.Orchestration follows as the layer that turns isolated tools into workflows. Most teams play with a single agent at a time, like one that generates subject lines or one that codes email templates. Those agents may produce something useful, but orchestration connects them into a process that runs without human babysitting. In lifecycle marketing, that could mean a copy agent handing text to a Figma agent for design, which then passes to a coding agent for HTML. The difference is night and day: disconnected experiments versus a relay where agents actually collaborate.“If I am sending out an email campaign, I could have a copy agent, a Figma agent, and a coding agent. Right now, teams are building those individually, but at some point you need orchestration so they can pass work back and forth.”Execution is where many experiments stall. An agent cannot just generate outputs in a vacuum. It needs an environment where the work lives and runs. Sometimes this looks like a custom GPT creating copy inside OpenAI. Other times it connects directly to a marketing automation platform to publish campaigns. Execution means wiring agents into systems that already matter for your business. That way you can turn novelty into production-level work.Feedback and human oversight close the loop. Feedback ensures agents learn from results instead of repeating the same mistakes, and human review protects brand standards, compliance, and legal requirements. Tools like Zapier already help agents talk across systems, and protocols like MCP push the idea even further. These pieces are developing quickly, but most teams still treat them as experiments. Building infrastructure means treating feedback and oversight as required layers, not extras.Key takeaway: Agentic infrastructure requires more than a handful of isolated agents. Build it in five layers: a unified data warehouse, orchestration to coordinate handoffs, execution inside production tools, feedback loops that improve performance, and human oversight for brand safety. Draw this stack for your own team and map what exists today. That way you can see the gaps clearly and design the next layer with intention instead of chasing hype.Self Healing Data Quality AgentsAutonomous data quality agents are being pitched as plug-and-play custodians for your warehouse. Vendors claim they can auto-fix more than 200 common data problems using patterns they have already mapped from other customers. Instead of ripping apart your stack, you “plug in” the agent to your warehouse or existing data layer. From there, the system runs on the execution layer, watching data as it flows in, cleaning and correcting records without waiting for human approva

What’s up everyone, today we have the pleasure of sitting down with Henk-jan ter Brugge, Head of global digital programs and Martech at Philips.(00:00) - Intro (01:17) - In This Episode (05:11) - Embracing the Digital Pirate Mindset in Martech (16:18) - Why Clean Data Is the Real Treasure Map for AI in Marketing Ops (19:20) - Why Composable Martech Stacks Work in High Seas Regulated Enterprises (24:35) - Rethinking Martech as People Tech (32:51) - Elevating Martech Teams Beyond Button Pushing (37:16) - Where Martech Should Report in the Organization (42:58) - Unlocking Innovation Through the Long Tail of Martech (47:42) - The Limits of Vendor Isolation in Martech (52:12) - Philips Digital Marketing & e-Commerce Stack (55:10) - How to Use Weekly Prioritization to Protect Energy Summary: Henk-jan works like a pirate inside the navy, exposing inefficiency with data, redesigning roles around real capabilities, and breaking AI promises into measurable wins backed by clean data and clear standards. He treats composability as an operating model with budgets tied to usage, gives local teams autonomy within guardrails, and measures martech by how it serves people and drives revenue. Ops leaders earn influence by pulling in allies and securing executive sponsorship, while reporting debates matter less than accountability and outcomes. Real innovation comes from embracing the long tail of smaller tools, working with vendors who integrate into the ecosystem, building adoption models with champions, and protecting energy through ruthless prioritization.About Henk-janHenk-jan ter Brugge is Head of Digital Programs and Martech at Philips, where he leads the global digital marketing and ecommerce technology team. With over a decade at Philips, he has driven transformation across CRM, ecommerce, sales enablement, web experience, ad tech, analytics, and AI innovation. Henk-jan is a lean and agile certified leader who believes technology is an enabler, but it’s people who create the real impact. His career spans international experience in Seoul, Paris, and Shanghai, and he is a frequent keynote speaker on martech, salestech, and digital transformation. Passionate about improving health and wellbeing through meaningful innovation, he connects strategy, technology, and change management to deliver customer value at scale.Embracing the Digital Pirate Mindset in MartechPirates were early system hackers. They rewrote rules on their ships, experimented with shared decision-making, and introduced ideas like equal pay centuries before they reached land. That spirit of rewriting norms has carried into Henk-jan’s work in martech. He frames the pirate as someone inside the navy, pushing the big ship to move differently, rather than a rogue causing chaos on the outside.Corporate inertia creates its own myths. Vendor onboarding still takes 12 to 18 months in some organizations. Translation cycles hold content hostage for weeks. Colleagues accept these delays as culture, with a shrug and a “that’s just how we do things.” Henk-jan refuses to let tradition dictate output. He arms himself with data and turns it into proof. If a team claims a translation cycle takes three months, he presents the real number: 10, 15, maybe 20 days.“Everything we say can be data driven. If someone tells me translation takes three months, I can show with data that it takes 10, 15, maybe 20 days. The data talks there.”The pirate mindset works only when it builds coalitions. Lone rebels fade out in corporate structures. Movements form when people across teams share the same impatience for inefficiency and the same hunger for progress. That is why Henk-jan focuses on allies who welcome change. With them, he introduces controlled experiments that rewire expectations step by step until the new way becomes the default.One of his boldest moves came in team design. He rebranded product owners as platform managers. They stopped acting like ticket clerks and became capability builders, consultants, and business partners. They handled strategy, education, and enablement, while still owning the backlog. A time study revealed that 70 percent of team energy had been going into internal operations. After the shift, 60 percent went directly into business-facing work. The lesson was clear: titles shape behavior, and behavior shapes impact.Key takeaway: The digital pirate mindset thrives when you expose inefficiency with data, recruit allies who share your appetite for change, and redesign roles so teams build capabilities instead of servicing tickets. Work inside the system, use transparency to gain trust, and experiment in controlled steps. That way you can redirect energy from internal bureaucracy toward direct customer value, creating momentum that compounds over time.Why Clean Data Is the Real Treasure Map for AI in Marketing OpsSpeaking of chasing treasures… AI has forced leadership teams to finally pay attention to the quality of their data. Henk-jan described it with

What’s up everyone, today we have the pleasure of sitting down with Aditi Uppal, Vice President, Digital Marketing and Demand Generation at Teradata.(00:00) - Intro (01:15) - In this Episode (04:03) - How to Use Customer Conversations to Validate Marketing Data (10:49) - Balancing Quantitative Data with Customer Conversations (16:14) - Gathering Customer Insights From Underrated Feedback Channels (22:00) - Activating Voice of Customer with AI Agents (29:09) - Voice of Customer Martech Examples (34:48) - How to Use Rapid Response Teams in Marketing Ops (39:07) - Building Customer Obsession Into Marketing Culture (43:44) - Why Voice of Customer Works Differently in B2B and B2C (48:26) - Why Life Integration Works Better Than Work Life Balance Summary: Aditi shows how five honest conversations can reshape how you read data, because customer language carries context that numbers miss. She points to overlooked signals like product usage trails, community chatter, sales recordings, and event conversations, then explains how to turn them into action through a simple pipeline of capture, tag, route, track, and activate. Tools like BrightEdge and UserEvidence prove their worth by removing grunt work and delivering usable outputs. The system only works when culture supports it, with rapid response channels, proposals that start with customer problems, and councils that align leaders around real needs. Blend the speed of B2C listening with the discipline of B2B execution, and you build strategies grounded in reality.About AditiAditi Uppal is a data-driven growth leader with over a decade of experience driving digital transformation, product marketing, and go-to-market strategy across India, Canada, and the U.S. She currently serves as Vice President of Digital Marketing and Demand Generation at Teradata, where she leads global strategies that fuel pipeline growth and customer engagement. Throughout her career, Aditi has built scalable marketing systems, launched partner programs delivering double-digit revenue gains, and led multi-million-dollar campaign operations across more than 50 technologies. Recognized as a B2B Revenue Marketing Game Changer, she is known for blending strategy, operations, and technology to create high-performing teams and measurable business impact.How to Use Customer Conversations to Validate Marketing DataDashboards create scale, but they do not always create confidence. Aditi explains that marketers often stop at what the model tells them, without checking whether real people would ever phrase things the same way. Early in her career she spent time talking directly to retailers, truck drivers, and mechanics. Those interactions were messy and slow, filled with handwritten notes, but they gave her words and patterns that no software could generate. That language still shapes how she thinks about campaigns today.She argues that even a small number of conversations can sharpen a marketer’s decisions. Five well-chosen interviews can give more clarity than months of chasing analytics dashboards. Once you hear a customer describe a problem in their own terms, the charts you already have feel more trustworthy. As Aditi put it:“If you get an insight that says this is their pain point, it helps so much to hear a customer saying it. The words they use resonate with them in ways marketers’ words often do not.”She points out that B2C teams benefit from built-in feedback loops since their channels naturally keep them closer to customers. B2B teams, on the other hand, often hide behind personas and assumptions. Aditi suggests widening the pool by talking to students and early-career professionals who already use enterprise software. They may not be buyers today, but they become decision makers tomorrow. Those conversations cost almost nothing and create raw material more valuable than agency-produced content.She frames the real task as choosing the right method for the right question. If you want to refine messaging, talk to your most active customers. If you want to understand adoption patterns, run reports. If you want to pressure test a product roadmap, combine both and compare the results. Decide upfront what you need and when you need it. Then continue adjusting, because customer understanding is not a one-time project, it is an ongoing discipline.Key takeaway: Use customer conversations as a validation layer for your data. Pair five direct interviews with your dashboards, and you gain language, context, and trust that numbers alone cannot provide. Always define why you need an insight, then pick the method that gets you there fastest. That way you can build messaging, campaigns, and roadmaps grounded in reality rather than in assumptions.Balancing Quantitative Data with Customer ConversationsMarketers keep adding dashboards, yet confidence in the numbers rarely grows. Aditi argues that a few customer conversations often do more to build certainty than a warehouse of metrics. Early in her career she

