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

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.

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## How Code-Driven AI Workflows Outperform Chat-Based Prompting

Most 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.

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## How to Start Building With Claude Code When You Have No Time

The 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 (Claude Code in terminal) and they conclude the gap is too wide. Step 2 exists. And step 2 is where everything clicks.

Anthropic's rollout is layered deliberately. Chat first: ask a question, read the answer, copy the output. Cowork space second: Claude works inside a folder on your computer, local or cloud-based, and you're giving it real files to act on. Coding interface third: terminal, commands, agents. The cowork space is a distinct step with its own payoff. It's where the model stops being a question-answering machine and becomes an environment you work inside.

> "Once people understand that Claude lives in a folder on your computer and you can throw stuff in that folder and have it work for you — that's the next step."

When you upload documents inside a Claude project and ask it to work on them, you learn something you can't get from chat: Claude lives in a folder. It acts on what's in front of it. That sounds obvious. It does not feel obvious until you've done it. And once you feel it, the jump from cowork to terminal starts feeling like a small step forward rather than a cliff.

Where this leads, eventually, is automation that runs without you. A cron job fires at 6am. A script processes your data. A workflow runs in the cloud while you're on a call or asleep. Austin maps the progression clearly: folder on your machine, then a local cron, then a cloud-deployed process that runs continuously. The people building now are building the muscle memory to get there faster. You don't have to start in the deep end. But you have to start somewhere.

Key takeaway: Start in Claude's cowork space, not the terminal. Upload a folder of documents you already work with regularly — meeting notes, a newsletter draft, recurring reports, templates — and ask Claude to perform a real task on them. That interaction builds the foundational mental model before you write a single line of code.

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## The Programming Concepts Non-Developers Need to Build With Claude Code

Austin has been saying "learn the command line" for a decade. That advice predates AI by years. The reason it matters now is completely different from the reason it mattered then.

The 3 foundations: command line (how computers work), object orientation (how APIs work), one programming language (how the web works). You don't need to master any of them. You need to understand them. Because without that base layer, you can use the tools that exist today, but you can't evaluate what Claude does when it uses them on your behalf.

> "When you have those 3 things, you can teach yourself anything."

That's the real value. When you...

What’s up everyone, today we have the pleasure of sitting down with Dr. John Whalen, Cognitive Scientist, Author, and Founder at Brilliant Experience.

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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

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 It
Synthetic 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 narrow, tech-adjacent demographic slice, was real and valid and well-documented. But training data keeps expanding, and the gap between “Silicon Valley assumption” and “what people in Nairobi or Jakarta actually think” is narrowing in ways that deserve acknowledgment.

Key takeaway: Synthetic user research earns its place not as a replacement for real human data, but as a low-cost, always-available pressure valve for the enormous volume of decisions that currently happen with no research input at all, so before you dismiss it as gimmicky, ask yourself honestly how many of your last ten strategic calls were backed by anything more rigorous than internal consensus.

How Synthetic Users Make Stakeholders Want More Real Human Research
Thos big hairy static research decks have a fundamental limitation that anyone who has sat through a stakeholder presentation already understands. You hand over a slide deck, someone reads it, and then three days later they have five more questions you can’t answer without going back to the field. Brutal feeling.

Interrogating a Live Persona
John argues that synthetic users solve this problem in a surprisingly indirect way: when a stakeholder can keep interrogating a live AI persona, the conversation never closes. They start poking at the model, asking things like “would you like this?” or “why would you feel that way about that?” and somewhere in that process, something shifts. They stop treating research as a report and start treating it as a living, always-on thing.

What John has observed across a half-dozen client engagements is that this interactivity makes leaders ravenous for it. His team positions synthetic user outputs as directional, explicitly not as data, closer to hypothesis generation than validation. But still cray valuable. When a stakeholder gets genuinely excited about a pattern they’re seeing in a synthetic persona, the natural next thought tends to be “if this could actually be true, we need to go test it with real humans.” The synthetic user functions as a preview of the variance you might find in the field, not a substitute for going there.

“Think of this as almost a preview of what you could have with your humans. So you’re being more prepared for what might be to come, what might be the distribution of different responses.”