What’s up folks, today we have the pleasure of sitting down with Rebecca Corliss, VP Marketing at GrowthLoop. (00:00) - Intro (01:20) - In This Episode (03:46) - The Future Agentic Marketing Org (07:59) - The Rise of the Marketing Dispatch Layer (14:47) - Lifecycle Marketers Belong at the Center of Every Agentic Org (21:19) - Why Channel Specialists Must Shift to Journey Orchestration (25:06) - How To Actually Become More Strategic (29:28) - This Team Promoted ChatGPT to Director of Product Marketing (32:55) - What it Means to Be a Specialist in the Moment Works (37:12) - How Systems Thinking Helps Lifecycle Marketers Shine in Agentic AI (40:10) - How AI Expands the Role of Marketing Ops (43:37) - The Speculative Future of Marketing With Compute Allocation and Machine Customers (46:35) - Mesh of Agents Coordinating Across Departments (50:07) - The Rise of Machine Customers (53:55) - How to Stay Energized as a Marketing Leader Summary: Rebecca imagines a future marketing org built on three layers: leadership fluent in data and AI, a dispatch control tower staffed by engineers and privacy experts, and pods that design customer journeys while agents handle scale. Lifecycle marketers are essential to this dispatch layer and provide the “heart,” keeping campaigns authentic. Her own path as a “specialist in the moment” shows the power of adaptability, diving deep where it counts and moving on with impact. The marketers who thrive will be those who pair technical fluency with empathy and judgment.About RebeccaRebecca is a veteran marketing executive known for building engines that drive outsized growth. She is currently VP of Marketing at GrowthLoop, shaping the go-to-market for its Compound Marketing Engine. Previously, she scaled VergeSense from Series A through Series C with over 8X ARR growth, and at Owl Labs she took the company from launch to 35,000 customers worldwide while establishing it as a future-of-work leader. She also spent eight years at HubSpot, where she grew demand generation to 60K leads per month, doubled blog-driven leads, and built leadership programs that developed the next generation of marketers. Across every role, Rebecca has consistently turned early-stage momentum into durable, scalable growth.The Future Agentic Marketing Org and the Rise of the Marketing Dispatch LayerRebecca lays out a future where marketing org charts gain an entirely new layer. She predicts three core structures: leadership, dispatch, and pods. Leadership continues to steer strategy, but the demands on CMOs change. They will need fluency in data systems, architecture, and AI operations. Rebecca explains that “CMOs have to flex their technical chops and their data systems and architecture chops,” a shift for leaders who have historically leaned on brand or budget narratives.The dispatch layer functions as the operational hub for campaigns. This group manages data flows, AI orchestration, and channel activations. It operates like a control room for all outbound communication. Dispatch is staffed with people who rarely sat in marketing orgs before. Data engineers move in from IT, privacy specialists join the table, and Rebecca even describes “traffic cops” who arbitrate which campaigns reach a customer when multiple business units compete for the same audience.“Imagine this new dispatch layer, the group that is thinking about the systems, the data, the AI, the architecture, and campaign activation for the entire marketing org holistically.”Pods sit at the edge of this system, each one tasked with a specific objective. A retail pod might obsess over repeat purchases and next best product recommendations. Pods shape customer journeys, creative work, and product presentation. They do not execute campaigns directly. Instead, they work with dispatch to push scaled, AI-driven activations that tie back to their mission. This structure gives pods focus while ensuring campaign execution remains coordinated and efficient.Rebecca stresses that humans remain responsible for organizing this system. Agents will handle execution, but people set goals, decide structures, and elevate the skills required to manage AI effectively. The companies that thrive will be the ones that invest in human fluency now, especially in data architecture and cross-functional collaboration. Marketing leaders cannot wait for agents to make the org smarter. They have to build teams ready to use agents well.Key takeaway: Treat dispatch as a new operational hub inside marketing. Staff it with cross-functional talent such as data engineers, privacy experts, and campaign traffic managers. Align pods around clear business outcomes, and let them focus on customer journeys and creative execution. Give dispatch responsibility for scaling campaigns through AI agents. Start by training CMOs and their leadership peers to speak the language of data and AI strategy. That way you can prepare your organization to actually run an agentic structure instead of scrambling wh

What’s up everyone, today we have the pleasure of sitting down with John Saunders, VP of Product at Nova / Power Digital Marketing. Power Digital is a San Diego-based growth marketing firm. Nova is their proprietary marketing technology. (00:00) - Intro (01:15) - In This Episode (03:26) - How an Agency Operating System Reduces Silos (05:47) - Why Context Driven Analytics Replaces Dashboards (09:15) - Building a Single Source of Truth in Marketing Data (16:00) - Building an AI Cockpit Before AI Copilots (18:26) - Why Data Accuracy and Transparency Build AI Trust (28:28) - Building Internal Data Products for Agencies (34:09) - Reducing Complexity in Martech Product Development (39:16) - How To Tell If An AI Tool Is More Than A Wrapper (46:49) - How to Build Client Portals That Clients Actually Use (49:50) - Finding Happiness in Building and Experimentation Summary: Agencies are drowning in tools, dashboards, and AI gimmicks, but John Saunders has spent years building something that actually works. Nova started as an internal fix and grew into an operating system that strips away noise, delivers context with every number, and gives AI a cockpit filled with real operational data. Along the way John learned that trust comes from accuracy, speed, and transparency, and that adoption only happens when products remove steps instead of adding them. From client portals to analytics to AI, his story shows how clarity beats complexity and why agencies that chase it finally get technology that feels like leverage instead of liability.About JohnJohn Saunders is the Vice President of Product at Power Digital Marketing. He leads strategy, UX, operations, and AI for nova, the agency’s enterprise marketing technology platform that connects with more than 2,000 integrations. Since 2021, he has grown the technology team from 2 to 40 members, delivered more than 20 production-ready applications, and developed intelligence tools that improve client retention and increase lifetime value. He has also built partnerships with Google, Meta, TikTok, and Amazon that resulted in multi-million-dollar funding and new product capabilities.Prior to his current role, John served as Vice President of Technology. He built the first applications that became the foundation of nova and improved scalable systems, API integrations, cloud performance, and automation for the firm. He previously worked as Software Development Project Manager at Internet Marketing Inc. (now REQ), and Co-Founder of Brightside Network Media, a platform that combined technical design with storytelling to highlight culture and music.John has also mentored students at the Lavin Entrepreneurship Center at San Diego State University. He guided undergraduates in UX, product strategy, and agile workflows while encouraging leadership and collaboration in a hands-on environment.How an Agency Operating System Reduces SilosAgencies are drowning in tools. CRMs handle sales, project boards track tasks, invoicing software manages billing, and analytics dashboards measure performance. Each tool may solve a specific problem, but together they create a scattered system where every team works in isolation. John Saunders has seen this problem repeat across agencies, and his solution is direct. Build a single operating system that reflects how the agency actually works rather than relying on disconnected platforms that never sync.John described Nova as that system. Instead of forcing teams to reinvent contracts or pricing every time, Nova uses a service library with set rates and guidelines. Automation handles the repetitive work, so teams spend less time drafting proposals and more time serving clients. Nova acts as a hub for the agency’s real workflows. It connects sales, operations, and delivery into one shared environment where everyone can see the same information."With an agency OS, we are trying to fix this problem where there are so many tools and platforms that people work on, and that inherently creates silos. With one system focused on operations, it provides a central spot for everybody to work from, which creates efficiency and alignment."The need for this kind of system is obvious once you look closely at agency life. Account managers keep their own spreadsheets, sales leaders adjust pricing rules on the fly, and creative teams use tools that never connect with operations. The result is misalignment, duplicated effort, and wasted hours. An operating system forces the agency to define its rules and then codify them into the platform. That way you can cut the daily noise and create repeatable workflows that scale.Agencies often assume the next SaaS subscription will solve their problems. The reality is that the core problems are internal. Building an operating system like Nova does not replace tools, it makes them work together. It creates one place where every team operates from the same playbook. That way you can reduce inefficiency, strengthen alignment, and free people to foc

What’s up everyone, today we have the pleasure of sitting down with Olga Andrienko, Former VP of Marketing Ops at Semrush. (00:00) - Intro (01:24) - In This Episode (03:55) - How AI Agents Reshape Marketing Ops Roles (08:53) - How To Beat AI Imposter Syndrome And Start Using Custom GPTs (13:28) - How AI Content Agents Generate Drafts Using Internal Context (24:29) - How to Use a Risk and Reward Grid to Prioritize AI Projects (33:19) - How To Use Google Workspace To Skip AI Vendor Approvals (40:00) - How To Decide Which AI Agent to Use (46:44) - How To Build an AI-First Reflex in Marketing Ops (51:59) - AI’s Endgame: Play-to-Earn and Mandatory Human Quotas (01:03:58) - What Happens When You Optimize Your Body Like a Martech Stack Summary: Olga thought she was ahead of the AI curve, but a weekend course on autonomous systems showed her she was thinking too small. She pitched a shared internal AI stack at Semrush, built systems off APIs, skipped procurement by using already-approved tools, and tracked hours saved instead of promising vague ROI. She started with the work she already knew, made it faster, and used that time to build better systems. Now she’s looking ahead, watching work blur into participation, prepping for human quotas, and making sure ops teams aren’t caught off guard while the rest of the company is still testing prompts.About OlgaOlga Andrienko spent nearly 12 years at Semrush, where she helped build one of the strongest B2B marketing brands in tech. She started by leading social media, then expanded into global marketing, eventually becoming VP of Brand and later VP of Marketing Operations. She helped guide the company through its IPO, launched brand campaigns that drove massive reach, and scaled AI systems that saved her teams hundreds of hours. Most recently, she built out a marketing and AI ops function from scratch, automating reporting, content feedback, and influencer analytics across the org. Recently, Olga announced she was leaving Semrush to go out on her own. She’s now building a marketing SaaS product while advising companies on how to use AI agents to rethink marketing operations from the inside out.How AI Agents Reshape Marketing Ops RolesOlga had already logged countless hours with Claude and ChatGPT. She was building chatbots, fine-tuning prompts, and staying sharp on every update. Then she joined a weekend course on agent-based AI. At first, it felt like overkill. By the end of day two, she had completely changed direction. That course forced her to realize she had been spending time in the shallow end. Agent AI wasn’t just a smarter assistant. It was a structural overhaul. It changed what could be automated and who was needed to do it.Agent AI builds systems instead of just responding to inputs. Olga described a clean divide between tools that help you finish tasks faster and agents that actually run the tasks for you. How agent AI differs from task-level tools:Traditional tools require manual input for each useAgent systems operate autonomously and initiate actionsTools accelerate individual workAgents orchestrate end-to-end processesTools help you move fasterAgents help you step away entirelyShe saw use cases stacking up that didn’t fit inside marketing’s current playbook. Systems could now operate without manual checkpoints. Processes that once relied on operators could be built into fully autonomous loops.“I went into panic mode. Even with our tech stack at Semrush, I realized we were behind. Every company is behind.”The realization came with a cost model. Internal adoption of Claude and ChatGPT was rising fast. Olga noticed growing subscription bills across teams, with everyone spinning up individual accounts. She ran the numbers and saw the future expense curve. Giving each person their own sandbox didn’t scale. What made sense was building shared tools through APIs, designed to solve repeatable tasks. That way you can maintain quality, cut costs, and still give everyone access to powerful AI systems.Timing mattered. Olga was coming off a quarter where she had high visibility, internal trust, and a direct line to leadership. Instead of waiting for AI priorities to come down from the top, she used that leverage to move. She pitched a new team and made the case for shifting from brand to ops. She had technical interest, political capital, and an urgent belief that velocity mattered more than perfection.Key takeaway: Marketing ops leaders are uniquely positioned to build agent-level systems that scale across teams. Instead of waiting for strategy teams to greenlight AI plans, use cost data to make the case for shared infrastructure. Build with APIs, not individual tool access. Push for automation at the system level, not just task-level assistance. If you understand the workflows, know the tools, and already have trust inside the org, you are the one who should be building what comes next.How To Beat AI Imposter Syndrome And Start Using Custom GPTsAI imposter syndrome sh

What’s up everyone, today we have the pleasure of sitting down with Jonathan Kazarian, Founder & CEO of Accelevents.(00:00) - Intro (01:35) - In This Episode (03:41) - Are Point Solutions Actually a Distraction for Marketing Teams? (09:32) - Data Models Can Decide Platforms or Point Solutions (14:20) - Contact Based Pricing Skews Platform Versus Point Solution Costs (19:44) - Integration Depth Can Decide Platforms Versus Point Solutions (31:32) - Point Solutions Provide Faster and Smarter Support Than Platforms (37:28) - Documentation Shapes Point Solution Stacks (42:01) - How to Manage Shiny Object Syndrome in Marketing Ops (49:35) - A Founder's Admiration for Marketing Operators (54:42) - Why Continuous Growth Keeps Founders Balanced Summary: Jonathan framed point solutions as late-night distractions that add baggage, while Phil argued they solve real constraints platforms can’t touch, like global routing or multilingual campaigns. Darrell pulled the lens to data models, showing how shared schemas keep stacks clean but warehouse-native teams lean on composability for speed and control. Money made the tradeoffs clear when Phil cut HubSpot costs from $150k to $70k with Ghost, ConvertFlow, and Zapier, and Jonathan countered that the problem was platform fit, not price alone. Support stories added texture, with Phil praising startups that fix issues in Slack within hours and Jonathan noting how urgency and empathy thrive in smaller teams. The thread ran through every topic: platforms provide coherence and stability, point solutions unlock lift when constraints demand it, and the operator’s job is knowing which moment they are in.About JonathanJonathan Kazarian is the Founder & CEO of Accelevents, an all-in-one event management platform trusted by over 12,500 organizations worldwide. Since launching in 2015, he has led the company’s growth into a leader in powering in-person, virtual, and hybrid events with enterprise-grade features and 24/7 customer support. Before Accelevents, Jonathan worked in investment management and business development at Windham Labs and Windham Capital, where he supported strategy and client relationships across $1.5B in global assets. Based in Miami, he’s passionate about building technology that makes life easier for event organizers.Are Point Solutions Actually a Distraction for Marketing Teams?We all know the cycle of startups and enterprise. Point tools surge to fix sharp pains, a small group wins, platforms acquire them, founders spin out, and the next crop floods your feed. Jonathan thinks that those shiny tools pull teams off the work that actually moves numbers. He describes a scene every operator recognizes, the glow of a laptop at 3 a.m. and a to-do list that did not get shorter by sunrise.“I will see something, get excited about it, and then I am up until 3 a.m. playing with it. It distracts me from the things that actually matter.”Jonathan sets a firm bar for focus. Ship on a platform first, then layer selectively when a real constraint shows up. He treats events as a pillar beside CRM and marketing automation, so his platform must deliver value on day one without a four-tool puzzle. He stays explicit about the work that pays the bills:Tighten positioning so buyers understand you in one scroll.Communicate with customers in their language, not vendor speak.Make the core stack usable for sales, finance, and ops, not only for marketing.That way you can add niche tools later without freezing adoption while integrations sprawl.Phil takes the other corner and argues for composability with lived examples. He respects HubSpot and has shipped plenty on it, but real constraints demand specialists. Example: territory routing across pooled rep availability needs a product built for that job, which is why RevenueHero exists. Example: global email collaboration with dozens of languages and brand guardrails needs serious template control, which is why Knak clears roadblocks. Phil speaks to the operator who needs real lift:Match routing logic to the sales org rather than bending the org to the tool.Scale content production with permissions, templates, and translation workflows that teams actually follow.“I have built stacks that blended platform basics with pointed upgrades for specific constraints, and those upgrades paid off when growth demanded it.”Jonathan agrees on the destination, then anchors the sequence. Buy, go live, and prove value within weeks. Add point tools only when a named constraint blocks revenue or customer experience. Keep the stack boring where it should be boring. Run a simple playbook that your team can execute:Stand up your platform baseline and drive daily use from sales and marketing.Write down the first constraint that limits revenue or adoption.Choose one specialist that removes that constraint end to end.Set a 14-day integration target with one success metric tied to pipeline or retention.Move to the next constraint when the metric shows lift.Key ta

What’s up everyone, today we have the pleasure of sitting down with Nadia Davis, VP Marketing at CaliberMind. (00:00) - Intro (01:12) - In This Episode (02:53) - Understanding the Attribution Periodic Table Framework (07:49) - Why Marketing Teams Face Higher ROI Pressure Than Other Departments (20:15) - Why Attribution Fails Without Data Stewardship (33:02) - Treating Multi-Touch Attribution as an Analytical Tool (39:05) - Exploring Chain Based Attribution Models for B2B Marketers (46:31) - Why Customizing Markov Chain Attribution Improves Accuracy (50:56) - How to Decide When Attribution Data Is Good Enough to Guide Strategy (01:00:00) - Why Marketing Operations Defines Multi Touch Attribution Success (01:04:50) - Why Time Management Drives Career Fulfillment Summary: Nadia learned early that attribution keeps you in business, proving to executives why the budget, the team, and the work matter. Seeing “attribution is dead” posts, she built her Attribution Periodic Table to show data modeling, measurement rules, and cross-team alignment as one connected system. In B2B, where budgets are treated like investment portfolios, she uses multi-touch attribution to connect brand and demand to revenue in CFO terms. For her, it’s an analytics tool, not a scoreboard, shaped by sequences like her govtech playbook where event conversations plus on-demand webinars moved deals forward. Chain-based and Markov models help her cut noise, drop vanity metrics, and ground decisions in logged, meaningful touches, all anchored in strong marketing operations that make multi-touch attribution something teams actually trust.About NadiaNadia Davis is the VP of Marketing at CaliberMind, where she leads demand generation, ABM, and marketing operations. She is known for building teams from scratch, overhauling martech stacks, and creating data-driven programs that sales teams can act on immediately. With over 15 years in B2B marketing, she has worked across SaaS, IT automation, healthcare tech, and data platforms, consistently delivering measurable growth by aligning marketing execution with revenue goals.Her career includes senior roles at PayIt, Stonebranch, LexisNexis Risk Solutions, Informa, and ND Medica Inc., as well as nearly a decade as an ABM and digital strategy consultant. She has led global campaigns, designed persona-driven targeting, run high-profile industry events, and built marketing programs that continue to deliver pipeline well beyond launch. A former Girls in Tech board member, Nadia combines hands-on technical expertise with the leadership skills to grow both teams and results.The Periodic Table of Marketing Attribution ElementsNadia has worked in revenue marketing long enough to know attribution is a survival tool. In every demand generation and performance role, she carried it like part of her standard kit. It was how she justified headcount, protected budgets, and kept the lights on in her department. Attribution helped her prove progress in a language executives understood.When she took over marketing at CaliberMind, she noticed the volume of “attribution is dead” posts climbing in her feed. The pattern felt familiar. Marketing tactics often get declared obsolete the moment they fail for someone, then replaced with whatever is trending. From her perspective, most of those posts came from SMB marketers moving on after a bad run. Meanwhile, enterprise teams were applying attribution with discipline, pairing it with strong data modeling, and getting measurable results. They simply were not talking about it publicly.That split in sentiment drove her to dig deeper. She wanted to measure the gap between what people were saying and what they were actually doing. The outcome was the State of 2025 Attribution report, anchored by her Revenue Marketing Periodic Table. Nadia built it to show attribution as part of an integrated framework, not a lone tactic. She broke it down into interconnected components:Data modeling that improves accuracy and removes noiseMeasurement frameworks that define terms and keep reporting consistentCross-functional alignment that ensures teams interpret the data the same way"So many things may seem completely disconnected, yet they all come together within a bigger ecosystem."The iceberg metaphor stuck with her. Most marketers focus on the visible metrics, but the real forces driving success are below the surface. Choosing the periodic table format brought this idea into focus. It showed each element as part of a larger system, each with its own role and complexity. Nadia even remembered struggling with chemistry in school, to the point where she once cheated on a test because she could not memorize the valency of certain elements. That frustration helped her appreciate the value of a clear visual framework when dealing with something complicated. The periodic table worked because it grouped related elements, revealed their relationships, and made the whole system easier to navigate.Key takeawa