Instant Reactions
There’s a second use case John describes, about discovering new questions. When a stakeholder first sits down to scope a research project, they often don’t know what they’re actually asking. Spinning up a synthetic user in the room and throwing that rough, half-formed question at it live tends to prod...

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

Tobias 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 Inference

Prediction 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 sampling error.
Execution introduces operational noise.

Each layer adds variability. When teams treat prediction accuracy as the goal, they lose visibility into where uncertainty enters the system. When teams focus on intervention impact, they concentrate measurement on the lever that drives revenue.

Boardrooms already operate in causal language. Incremental ROI is causal. Budget allocation is causal. Executives care about what caused growth, not which segment looked promising in a dashboard. Prediction can inform prioritization. Causal inference determines what to scale.

If you want to move in that direction, adjust your operating model:

Start every initiative with a controllable lever.
Define the action before defining the segment.
Design experiments that isolate the incremental effect of that lever.
Randomized or adaptive allocation both estimate causal lift.
Report impact in revenue, retention, or contribution margin.
Tie every experiment to a business outcome.
Document assumptions and uncertainty.
Build institutional memory around what caused change.

Prediction remains useful. Intervention drives growth. Teams that understand that distinction build systems that learn through action instead of watching the future unfold from the sidelines.

Key takeaway: Anchor your marketing engine in causal experiments. For every predictive score, define the specific action it informs, test that action against a control, and quantify incremental lift tied directly to revenue or retention. Replace segment rankings with lever performance dashboards that show effect size, confidence, and business impact. When every campaign answers the question “What did this intervention cause?” your team shifts from observing trajectories to shaping them.

How to Validate Causal Impact on Customer Lifetime Value

Most teams treat high LTV segments as proof of where to spend. The model ranks customers. The top decile looks profitable. Budget flows upward. Tobi described asking the head of CRM at a billion dollar outdoor brand what he does when a model predicts someone will be high LTV. The answer came instantly: Spend more on them, no?

That instinct feels responsible. It also confuses observation with intervention. Introducing the high LTV Fallacy:

On the right side of the chart, you see a dense cluster labeled high LTV customers. Revenue increases with marketing spend. The correlation line slopes upward. It looks clean and convincing. They were going to buy anyway. That cluster may represent customers with higher income, stronger brand affinit...

What’s up everyone, today we have the pleasure of chatting with Jenna Kellner, VP Marketing at Workleap.

• (00:00) - Intro

About Jenna

Jenna 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 Growth

You 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-together dashboards. She compares the stack to a house. Repairs never end, but neglect guarantees structural damage. Leaders choose whether maintenance becomes routine or deferred risk.

Key takeaway: Treat marketing and sales tech debt as a leadership responsibility, not an ops inconvenience. Schedule deliberate cleanup cycles, secure executive buy-in early, and protect time and budget to rebuild core systems before the drag on revenue, morale, and reporting compounds beyond control.

Prioritizing RevOps Tech Debt Without Perfect ROI Models

Just get buy-in to fix all of our tech debt… myeah… sounds great. Good luck convincing your leadership team who’s off chasing the next AI tool they just read about on LinkedIn. Just assign a dollar figure to it, doesn’t have to be perfect, just guestimate it. Someone is building a report by hopping across eight tools, copying fields, reconciling numbers. You can measure the hours. You can attach a salary. You still miss the real cost.

Jenna takes a different approach. 

She’s not a fan of squeezing every system fix into an artificial ROI model. She focuses on the role RevOps plays in revenue creation. She says it directly:

“The job is to enable sales and marketing to find patterns, to hunt better, to run better campaigns and plays, to drive stronger revenue.”

When RevOps becomes a reporting service desk, capacity shrinks. The team spends its energy on maintenance rather than momentum. The opportunity cost compounds quietly. High leverage work stalls, including:

• Designing sharper segmentation models.
• Identifying conversion bottlenecks across funnel stages.
• Equipping sales with data driven plays that improve win rates.

You feel the drag in slower experiments and reactive decision making. Pipeline velocity flattens. Leadership wonders why growth feels harder than it should.

The urge to quantify every hour saved can trap teams in defensive mode. You start arguing over whether saving ten hours per week justifies a cleanup project. You try to forecast the dollar value of future pattern recognition. That debate rarely captures the structural risk of lagging systems. Jenna frames it as a leadership judgment call grounded in timing and context. If headwinds are rising, if competitors are shipping faster, if your team spends more time patching than building, the signal is strong enough.