What’s up everyone, today we have the pleasure of sitting down with Kevin White, Head of GTM Strategy at Common Room. (00:00) - Intro (01:00) - In This Episode (02:59) - How to Design a Super IC Role for Senior Marketers (09:11) - How to Get Comfortable With Public Visibility as an Introverted Leader (10:39) - sing Empathy and Product Demos to Build Authentic GTM Strategies (16:52) - How to Use Pain Points to Make Personalization Work (19:21) - How to Use Buyer Behavior Signals to Improve Outreach Timing (21:36) - Leveraging GitHub Signals to Drive High-Conversion Micro Campaigns (24:57) - Smarter Account Prioritization With Buyer Signals (29:02) - Why Messaging Drives GTM More Than Signals and Plays (31:16) - Why Overengineered Tech Stacks Fail GTM Teams (35:05) - Why AI SDR Agents Need Structured Coaching to Work (41:43) - Why The Last Mile Of AI Marketing Still Belongs To Humans (43:57) - AI Sharpens the Divide Between Experts and Amateurs (45:46) - Why Declaring Human-Written Outreach Gets Better Responses (48:00) - Futureproofing Operations Skills Through Challenge Driven Learning (51:46) - Why Data Warehouses Are Taking Over Customer Data Platforms (55:32) - Finding Career Balance Through Self Reflection Summary: Kevin rebuilt his career around the work that fuels him. After years leading teams at Segment, Retool and Common Room, he walked away from politics and board decks to create a “super IC” role focused on experiments, product evangelism, and hands‑on growth. He applies that same mindset to go‑to‑market: strip out the bloat, ditch templated outreach, and use real buyer behavior to build small, personal campaigns. He treats AI as an amplifier for skilled marketers, using it to speed research and sharpen ideas, while relying on human judgment to make the output work. Even visibility, once draining for him, became a muscle he trained through repetition. Kevin’s story is a guide for marketers who want less political fluff, more impact, and roles built around the work they actually love to do.About KevinKevin White is a seasoned go-to-market leader with over 20 years of experience driving growth for high-growth SaaS companies. He’s held senior roles at Gigya, SingleStore, HackerOne, and Twilio Segment, where he built demand generation engines and scaled marketing operations during critical growth stages.Most recently, Kevin led marketing at Retool and advanced through multiple leadership roles at Common Room, from Head of Demand Generation to Head of Marketing, and now Head of GTM Strategy. He has also advised innovative startups like Ashby, Gretel.ai, and Deepnote, helping them refine their go-to-market strategies and accelerate adoption.How to Design a Super IC Role for Senior MarketersClimbing the marketing ladder feels like progress until you realize the work at the top is entirely different. Kevin spent years running teams at Retool and Common Room. He managed a dozen people, dealt with SDR team politics, prepared board updates, and handled internal marketing. Those tasks ate up his time and dulled his energy for the work that made him great in the first place. “My day-to-day was full of things I didn’t enjoy. One-on-ones, internal marketing, SDR team drama, board updates. None of it felt like what I wanted to be doing,” he said.Kevin thrived in the early-stage chaos. He loved being the first marketer, building programs from scratch, experimenting with growth channels, and connecting directly with customers. Those environments let him create instead of coordinate. He could see the direct impact of his work and feel close to the product. As companies grew, that hands-on work disappeared. He became a coach, a manager, and a political operator. For someone who values doing over directing, that was a poor fit.He worked with Common Room’s CEO to design a role that put him back in his zone. Now, as Head of GTM Strategy, Kevin functions as a “super IC.” He runs high-leverage growth experiments, drives product evangelism, and collaborates with a few freelancers instead of managing a team. That way he can focus on the work that delivers impact while avoiding the politics and administrative load that drained him. It is a custom role built around his strengths, and it brought back his enthusiasm for the job.Kevin’s thinking extends beyond his role. He shared how Common Room rethought sales development. They hired an excellent manager who knows how to attract and retain elite talent. Then they paid those top performers well above the market rate. “Harry is one of our SDRs,” Kevin explained. “We pay him a good amount because he produces outsized results. That playbook works.” In Kevin’s view, companies should build alternative tracks for individual contributors and reward them based on their production, not their willingness to manage people.Key takeaway: Create roles that match strengths instead of forcing people up a management ladder. Build paths for senior individual contributors who can deliver massive

What’s up everyone, today we have the pleasure of sitting down with Simon Lejeune, VP of Growth at Wealthsimple. (00:00) - Intro (01:16) - In This Episode (03:55) - How to Escape Local Maximum Traps in Growth Marketing (08:59) - Productive Laziness Mindsets (12:03) - The Psychological Trap of A/B Testing (15:55) - Balancing Clean Experiments with Bold Bets (18:43) - How to Use Incrementality to Measure Real Campaign Impact (22:32) - How to Approach Incrementality Without Large Data Sets (25:13) - The Best Use Cases for Incrementality Tests (29:58) - How to Handle ROI Conversations Without Slowing Down Growth (38:02) - Why Most A/B Testing Is a Waste of Time (47:17) - When Natural Language Becomes the Interface, Channel Expertise Stops Being a Moat (01:03:31) - How to Use Game Thinking to Stay Energized in Growth Roles Summary: Simon Lejeune learned early that chasing small wins keeps growth teams stuck, a lesson that landed hard when Hopper’s CEO dismissed his price‑point test as a “local maximum” and pushed him toward ideas bold enough to reshape the business. That experience drives how he leads at Wealthsimple, where he tells teams to stop polishing the same hill and start climbing new mountains by deleting work that doesn’t matter, cutting projects when the lift is negligible, and measuring true incrementality with one simple question: “What would have happened if we didn’t do this?” He believes AI is accelerating this shift, turning deep channel expertise into a commodity and making curiosity, speed, and ruthless prioritization the real competitive advantages. Growth, in his view, belongs to teams who can abandon the comfort of optimization and pursue experiments big enough to change the trajectory.About SimonSimon Lejeune is a seasoned growth leader with over a decade of experience scaling some of North America’s most recognized tech brands. Currently VP of Growth at Wealthsimple, he drives client and asset growth across products like Trade, Crypto, Cash, Invest, and Tax. Before that, Simon founded Mile End Growth, a boutique agency delivering strategy, creative, and media buying for startups, and led user acquisition at Hopper, where he managed multimillion‑dollar budgets and built one of the most sophisticated in‑house ad automation engines in travel tech. His career began at Busbud and Nomad Logic, where he directed growth marketing and developed new revenue‑generating spin‑offs.Local Maximum vs Global MaximumHow to Escape Local Maximum Traps in Growth MarketingA local maximum trap happens when teams keep optimizing small features that look like wins but cap long-term growth. Simon uses the metaphor of being blindfolded on uneven terrain. You walk in every direction until each step feels lower, then assume you have reached the peak. When you take off the blindfold, you see you are standing on a hill while a much larger mountain waits in the distance. Many growth teams spend months, sometimes years, stuck on those hills.Simon experienced this lesson in an uncomfortable way. During his final interview at Hopper, CEO Fred Lalonde asked him what he would change first to grow revenue in the app. Simon answered with what felt like a logical idea. He suggested testing different price points for the $5 tip option, maybe $4 or $6, to find the best revenue point.“He looked at me and said, ‘That’s literally a local maximum, and I do not want you doing that,’” Simon recalled.That feedback forced Simon to change his perspective. He proposed a more radical idea: building a separate app that would use Hopper’s flight data to surface ultra-cheap Ryanair-style deals under five euros. It sounded risky and unconventional, but Lalonde loved it. Simon left that meeting understanding that real growth often comes from bigger, more disruptive ideas that challenge the current model instead of refining it.Growth teams can apply this lesson by actively questioning whether their experiments drive material change or simply polish what already exists. Regularly evaluate whether you are optimizing features, pricing, or flows when the real opportunity may be entirely new product lines, bold pricing experiments, or acquisition channels that look nothing like what you use today.Key takeaway: Incremental optimizations create comfort but rarely drive exponential growth. Audit your current priorities and identify one experiment that pushes far beyond incremental gains. Focus on ideas that reimagine your product, acquisition model, or customer experience. That way you can escape local maximum traps and open paths to growth that small experiments will never reach.Productive Laziness MindsetsSimon challenges his team to delete more work than they refine. “The fastest way to do something is not to do it,” he said. He encourages what he calls “productive laziness,” which means questioning why a task exists before sinking hours into improving it. Many growth teams fill their calendars with recurring meetings and busywork that provide comfort

What’s up folks, today we have the pleasure of sitting down with Alison Albeck Lindland, CMO at Movable Ink.(00:00) - Intro (01:14) - In This Episode (03:10) - 1. Movable Ink's Platform Evolution (04:19) - 2. Alison's 3 Stage Journey at Movable Ink (05:08) - 3. Using Customer Relationships to Future Proof a Marketing Career (09:50) - 4. Building AI Literacy in Marketing Teams (16:17) - 5. How to Spot AI Literacy in Marketing Hires (21:35) - 6. Fostering AI Experimentation Across Your Team (25:43) - 7. AI Point Solutions vs Platforms (30:37) - 8. Align CMOs and Boards on Long Term Marketing Goals (33:37) - 9. How to Measure and Maximize the ROI of Video Podcasts (40:23) - 10. Building a Customer Strategy Team That Drives Enterprise Growth (49:36) - 11. How To Build Lasting Influence With B2B Buyers (55:49) - 12. Creating Energy and Balance as a CMO Summary: Alison believes marketing careers thrive when you stay close to the people who buy from you, and at Movable Ink she has built that into the culture with a customer strategy team, advisory boards, and events that create real connections customers carry into new roles. She applies the same thinking to AI, starting with shared tools and boundaries, then layering in structured experimentation and custom apps that live inside daily workflows. Alison hires people who tinker on their own time, keeps experimentation alive with weekly check‑ins and show‑and‑shares, and cuts projects that do not deliver, like ending a podcast to focus on high‑impact testimonial and “hero” videos. Through it all, she builds influence by aligning teams on one scorecard, sharing loyalty stories that prove long‑term value, and helping buyers see her platform as part of their personal playbook for success.About AlisonAlison is the Chief Marketing Officer at Movable Ink, leading global marketing, brand, strategy, and communications for the AI-powered personalization platform used by the world’s top brands. In her 12+ years at Movable Ink, she’s had three distinct phases: rising through customer success, founding the company’s now-influential strategy team, and stepping into the CMO role nearly three years ago. That journey (across constant evolution and new challenges) has kept the work “never the same company for more than six months at a time,” and helped shape Movable Ink’s role as a leader in enterprise personalization.Customer Relationships Can Future Proof a Marketing CareerAlison argues that the best way to future proof a marketing career is by knowing your customers as actual people rather than abstract data points. Marketers who thrive over time make it their job to understand what customers want, how they think, and why they buy. "You have to know them personally and pretty intimately," she says. "You’ve got to be constantly advocating for their perspective around the table." That kind of understanding does not happen in a spreadsheet. It happens in conversations, often unplanned ones, that give you unfiltered context about their challenges and priorities.She has turned this belief into a repeatable practice at Movable Ink. Her team builds ongoing contact with customers through multiple channels, including:Quarterly fireside chats with CMOs who share their challenges and ideas.A hybrid customer advisory board that rotates in staff members to observe and participate.Strategic placement of marketers at in-person events where they can form real connections.These interactions do more than collect feedback. They create a loop where customer input shapes campaigns, product positioning, and content. Alison credits these relationships with Movable Ink’s staying power. Marketers who use their platform often bring it with them when they change roles or companies, expanding the brand’s reach through personal advocacy."We spend a lot of time now trying to bring our team members in close contact with our customers in more than just a servicing capacity," Alison explains. "They need to develop personal relationships that inform the work they are doing, whether it is content marketing, events, or ABM."Alison also leans on product marketing as a partner in capturing deeper customer knowledge. She highlights win-loss interviews as especially valuable. Unlike survey data, these conversations expose what is working and where gaps exist with enough specificity to guide real change. Her team uses these discussions to refine strategy and make decisions with authority. Marketers who adopt this mindset do more than execute tactics. They become trusted voices in shaping what their company brings to market.Key takeaway: Build constant, meaningful contact with your customers. Use advisory boards, interviews, and live events to hear their unfiltered perspectives. Treat these conversations as fuel for your campaigns and strategies. When you consistently advocate for customers with authority, you position yourself as someone whose work will stay relevant no matter how the tools, titles, or industry trends s