She points to industries that invested early in overhauling core systems. Airlines that modernized their tech stack gained operat...

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

About Ronald
Ronald 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 Authority

Marketing 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. Leaders reference your metrics in executive meetings. Your function becomes a stabilizing force inside an environment that often feels chaotic.

Key takeaway: Build influence by formalizing intake, tying every request to a measurable business outcome, and publishing recurring impact reports that leadership can see and understand. Quantified results create credibility; credibility grants the leverage to prioritize work, manage trade offs, and lead cross functional execution with confidence.

2. Stop Waiting for the Org to Define Your Marketing Ops Role

Marketing operations carries the same title across companies, yet the role behaves differently in every environment. Ronald has held eight or nine versions of it, and each one demanded a new definition. Company size shifts the mandate. A B2B motion introduces different data pressures than B2C. A bloated tech stack creates one set of constraints; a lean stack creates another. Add AI pilots, compliance reviews, and executive reporting requests, and the scope expands before anyone formally acknowledges it.

Many practitioners wait for leadership to clarify what marketing ops owns. Ronald sees that waiting period as a risk. Work keeps arriving while clarity lags behind. Campaigns need automation. Sales wants cleaner routing. Finance wants tighter attribution. Legal wants governance. The role absorbs every undefined edge case because marketing ops understands systems. Over time, that pattern produces overextension and fatigue.

“There’s real danger in waiting for clarity from the organization. The work keeps expanding while you wait.”

Ronald describes marketing ops as a fluid operating system. Core modules travel with you, including automation design, data integrity, reporting frameworks, and process governance. Configuration changes with each company’s maturity and business model. Leaders who thrive treat the role as something they architect rather than inherit. They enter a new org and immediately assess four dimensions:

Revenue model and buying motion
Data quality and integration gaps
Tech stack complexity and ownership lines
Organizational expectations of marketing ops

From there, they document a first version of the marketing ops system. That document defines scope, service boundaries, and maturity milestones. They share it early. They revise it publicly. Internal education becomes part of the job. Adjacent teams learn what marketing ops owns and how requests map to a structured framework.

Ronald believes destiny in this function ties directly to definition. When you articulate your operating model, you create predictability. You create tradeoffs....

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

About Maria

Maria 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 Explained

Mobile 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 between ad networks. If you work with apps or monetization platforms, learn how bidding dynamics, SDK choices, and latency affect revenue in production. That understanding helps you evaluate partners faster, ask better technical questions, and make monetization decisions that hold up after launch.

AI Credibility In Ad Tech Sales

AI credibility in programmatic advertising depends on how clearly people describe what the systems actually do. Many sales and marketing conversations drift into abstraction because AI gets framed as something mystical or unknowable. Maria grounds the discussion in operational reality. Machine learning already drives decisions across ad tech, including bidding, ranking, fraud detection, and optimization. Those systems learn from patterns in data and apply them repeatedly at scale, which makes them useful in everyday workflows rather than theoretical debates.

Maria’s confidence comes from repetition and exposure across roles. Before working at Yango Ads, she spent years explaining machine learning in brand protection environments where trust mattered. Clients wanted to know how models learned, where signals came from, and why outputs behaved the way they did. That experience shaped how she talks about AI today. Credibility grows when explanations stay concrete and connected to observable behavior.

“You can build transparency around where the artificial intelligence pulls information from, how it learns patterns, and how it supports the work of an everyday marketer.”

That same philosophy shapes how Maria coaches her business development team. Everyone is expected to understand a shared vocabulary that shows practical fluency. The goal is not academic depth. The goal is conversational confidence around the mechanics that influence outcomes:

Precision and recall explain how models balance accuracy and coverage.
Gradient boosting explains how multiple weak signals combine into stronger predictions.
Feedback loops explain how systems improve over time based on results.

Programmatic advertising gives those concepts a clear home. Programmatic systems coordinate monetization at a scale that direct sales teams cannot match. Large platforms with massive audiences can sell inventory directly. Smaller developers ship many apps and need automated ways to monetize each one without maintaining advertiser relationships. AI-driven auctions price impressions, select creatives, and allocate demand across millions of opportunities every second. That coordination happens continuously and quietly, which makes it easy to underestimate.