What’s up everyone, today we have the pleasure of sitting down with István Mészáros, Founder and CEO of Mitzu.io. (00:00) - Intro (01:00) - In This Episode (03:39) - How Warehouse Native Analytics Works (06:54) - BI vs Analytics vs Measurement vs Attribution (09:26) - Merging Web and Product Analytics With a Zero-Copy Architecture (14:53) - Feature or New Category? What Warehouse Native Really Means For Marketers (23:23) - How Decoupling Storage and Compute Lowers Analytics Costs (29:11) - How Composable CDPs Work with Lean Data Teams (34:32) - How Seat-Based Pricing Works in Warehouse Native Analytics (40:00) - What a Data Warehouse Does That Your CRM Never Will (42:12) - How AI-Assisted SQL Generation Works Without Breaking Trust (50:55) - How Warehouse Native Analytics Works (52:58) - How To Navigate Founder Burnout While Raising Kids Summary: István built a warehouse-native analytics layer that lets teams define metrics once, query them directly, and skip the messy syncs across five tools trying to guess what “active user” means. Instead of fighting over numbers, teams walk through SQL together, clean up logic, and move faster. One customer dropped their bill from $500K to $1K just by switching to seat-based pricing. István shares how AI helps, but only if you still understand the data underneath. This conversation shows what happens when marketing, product, and data finally work off the same source without second-guessing every report.About IstvánIstvan is the Founder and CEO of Mitzu.io, a warehouse-native product analytics platform built for modern data stacks like Snowflake, Databricks, BigQuery, Redshift, Athena, Postgres, Clickhouse, and Trino. Before launching Mitzu.io in 2023, he spent over a decade leading high-scale data engineering efforts at companies like Shapr3D and Skyscanner. At Shapr3D, he defined the long-term data strategy and built self-serve analytics infrastructure. At Skyscanner, he progressed from building backend systems serving millions of users to leading data engineering and analytics teams. Earlier in his career, he developed real-time diagnostic and control systems for the Large Hadron Collider at CERN. How Warehouse Native Analytics WorksMarketing tools like Mixpanel, Amplitude, and GA4 create their own versions of your customer. Each one captures data slightly differently, labels users in its own format, and forces you to guess how their identity stitching works. The warehouse-native model removes this overhead by putting all customer data into a central location before anything else happens. That means your data warehouse becomes the only source of truth, not just another system to reconcile.István explained the difference in blunt terms. “The data you’re using is owned by you,” he said. That includes behavioral events, transactional logs, support tickets, email interactions, and product usage data. When everything lands in one place first (BigQuery, Redshift, Snowflake, Databricks) you get to define the logic. No more retrofitting vendor tools to work with messy exports or waiting for their UI to catch up with your question.In smaller teams, especially B2C startups, the benefits hit early. Without a shared warehouse, you get five tools trying to guess what an active user means. With a warehouse-native setup, you define that metric once and reuse it everywhere. You can query it in SQL, schedule your campaigns off it, and sync it with downstream tools like Customer.io or Braze. That way you can work faster, align across functions, and stop arguing about whose numbers are right.“You do most of the work in the warehouse for all the things you want to do in marketing,” István said. “That includes measurement, attribution, segmentation, everything starts from that central point.”Centralizing your stack also changes how your data team operates. Instead of reacting to reporting issues or chasing down inconsistent UTM strings, they build shared models the whole org can trust. Marketing ops gets reliable metrics, product teams get context, and leadership gets reports that actually match what customers are doing. Nobody wins when your attribution logic lives in a fragile dashboard that breaks every other week.Key takeaway: Warehouse native analytics gives you full control over customer data by letting you define core metrics once in your warehouse and reuse them everywhere else. That way you can avoid double-counting, reduce tool drift, and build a stable foundation that aligns marketing, product, and data teams. Store first, define once, activate wherever you want.BI vs Analytics vs Measurement vs AttributionBusiness intelligence means static dashboards. Not flexible. Not exploratory. Just there, like laminated truth. István described it as the place where the data expert’s word becomes law. The dashboards are already built, the metrics are already defined, and any changes require a help ticket. BI exists to make sure everyone sees the same numbers, even if nobody knows exactly h

What’s up everyone, today we have the pleasure of sitting down with Tiankai Feng, Data & AI Strategy Director at Thoughtworks and Author of Humanizing Data Strategy. (00:00) - Intro (01:06) - In This Episode (03:18) - How Data and Marketing Create a Symbiotic Relationship (06:00) - If Data Governance Is the Jedi Council, Marketing Ops Is the Rebel Alliance (08:26) - How to Organize Data Teams and Improve Marketing Collaboration (14:49) - Handling Healthy Data Conflicts Without Crushing Creativity (25:23) - How to Use Shadowing to Fix Broken Marketing Alignment (36:44) - The Comeback of Data Quality (43:20) - How Natural Language BI Tools Change Data Analyst Work (46:50) - How Composable Data Management Works in Marketing (53:30) - How to Use Authentic Communication to Build Influence in Marketing Ops (56:40) - Happiness Summary: Data governance feels like the Jedi Council, steady with its rules, while marketing ops moves like the Rebel Alliance, quick to adapt when perfect data never arrives. Tiankai believes progress comes from blending discipline with curiosity, bringing data in early as a partner, not a critic. He’s seen teams thrive when they pick trade-offs upfront, document how everyone fits together, and take ownership of clean, reliable inputs instead of trusting AI to fix sloppy work later. Even the best tools still need humans to design the logic behind the scenes. When teams care about context and build real relationships, data becomes the backbone that keeps marketing strong under pressure.About TiankaiTiankai Feng is Director of Data & AI Strategy at Thoughtworks, where he leads global service offerings spanning data governance, AI strategy, and modernization initiatives. He is the author of Humanizing Data Strategy – Leading Data with the Head and the Heart, and serves on the Education Advisory Board at DataQG. Previously, Tiankai spent over six years at Adidas as Senior Director of Product Data Governance, shaping data practices across global teams. He is also Head of Marketing at DAMA Germany, helping grow the country’s leading data management community. Earlier in his career, Tiankai worked as a senior consultant with TD Reply, advising major brands on digital strategy and performance. Recognized as a top data product thought leader, he is passionate about bridging the gap between technical excellence and human-centered data cultures.How Data and Marketing Create a Symbiotic RelationshipIt is interesting to consider how many data professionals started their careers by obsessing over why advertising can make people feel something. Tiankai shared that he studied campaigns as a kid and felt driven to decode the hidden mechanics behind each message. He called it the science behind the feeling. He wanted to understand why a phrase could trigger a decision and what evidence proved it actually worked.When he chose his degree, he blended marketing with database systems because he believed data could ground creative work in reality. He wanted a way to measure the effectiveness of ideas instead of relying on gut reactions. That decision led him into marketing analytics, where he learned to balance instinct with structured evidence. He described this period as the moment he first saw every click, conversion, and impression as a trail of signals pointing to what people valued most.Tiankai shared that many companies separate marketing from data in ways that weaken both. He believes that every creative idea grows stronger when it gets tested by proof. He said, “You have a lot of thoughts and gut feelings, but what if you could actually rely on proof to make better decisions?” He still asks this question whenever he evaluates a strategy or decides how to communicate the value of a data project.He also applies marketing principles inside his own teams. He treats internal projects like product launches and focuses on storytelling as much as reporting. He learned that evidence alone rarely convinces stakeholders. People respond when data feels relevant and easy to act on. He credits this mindset to his early work in brand campaigns, which taught him that information becomes meaningful when it connects to someone’s goals and emotions.“By heart, I’m still a marketer,” he said. “Even now, I’m applying what I learned in marketing to convince stakeholders to work with me.”This blend of skills helps teams create strategies that people believe in and understand. When marketing and data share the same goals, campaigns feel both credible and inspiring.Key takeaway: Blending marketing analytics with creative thinking lets you challenge assumptions and build strategies that people trust. When you share data work, present it like a product launch. Frame the message in relatable stories, make the numbers clear, and show how the information supports better decisions. That way you can help teams act with confidence and prove the impact of their ideas.If Data Governance Is the Jedi Council, Marketing Ops Is the Rebe

What’s up everyone, today we have the pleasure of sitting down with Guta Tolmasquim, CEO at Purple Metrics. Summary: Brand measurement often feels like a polite performance nobody fully believes, and Guta learned this firsthand moving from performance marketing spreadsheets to startup rebrands that showed clear sales bumps everyone could feel. She kept seeing blind spots, like a bank’s soccer sponsorship that quietly cut churn or old LinkedIn pages driving conversions no one tracked. When she built Purple Metrics, she refused to pretend algorithms could explain everything, designing tools that encourage gradual shifts over sudden upheaval. She watched CMOs massage attribution settings to fit their instincts and knew real progress demanded something braver: smaller experiments, simpler language, and the courage to say, “We tried, we learned,” even when results stung. Her TikTok videos in Portuguese became proof that brand work can pay off fast if you track it honestly. If you’re tired of clean stories masking messy reality, her perspective feels like a breath of fresh air.How Brand Measurement Connects to RevenueBrand measurement drifted away from commercial reality when marketers decided to chase every click and impression. Guta traced this pattern back to the 1970s when companies decided to separate branding and sales into distinct functions. Before that split, teams treated branding as a sales lever that directly supported revenue. The division created two camps that rarely spoke the same language. One camp focused on lavish creative campaigns, and the other became fixated on dashboards filled with shallow metrics.Guta started her career in performance marketing because she valued seeing every dollar accounted for. She described those years as productive but ultimately unsatisfying. She moved to big enterprises and spent nearly a decade trying to make brand lift reports feel credible in boardrooms. She eventually turned her focus to startups and noticed a clearer path. Startups often have budgets that force prioritization. They pick one initiative, implement it, and measure its direct impact on revenue without dozens of overlapping campaigns.“When you only have money to do one thing, it becomes obvious what’s working,” Guta explained. “You almost get this A/B test without even planning for it.”That clarity shaped her view of brand measurement. She learned that disciplined isolation of variables makes results easier to trust. When a startup rebranded, sales moved in a way that confirmed the decision. The data was hard to ignore. Guta saw purchase volumes increase after brand updates, and she knew these signals were stronger than any generic awareness metric. The companies she worked with never relied on sentiment scores alone because they tracked actual transactions.Guta later built her own product to modernize brand research with a sharper focus on financial outcomes. She designed the system to map brand activities to revenue signals so marketing could prove its impact without resorting to vague reports. The product found traction because it respected the mindset of finance leaders and offered direct evidence that branding drives growth. Guta believed this connection was essential for any team that wants to secure resources and build trust across departments.Key takeaway: Brand measurement works best when you focus on one clear change at a time and track its impact on revenue without distractions. You can earn credibility with your finance partners by showing how brand decisions move purchase behavior in measurable ways. When you build discipline into measurement and align it with actual sales, you transform branding from a creative exercise into a proven growth lever.Examples Where Brand Investments Shifted Real Business OutcomesBrand investments often get treated as trophies that decorate a budget presentation. Guta shared a story that showed how sponsorships can drive specific business results when you track them properly. A Brazilian bank decided to sponsor a soccer championship. On the surface, the campaign looked like a glossy PR move. When Guta’s team measured what they called “mindset metrics,” they found that soccer fans reported higher loyalty toward the bank. The data set off a chain reaction that forced everyone involved to reconsider how they viewed sponsorships.The bank pulled internal reports and discovered a clear pattern. Fans who followed the soccer sponsorship churned at much lower rates than other customers. Guta said the marketing team realized they were sitting on a revenue engine they never fully understood. They began to see sponsorship as a serious retention tool rather than a vanity spend. That shift did not happen automatically. Someone had to ask whether the big brand push was connected to any measurable outcomes, and then look carefully for the link between sentiment and behavior.Guta described another client who rebranded their product suite under one name. They planned to de