Maria pays closest attention to AI applications that operate below the hype line...

What’s up folks, today we have the pleasure of sitting down with Anthony Rotio, Chief Data Strategy Officer at GrowthLoop.

• (00:00) - Intro

About Anthony

Anthony 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 Systems

Anthony’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 capability belonged in the core workflow. Standing on stage at Google Cloud Next marked a clear moment of validation.

That arc reshaped Anthony’s role into Chief Data Strategy Officer. His work now focuses on building systems that learn over time. Faster audience creation leads to shorter feedback loops. Shorter loops improve decision quality. Better decisions compound. The throughline from robotics to marketing holds steady. Systems improve when learning sits at the center of execution.

Key takeaway: Career leverage often comes from carrying one mental model across multiple domains. Anthony applied learning systems thinking from computer science to enterprise marketing, then rebuilt the tooling to match that mindset. You can do the same by identifying where translation slows your work, then designing interfaces that move intent directly into action. When feedback loops tighten, progress accelerates naturally.

Most Marketing Systems Don’t Learn Because They Lack Feedback Loops

Marketing organizations generate enormous amounts of activity, but learning often lags behind execution. Campaigns launch on schedule, dashboards fill with numbers, and post-campaign reviews happen right on time. The pattern repeats month after month with small adjustments and familiar explanations. Over time, teams become highly efficient at producing output while remaining surprisingly weak at retaining knowledge. The system rewards motion, visibility, and short-term lifts, which slowly conditions teams to forget what they learned last quarter.

Anthony connects this behavior to structural pressure inside large organizations. Quarterly reporting cycles dominate priorities, and executive tenures continue to compress. Leaders feel urgency to show impact quickly and publicly. Compounding growth requires early patience and repeated reinforcement, which rarely aligns with board expectations or career incentives. Short time horizons shape long-term behavior, even when everyone agrees that learning should stack over time.

“When you think about compound interest in finance, the early part looks almost linear. People want big bumps now, even if those bumps never build momentum.”

Technology choices deepen the problem. Many companies invested heavily in customer data and built impressive data clouds that capture transactions, events, and engagement in detail. Activation remains slow because teams still rely on handoffs between marketing and data groups. A familiar sequence plays out:

A marketer defines a campaign and requests an audience.
A ticket moves to a data team for interpretation and SQL.
The audience returns weeks later.
The marketer realizes the audience lacks scale for ne...

"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

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 Moving

First 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 Growth

That’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, expectations rose with it, and satisfaction stayed elusive because the standard never settled.

Progress creates a different rhythm. It shows up in motion you can recognize without squinting. Samia pays attention to signals that accumulate instead of reset:

Teams moving forward together rather than cycling through urgency.
People developing judgment and confidence over time.
Personal growth that feels lived-in rather than optimized.
A child learning, changing, and surprising you in ways no metric could predict.

That framing matters because it ties work back to a broader life rather than isolating it. Progress carries meaning when it connects professional effort to personal identity. Samia talks about watching her daughter grow with the same care she gives to her team’s evolution. Growth becomes something you witness and participate in, rather than something you chase or defend. That mindset keeps work from becoming the only place where worth gets measured.

“Anchoring on perfection as your metric for happiness sets you up for unhappiness. Progress is where I find it now.”

Many careers quietly reward polish over development and composure over learning. Progress resists that pressure by valuing direction and continuity. It leaves room for ambition while protecting a sense of self that exists beyond job titles. You still push forward, but you also recognize that your life holds meaning across roles, seasons, and relationships that no performance system can fully capture.

Key takeaway: Track progress instead of perfection. Pay attention to growth across work and life, because meaning comes from seeing yourself develop over time, not from chasing a standard that keeps moving.

Jonathan Kazarian: Tracking Growth Across Life Health and Work

That’s Jonathan Kazarian, Founder & CEO of Accelevents. He’s also father and a frequent sailor.

Jonathan keeps work from consuming his identity by actively measuring progress in more than one place at the same time. He pays attention to movement in business, health, and personal life, and he revisits those signals regularly. That habit creates distance between who he is and what he works on. Work becomes one lane of progress instead of the entire road.