What’s up everyone, today we have the pleasure of sitting down with Chris O'Neill, CEO at GrowthLoop. Summary: Chris explains how leading marketing teams are deploying swarms of AI agents to automate campaign workflows with speed and precision. By assigning agents to tasks like segmentation, testing, and feedback collection, marketers build fast-moving loops that adapt in real time. Chris also breaks down how reinforcement learning helps avoid a sea of sameness by letting campaigns evolve mid-flight based on live data. To support velocity without sacrificing control, top teams are running red team drills, assigning clear data ownership, and introducing internal AI regulation roles that manage risk while unlocking scale.The 2025 AI and Marketing Performance IndexThe 2025 AI and Marketing Performance Index that GrowthLoop put together is excellent, we’re honored to have gotten our hands on it before it went live and getting to unpack that with Chris in this episode. The report answers timely questions a lot of teams are are wrestling with:Are top performers ahead of the AI curve or just focused on solid foundations? Are top performers focused on speed and quantity or does quality still win in a sea of sameness?We’ve chatted with plenty of folks that are betting on patience and polish. But GrowthLoop’s data shows the opposite.🤖🏃 Top performerming marketing teams are already scaling with AI and their focus on speed is driving growth. For some, this might be a wake-up call. But for others, it’s confirmation and might seem obvious: Teams that are using AI and working fast are growing faster. We all get the why. But the big mystery is the how. So let’s dig into the how teams can implement AI to grow faster and how to prepare marketers and marketing ops folks for the next 5 years.Reframing AI in Marketing Around Outcomes and VelocityMarketing teams love speed. AI vendors promise it. Founders crave it. The problem is most people chasing speed have no idea where they’re going. Chris prefers velocity. Velocity means you are moving fast in a defined direction. That requires clarity. Not hype. Not generic goals. Clarity.AI belongs in your toolkit once you know exactly which metric needs to move. Chris puts it plainly: revenue, lifetime value, or cost. Pick one. Write it down. Then explain how AI helps you get there. Not in vague marketing terms. In business terms. If you cannot describe the outcome in a sentence your CFO would nod at, you are wasting everyone’s time.“Being able to articulate with precision how AI is going to drive and improve your profit and loss statement, that’s where it starts.”Too many teams start with tools. They get caught up in features and launch pilots with no destination. Chris sees this constantly. The projects that actually work begin with a clearly defined business problem. Only after that do they start choosing systems that will accelerate execution. AI helps when it fits into a system that already knows where it’s going.Velocity also forces prioritization. If your AI project can't show directional impact on a core business metric, it does not deserve resources. That way you can protect your time, your budget, and your credibility. Chris doesn’t get excited by experiments. He gets excited when someone shows him how AI will raise net revenue by half a percent this quarter. That’s the work.Key takeaway: Start with a business problem. Choose one outcome: revenue, lifetime value, or cost reduction. Define how AI contributes to that outcome in concrete terms. Use speed only when you know the direction. That way you can build systems that deliver velocity, not chaos.How to Use Agentic AI for Marketing Campaign ExecutionMany marketing teams still rely on AI to summarize campaign data, but stop there. They generate charts, read the output, and then return to the same manual workflows they have used for years. Chris sees this pattern everywhere. Teams label themselves as “data-driven,” while depending on outdated methods like list pulls, rigid segmentation, and one-off blasts that treat everyone in the same group the same way.Chris calls this “waterfall marketing.” A marketer decides on a goal like improving retention or increasing lifetime value. Then they wait in line for the data team to write SQL, generate lists, and pass it back. That process often takes days or weeks, and the result is usually too narrow or too broad. The entire workflow is slow, disconnected, and full of friction.Teams that are ahead have moved to agent-based execution. These systems no longer depend on one-off requests or isolated tools. AI agents access a shared semantic layer, interpret past outcomes, and suggest actions that align with business goals. These actions include:Identifying the best-fit audience based on past conversionsSuggesting campaign timing and sequencingLaunching experiments automaticallyFeeding all results back into a single data source“You don’t wait in line for a data pull anymore,” Chris said. “Th

What’s up everyone, today we have the pleasure of sitting down with Rajeev Nair, Co-Founder and Chief Product Officer at Lifesight. Summary: Rajeev believes measurement only works when it’s unified or multi-modal, a stack that blends multi-touch attribution, incrementality, media mix modeling and causal AI, each used for the decision it fits. At Lifesight, that means using causal machine learning to surface hidden experiments in messy historical data and designing geo tests that reveal what actually drives lift. Attribution alone can’t tell you what changed outcomes. Rajeev’s team moved past dashboards and built a system that focuses on clarity, not correlation. Attribution handles daily tweaks. MMM guides long-term planning. Experiments validate what’s real. Each tool plays a role, but none can stand alone.About RajeevRajeev Nair is the Co-Founder and Chief Product Officer at Lifesight, where he’s spent the last several years shaping how modern marketers measure impact. Before that, he led product at Moda and served as a business intelligence analyst at Ebizu. He began his career as a technical business analyst at Infosys, building a foundation in data and systems thinking that still drives his work today.Digital Astrology and the Attribution IllusionLifesight started by building traditional attribution tools focused on tracking user journeys and distributing credit across touchpoints using ID graphs. The goal was to help brands understand which interactions influenced conversions. But Rajeev and his team quickly realized that attribution alone didn’t answer the core question their customers kept asking: what actually drove incremental revenue? In response, they shifted gears around 2019, moving toward incrementality testing. They began with exposed versus synthetic control groups, then evolved to more scalable, identity-agnostic methods like geo testing. This pivot marked a fundamental change in their product philosophy; from mapping behavior to measuring causal impact.Rajeeve shares his thoughts on multi-touch attribution and the evolution of the space.The Dilution of The Term AttributionAttribution has been hijacked by tracking. Rajeev points straight at the rot. What used to be a way to understand which actions actually led to a customer buying something has become little more than a digital breadcrumb trail. Marketers keep calling it attribution, but what they're really doing is surveillance. They're collecting events and assigning credit based on who touched what ad and when, even if none of it actually changed the buyer’s mind.The biggest failure here is causality. Rajeev is clear about this. Attribution is supposed to tell you what caused an outcome. Not what appeared next to it. Not what someone happened to click on right before. Actual cause and effect. Instead, we get dashboards full of correlation dressed up as insight. You might see a spike in conversions and assume it was the retargeting campaign, but you’re building castles on sand if you can’t prove causality.Then comes the complexity problem. Today’s marketing stack is a jungle. You have:Paid ads across five different platformsOrganic contentDiscountsSeasonal shiftsPricing changesProduct updatesAll these things impact results, but most attribution models treat them like isolated variables. They don’t ask, “What moved the needle more than it would’ve moved otherwise?” They ask, “Who touched the user last before they bought?” That’s not measurement. That’s astrology for marketers.“Attribution, in today’s marketing context, has just come to mean tracking. The word itself has been diluted.”Multi-touch attribution doesn’t save you either. It distributes credit differently, but it’s still built on flawed data and weak assumptions. If you’re measuring everything and understanding nothing, you’re just spending more money to stay confused. Real marketing optimization requires incrementality analysis, not just a prettier funnel chart.To Measure What Caused a Sale, You Need ExperimentsEven with perfect data, attribution keeps lying. Rajeev learned that the hard way. His team chased the attribution grail by building identity graphs so detailed they could probably tell you what toothpaste a customer used. They stitched together first-party and third-party data, mapped the full user journey, and connected every touchpoint from TikTok to in-store checkout. Then they ran the numbers. What came back wasn’t insight. It was statistical noise.Every marketing team that has sunk months into journey mapping has hit the same wall. At the bottom of the funnel, conversion paths light up like a Christmas tree. Retargeting ads, last-clicked emails, discount codes, they all scream high correlation with purchase. The logic feels airtight until you realize it's just recency bias with a data export. These touchpoints show up because they’re close to conversion. That doesn’t mean they caused it.“Causality is essentially correlation plus bias. Can we somehow manage the bia

What’s up everyone, today we have the pleasure of sitting down with Hope Barrett, Sr Director of Product Management, Martech at SoundCloud. Summary: In twelve weeks, Hope led a full messaging stack rebuild with just three people. They cut 200 legacy campaigns down to what mattered, partnered with MoEngage for execution, and shifted messaging into the product org. Now, SoundCloud ships notifications like features that are part of a core product. Governance is clean, data runs through BigQuery, and audiences sync everywhere. The migration was wild and fast, but incredibly meticulous and the ultimate gain was making the whole system make sense again.About HopeHope Barrett has spent the last two decades building the machinery that makes modern marketing work, long before most companies even had names for the roles she was defining. As Senior Director of Product Management for Martech at SoundCloud, she leads the overhaul of their martech stack, making every tool in the chain pull its weight toward growth. She directs both the performance marketing and marketing analytics teams, ensuring the data is not just collected but used with precision to attract fans and artists at the right cost.Before SoundCloud, she spent over six years at CNN scaling their newsletter program into a real asset, not just a vanity list. She laid the groundwork for data governance, built SEO strategies that actually stuck, and made sure editorial, ad sales, and business development all had the same map of who their readers were. Her career also includes time in consulting, digital analytics agencies, and leadership roles at companies like AT&T, Patch, and McMaster-Carr. Across all of them, she has combined technical fluency with sharp business instincts.SoundCloud’s Big Messaging Platform Migration and What it Taught Them About Future-Proofing Martech: Diagnosing Broken Martech Starts With Asking Better QuestionsHope stepped into SoundCloud expecting to answer a tactical question: what could replace Nielsen’s multi-touch attribution? That was the assignment. Attribution was being deprecated. Pick something better. What she found was a tangle of infrastructure issues that had very little to do with attribution and everything to do with operational blind spots. Messages were going out, campaigns were triggering, but no one could say how many or to whom with any confidence. The data looked complete until you tried to use it for decision-making.The core problem wasn’t a single tool. It was a decade of deferred maintenance. The customer engagement platform dated back to 2016. It had been implemented when the vendor’s roadmap was still theoretical, so SoundCloud had built their own infrastructure around it. That included external frequency caps, one-off delivery logic, and measurement layers that sat outside the platform. The platform said it sent X messages, but downstream systems had other opinions. Hope quickly saw the pattern: legacy tooling buried under compensatory systems no one wanted to admit existed.That initial audit kicked off a full system teardown. The MMP wasn’t viable anymore. Google Analytics was still on Universal. Even the question that brought her in—how to replace MTA—had no great answer. Every path forward required removing layers of guesswork that had been quietly accepted as normal. It was less about choosing new tools and more about restoring the ability to ask direct questions and get direct answers. How many users received a message? What triggered it? Did we actually measure impact or just guess at attribution?“I came in to answer one question and left rebuilding half the stack. You start with attribution and suddenly you're gut-checking everything else.”Hope had done this before. At CNN, she had run full vendor evaluations, owned platform migrations, and managed post-rollout adoption. She knew what bloated systems looked like. She also knew they never fix themselves. Every extra workaround comes with a quiet cost: more dependencies, more tribal knowledge, more reasons to avoid change. Once the platforms can’t deliver reliable numbers and every fix depends on asking someone who left last year, you’re past the point of iteration. You’re in rebuild territory.Key takeaway: If your team can't trace where a number comes from, the stack isn’t helping you operate. It’s hiding decisions behind legacy duct tape. Fixing that starts with hard questions. Ask what systems your data passes through, which rules live outside the platform, and how long it’s been since anyone challenged the architecture. Clarity doesn’t come from adding more tools. It comes from stripping complexity until the answers make sense again.Why Legacy Messaging Platforms Quietly Break Your Customer ExperienceHope realized SoundCloud’s customer messaging setup was broken the moment she couldn’t get a straight answer to a basic question: how many messages had been sent? The platform could produce a number, but it was useless. Too many things happened after

What’s up everyone, today we have the pleasure of sitting down with Joshua Kanter, Co-Founder & Chief Data & Analytics Officer at ConvertML. Summary: Joshua spent the earliest parts of his career buried in SQL, only to watch companies hand out dashboards and call it strategy. Teams skim charts to confirm hunches while ignoring what the data actually says. He believes access means nothing without translation. You need people who can turn vague business prompts into clear, interpretable answers. He built ConvertML to guide those decisions. GenAI only raises the stakes. Without structure and fluency, it becomes easier to sound confident and still be completely wrong. That risk scales fast.About JoshuaJoshua started in data analytics at First Manhattan Consulting, then co-founded two ventures; Mindswift, focused on marketing experimentation, and Novantas, a consulting firm for financial services. From there, he rose to Associate Principal at McKinsey, where he helped companies make real decisions with messy data and imperfect information. Then he crossed into operating roles, leading marketing at Caesars Entertainment as SVP of Marketing, where budgets were wild.After Caesars, he became a 3-time CMO (basically 4-time); at PetSmart, International Cruise & Excursions, and Encora. Each time walking into a different industry with new problems. He now co-leads ConvertML, where he’s focused on making machine learning and measurement actually usable for the people in the trenches.Data Democratization Is Breaking More Than It’s FixingData democratization has become one of those phrases people repeat without thinking. It shows up in mission statements and vendor decks, pitched like some moral imperative. Give everyone access to data, the story goes, and decision-making will become magically enlightened. But Joshua has seen what actually happens when this ideal collides with reality: chaos, confusion, and a lot of people confidently misreading the same spreadsheet in five different ways.Joshua isn’t your typical out of the weeds CMO, he’s lived in the guts of enterprise data for 25 years. His first job out of college was grinding SQL for 16 hours a day. He’s been inside consulting rooms, behind marketing dashboards, and at the head of data science teams. Over and over, he’s seen the same pattern: leaders throwing raw dashboards at people who have no training in how to interpret them, then wondering why decisions keep going sideways.There are several unspoken assumptions built into the data democratization pitch. People assume the data is clean. That it’s structured in a meaningful way. That it answers the right questions. Most importantly, they assume people can actually read it. Not just glance at a chart and nod along, but dig into the nuance, understand the context, question what’s missing, and resist the temptation to cherry-pick for whatever narrative they already had in mind.“People bring their own hypotheses and they’re just looking for the data to confirm what they already believe.”Joshua has watched this play out inside Fortune 500 boardrooms and small startup teams alike. People interpret the same report with totally different takeaways. Sometimes they miss what’s obvious. Other times they read too far into something that doesn’t mean anything. They rarely stop to ask what data is not present or whether it even makes sense to draw a conclusion at all.Giving everyone access to data is great and all… but only works when people have the skills to use it responsibly. That means more than teaching Excel shortcut keys. It requires real investment in data literacy, mentorship from technical leads, and repeated, structured practice. Otherwise, what you end up with is a very expensive system that quietly fuels bias and bad decisions and just work for the sake of work.Key takeaway: Widespread access to dashboards does not make your company data-informed. People need to know how to interpret what they see, challenge their assumptions, and recognize when data is incomplete or misleading. Before scaling access, invest in skills. Make data literacy a requirement. That way you can prevent costly misreads and costly data-driven decision-making.How Confirmation Bias Corrupts Marketing Decisions at ScaleExecutives love to say they are “data-driven.” What they usually mean is “data-selective.” Joshua has seen the same story on repeat. Someone asks for a report. They already have an answer in mind. They skim the results, cherry-pick what supports their view, and ignore everything else. It is not just sloppy thinking. It’s organizational malpractice that scales fast when left unchecked.To prevent that, someone needs to sit between business questions and raw data. Joshua calls for trained data translators; people who know how to turn vague executive prompts into structured queries. These translators understand the data architecture, the metrics that matter, and the business logic beneath the request. They return with a r

What’s up everyone, today we have the pleasure of sitting down with Samia Syed, Director of Growth Marketing at Dropbox. Summary: Samia Syed treats martech like hiring. If it costs more than a headcount, it needs to prove it belongs. She scopes the problem first, tests tools on real data, and talks to people who’ve lived with them not just vendor reps. Then she tracks usage and outcomes from day one. If adoption stalls or no one owns it, the tool dies. She once watched a high-performing platform get orphaned after a reorg. Great tech doesn’t matter if no one’s accountable for making it work.Don’t Buy the Tool Until You’ve Scoped the JobMartech buying still feels like the Wild West. Companies drop hundreds of thousands of dollars on tools after a single vendor call, while the same teams will debate for weeks over whether to hire a junior coordinator. Samia calls this out plainly. If a piece of software costs more than a person, why wouldn’t it go through the same process as a headcount request?She maps it directly: recruiting rigor should apply to your tech stack. That means running a structured scoping process before you ever look at vendors. In her world, no one gets to pitch software until three things are clear:What operational problem exists right nowWhat opportunities are lost by not fixing itWhat the strategic unlock looks like if you doMost teams skip that. They hear about a product, read a teardown on LinkedIn, and spin up a trial to “explore options.” Then the feature list becomes the job description, and suddenly there’s a contract in legal. At no point did anyone ask whether the team actually needed this, what it was costing them not to have it, or what they were betting on if it worked.Samia doesn’t just talk theory. She has seen this pattern lead to ballooning tech stacks and stale tools that nobody uses six months after procurement. A shiny new platform feels like progress, but if no one scoped the actual need, you’re not moving forward. You’re burying yourself in debt, disguised as innovation.“Every new tool should be treated like a strategic hire. If you wouldn’t greenlight headcount without a business case, don’t greenlight tech without one either.”And it goes deeper. You can’t just build a feature list and call that a justification. Samia breaks it into a tiered case: quantify what you lose without the tool, and quantify what you gain with it. How much time saved? How much revenue unlocked? What functions does it enable that your current stack can’t touch? Get those answers first. That way you can decide like a team investing in long-term outcomes, not like a shopper chasing the next product demo.Key takeaway: Treat every Martech investment like a senior hire. Before you evaluate vendors, run a scoping process that defines the current gap, quantifies what it costs you to leave it open, and identifies what your team can achieve once it’s solved. Build a business case with numbers, not just feature wishlists. If you start by solving real problems, you’ll stop paying for shelfware.Your Martech Stack Is a Mess Because Mops Wasn’t in the Room EarlyMost marketing teams get budget the same way they get unexpected leftovers at a potluck. Something shows up, no one knows where it came from, and now it’s your job to make it work. You get a number handed down from finance. Then you try to retroactively justify it with people, tools, and quarterly goals like you’re reverse-engineering a jigsaw puzzle from the inside out.Samia sees this happen constantly. Teams make decisions reactively because their budget arrived before their strategy. A renewal deadline pops up, someone hears about a new tool at a conference, and suddenly marketing is onboarding something no one asked for. That’s how you end up with shelfware, disconnected workflows, and tech debt dressed up as innovation.This is why she pushes for a different sequence. Start with what you want to achieve. Define the real gaps that exist in your ability to get there. Then use that to build a case for people and platforms. It sounds obvious, but it rarely happens that way. In most orgs, Marketing Ops is left out of the early conversations entirely. They get handed a brief after the budget is locked. Their job becomes execution, not strategy.“If MOPS is treated like a support team, they can’t help you plan. They can only help you scramble.”Samia has seen two patterns when MOPS lacks influence. Sometimes the head of MOPS is technically in the room but lacks the confidence, credibility, or political leverage to speak up. Other times, the org’s workflows never gave them a shot to begin with. Everything is set up as a handoff. Business leaders define targets, finance approves the budget, then someone remembers to loop in the people who actually have to make it all run. That structure guarantees misalignment. If you want a smarter stack, you have to fix how decisions get made.Key takeaway: Build your Martech plan around strategic goals, not leftover budget