Growth carries real weight in his thinking because it shows up as momentum you can feel. He talks about forward movement as something tangible, the sense that effort today pushes life somewhere better tomorrow. Setbacks still happen, but they do not erase t...

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

Eric Holland: Limiting Slack and Prioritizing Family Time

First 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 Routines

Next 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 when they are top of mind, and she pairs them with clear expectations about response time. People answer when they are working. Evenings stay intact. That clarity removes the quiet pressure that turns collaboration tools into stress machines.

Connection at home runs on small rituals that happen often. Family dinner stays protected. Phones stay off the table. Conversation has shape, which keeps it from drifting back to work. One simple routine anchors the evening.

Each person shares a positive moment from their day.
Each person shares a hard moment.
Everyone gets space to talk without interruption.

“We have a game we play called Popsicle and Poopsicle where each person says a positive thing from their day and a negative thing from their day.”

The table sounds different when everyone is present. You hear voices instead of keyboards. You notice moods. Kids learn that their experiences matter. Adults slow their breathing without realizing it. Work fades because attention has somewhere better to land.

These habits teach through repetition. Kids learn priorities by watching how time is protected. Teams learn boundaries by watching how leaders behave. Meg models presence through behavior rather than explanation. She sits down. She listens. She disconnects. Those signals travel further than any policy ever could.

Career decisions follow the same logic. Meg focused on the life she wanted to live and then shaped work around it. Dinner with her kids mattered. Time away mattered. Flexibility mattered. That perspective runs against an industry that rewards visibility and constant availability. Many people chase recognition and wonder why their days feel thin. Meg invested in connection and built everything else around it.

Key takeaway: Connection grows when time is defended on purpose. Protect shared moments, set expectations clearly, and let daily behavior show people where your attention truly belongs.

David Joosten: Filtering Reactive Work So Time Stays With Family

Next up is David Joosten, Co-Founder and President at GrowthLoop and the co-author of ‘First-Party Data Activation’. He’s also a dad of 3.

Connection shows up here through restraint. David talks about time as something that gets crowded fast, especially once you step into leadership roles where every problem arrives wearing the same urgent expression. Days fill with requests, escalations, and thoughtful edge cases that sound responsible in isolation. Taken together, they quietly displace the people ...

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

Our 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 Stress

Next 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 attention, then presence. A career cannot outrun that erosion for long.

Control defines the structure of his days. Sundar organizes work and life decisions around what he can actively influence and treats everything else with intentional distance. That discipline reduces noise and preserves energy. The system stays practical because complexity invites self-deception.

Work within control receives effort, follow-through, and care.
Work outside control receives acknowledgment and release.
Outcomes matter, but the quality of effort matters more.
Emotional reactions get examined instead of amplified.

That repetition builds resilience as a habit rather than a personality trait. Over time, the body learns that urgency does not improve outcomes, while steadiness often does.

Long-term thinking provides ballast when short-term chaos shows up. Sundar frames happiness the way experienced investors frame capital. Daily decisions compound quietly. Some weeks produce visible setbacks. The trend still moves when investments stay consistent. He invests daily in relationships, energy, and directionally sound choices. Moving his family to Amsterdam followed that logic. The decision carried friction and uncertainty, yet it expanded daily happiness in ways that cautious planning rarely delivers.

“If you keep investing in yourself and the relationships that matter every day, the long-term trend moves up.”

Priorities reinforce the system. Sundar grew up with career dominance baked into identity. Family now anchors that identity with clarity. That hierarchy shapes calendars, boundaries, and energy allocation. Work performance benefits from this structure because focus sharpens when limits exist. Activities that drain energy lose priority quickly. Unhappiness spreads fast and contaminates every adjacent part of life.

Environment completes the infrastructure. Daily systems matter as much as mindset. Living in a place where flexibility exists without negotiation removes friction before it forms. Parenting logistics do not create anxiety. Time away from work does not require justification. Many expat families notice similar relief because daily life carries less ambient pressure. When systems support people, stress loses room to grow.

Key takeaway: Sustainable careers rely on daily infrastructure that prevents stress before it accumulates. Clear control boundaries, long-term thinking, and supportive environments create stability that protects energy and compounds over time.

Elena Hassan: Normalizing Str...