What’s up everyone, today we have the pleasure of sitting down with Ankur Kothari, Adtech and Martech Consultant who’s worked with big tech names and finance/consulting firms like Salesforce, JPMorgan and McKinsey.The views and opinions expressed by Ankur in this episode are his own and do not necessarily reflect the official position of his employer.Summary: Ankur explains how most AI personalization flops cause teams ignore the basics. He helped a brand recover millions just by making the customer journey actually make sense, not by faking it with names in emails. It’s all about fixing broken flows first, using real behavior, and keeping things human even when it’s automated. Ankur is super sharp, he shares a practical maturity framework for AI personalization so you can assess where you currently fit and how you get to the next stage. AI Personalization That Actually Increases Retention - Practical ExampleMost AI personalization in marketing is either smoke, mirrors, or spam. People plug in a tool, slap a customer’s first name on a subject line, then act surprised when the retention numbers keep tanking. The tech isn't broken. The execution is lazy. That’s the part people don’t want to admit.Ankur worked with a mid-sized e-commerce brand in the home goods space that was bleeding revenue; $2.3 million a year lost to customers who made one purchase and never returned. Their churn rate sat at 68 percent. Think about that. For every 10 new customers, almost 7 never came back. And they weren’t leaving because the product was bad or overpriced. They were leaving because the whole experience felt like a one-size-fits-all broadcast. No signal, no care, no relevance.So he rewired their personalization from the ground up. No gimmicks. No guesswork. Just structured, behavior-based segmentation using first-party data. They looked at:Website interactionsPurchase historyEmail engagementCustomer service logsThen they fed that data into machine learning models to predict what each customer might actually want to do next. From there, they built 27 personalized customer journeys. Not slides in a strategy deck. Actual, functioning sequences that shaped content delivery across the website, emails, and mobile app.> “Effective AI personalization is only partly about the tech but more about creating genuinely helpful customer experiences that deliver value rather than just pushing products.”The results were wild. Customer retention rose 42 percent. Lifetime value jumped from $127 to $203. Repeat purchase rate grew by 38 percent. Revenue climbed by $3.7 million. ROI hit 7 to 1. One customer who previously spent $45 on a single sustainable item went on to spend more than $600 in the following year after getting dropped into a relevant, well-timed, and non-annoying flow.None of this happened because someone clicked "optimize" in a tool. It happened because someone actually gave a damn about what the customer experience felt like on the other side of the screen. The lesson isn’t that AI personalization works. The lesson is that it only works if you use it to solve real customer problems.Key takeaway: AI personalization moves the needle when you stop using it as a buzzword and start using it to deliver context-aware, behavior-driven customer experiences. Focus on first-party data that shows how customers interact. Then build distinct journeys that respond to actual behavior, not imagined personas. That way you can increase retention, grow customer lifetime value, and stop lighting your acquisition budget on fire.Why AI Personalization Fails Without Fixing Basic Automation FirstSigning up for YouTube ads should have been a clean experience. A quick onboarding, maybe a personalized email congratulating you for launching your first campaign, a relevant tip about optimizing CPV. Instead, the email that landed was generic and mismatched—“Here’s how to get started”—despite the fact the account had already launched its first ad. This kind of sloppiness doesn’t just kill momentum. It exposes a bigger problem: teams chasing personalization before fixing basic logic.Ankur saw this exact issue on a much more expensive stage. A retail bank had sunk $2.3 million into an AI-driven loan recommendation engine. Sophisticated architecture, tons of fanfare. Meanwhile, their onboarding emails were showing up late and recommending products users already had. That oversight translated to $3.7 million in missed annual cross-sell revenue. Not because the AI was bad, but because the foundational workflows were broken.The failure came from three predictable sources:Teams operated in silos. Innovation was off in its own corner, disconnected from marketing ops and customer experience.The tech stack was split in two. Legacy systems handled core functions, but were too brittle to change. AI was layered on top, using modern platforms that didn’t integrate cleanly.Leaders focused on innovation metrics, while no one owned the state of basic automation or email

What’s up everyone, today we have the pleasure of sitting down with Kim Hacker, Head of Business Ops at Arrows. Summary: Tool audits miss the mess. If you’re trying to consolidate without talking to your team, you’re probably breaking workflows that were barely holding together. The best ops folks already know this: they’re in the room early, protecting momentum, not patching broken rollouts. Real adoption spreads through peer trust, not playbooks. And the people thriving right now are the generalists automating small tasks, spotting hidden friction, and connecting dots across sales, CX, and product. If that’s you (or you want it to be) keep reading or hit play.About KimKim started her career in various roles like Design intern and Exhibit designer/consultantShe later became an Account exec at a Marketing AgencyShe then moved over to Sawyer in a Partnerships role and later Customer OnboardingToday Kim is Head of Business Operations at Arrows Most AI Note Takers Just Parrot Back JunkKim didn’t set out to torch 19 AI vendors. She just wanted clarity.Her team at Arrows was shipping new AI features for their digital sales room, which plugs into HubSpot. Before she went all in on messaging, she decided to sanity check the market. What were other sales teams in the HubSpot ecosystem actually *doing* with AI? Over a dozen calls later, the pattern was obvious: everyone was relying on AI note takers to summarize sales calls and push those summaries into the CRM.But no one was talking about the quality. Kim realized if every downstream sales insight starts with the meeting notes, then those notes better be reliable. So she ran her own side-by-side teardown of 22 AI note takers. No configuration. No prompt tuning. Just raw, out-of-the-box usage to simulate what real teams would experience.> “If the notes are garbage, everything you build on top of them is garbage too.”She was looking for three things: accuracy, actionability, and structure. The kind of summaries that help reps do follow-ups, populate deal intelligence, or even just remember the damn call. Out of 22 tools, only *three* passed that bar. The rest ranged from shallow summaries to complete misinterpretations. Some even skipped entire sections of conversations or hallucinated action items that never came up.It’s easy to assume an AI-generated summary is “good enough,” especially if it sounds coherent. But sounding clean is not the same as being useful. Most note takers aren't designed for actual sales workflows. They're just scraping audio for keywords and spitting out templated blurbs. That’s fine for keeping up appearances, but not for decision-making or pipeline accuracy.Key takeaway: Before layering AI on top of your sales stack, audit your core meeting notes. Run a side-by-side test on your current tool, and look for three things: accurate recall, structured formatting, and clear next steps. If your AI notes aren’t helping reps follow up faster or making your CRM smarter, they’re just noise in a different font.Why Most Teams Will Miss the AI Agent Wave EntirelyThe vision is seductive. Sales reps won't write emails. Marketers won’t build workflows. Customer success won’t chase follow-ups. Everyone will just supervise agents that do the work for them. That future sounds polished, automated, and eerily quiet. But most teams are nowhere close. They’re stuck in duplicate records, tool bloat, and a queue of Jira tickets no one’s touching. AI agents might be on the roadmap, but the actual work is still being done by humans fighting chaos with spreadsheets.Kim sees the disconnect every day. AI fatigue isn’t coming from overuse. It’s coming from bad framing. “A lot of people talking about AI are just showing the most complex or viral workflows,” she explains. “That stuff makes regular folks feel behind.” People see demos built for likes, not for legacy systems, and it creates a false sense that they’re supposed to be automating their entire job by next quarter.> “You can’t rely on your ops team to AI-ify the company on their own. Everyone needs a baseline.”Most reps haven’t written a good prompt, let alone tried chaining tools together. You can’t go from zero to agent management without a middle step. That middle step is building a culture of experimentation. Start with small, daily use cases. Help people understand how to prompt, what clean AI output looks like, and how to tell when the tool is lying. Get the entire org to that baseline, then layer on tools like Zapier Agents or Relay App to handle the next tier of automation.Skipping the basics guarantees failure later. Flashy agents look great in demos, but they don’t compensate for unclear processes or teams that don’t trust automation. If the goal is to future-proof your workflows, the work starts with people, not tools.Key takeaway: If your team isn't fluent in basic AI usage, agent-powered workflows are a pipe dream. Build a shared baseline across departments by teaching prompt writing, validating

What’s up everyone, today we have the pleasure of sitting down with Keith Jones, Head of GTM Systems at OpenAI. Also just a quick disclaimer that Keith is joining the podcast as Keith the technologist and human, not the employee at OpenAI. The views and opinions he expresses in this episode are his own and do not represent OpenAI.Summary: The best martech buying process isn’t a spreadsheet. It’s a cognitive extraction exercise.Keith Jones asks stakeholders to write what they want, say it out loud, and then feeds both into GPT to surface what actually matters. That discipline applies to agents too. Most teams chase orchestration before they have stable logic, clean data, or working workflows. Keith’s bet? The future of SaaS is fewer tools, built in-house, coordinated by agents not a graveyard of dashboards pretending to be automation.Why Sales Ops People Who’ve Actually Sold Have the Sharpest KnivesKeith Jones did not set out to work in sales operations earlier in his career. He landed in it sideways, like a lot of the best people in ops do. He was hired with the catch-all title of “Business Operations Associate,” which could mean anything or nothing, depending on the day. His job, in practice, involved forecasting bookings and revenue in Excel based on shipping data. No one told him he was in sales ops. No one even used that phrase. If someone had asked him whether he wanted a career in sales operations, he wouldn’t have known what they meant.The company later shifted him into a field sales role. They were trying to grow the team internally, so they dropped him into the southeast region and told him to start talking to CIOs and chief nursing officers. He moved to Atlanta and started selling. That job was hard in a way that most people who build systems for sales teams never understand. The structure was just enough to keep things moving, but not enough to support real learning. He had a quota, a few tools, and a manager who held weekly one-on-ones. There was no real training. No consistent coaching. No safety net. If he wanted to make it work, he had to figure it out himself.That experience never left him. Now that Keith leads systems for go-to-market teams, he still thinks about what it felt like to sit in a seller’s chair. Every tool that didn’t work, every field in Salesforce that meant nothing, every process that made his job harder stuck with him. He builds differently because of that.> “You’re given a quota, a few tools, some vague expectations, and then shoved into the wild.”The biggest disconnect he sees in GTM systems comes from people who have never sold anything. Many of the systems designed to help sales teams are built by career admins or operations specialists who’ve never had to ask for a purchase order or explain why a deal fell through. These people often optimize for what the business wants, not for what the seller needs to survive the quarter. Keith doesn’t speak about this in abstract terms. He lived through it.After his healthcare role, he joined a startup in Atlanta as employee number eight. He came in as an account executive, but quickly became the go-to person for explaining the product. He wasn’t the most technical person, but he could speak the language. That mattered. As the company grew and new reps joined, Keith found himself teaching them how to explain the product to customers. He was still selling, but he was also building shared knowledge. That part felt natural.Then his CEO pulled him into a room and told him something blunt. “You’re really bad at cold calling. You don’t even do it.” Keith agreed. He hated that part of the job. As an introvert, it never felt right. But the CEO followed up with something more important. “You know the product better than anyone else on the floor. I think you should be our first sales engineer.” Keith said yes immediately.There was one more thing. The Salesforce admin had just quit, and the CEO asked if he wanted to learn Salesforce too. Keith said yes to that as well. That moment when he stepped into a role that combined technical depth with operational design set the course for everything that came next.Today, he leads systems at a scale that touches thousands of sellers. He remembers what it felt like to sell without support, and he refuses to push that experience onto others. He builds tools that actually work because he knows what failure feels like.Key takeaway: Sales ops works best when it is built by people who have actually sold. If you want to build tools that sellers will use, you need someone who has lived with the friction of broken ones. Sellers do not care about elegant reporting architecture if the CRM slows them down. They care about speed, clarity, and context. Hiring operators who have carried a quota gives you an unfair advantage. They remember how it felt to lose time chasing bad leads or cleaning up messy data. That memory turns into better workflows. You can teach someone how to configure Salesforce. You cannot teach