217: How to interview a company before you take the job (The Martech job hunt survival guide, part 3)

Summary: This episode closes Phil and Darrell's 3-part series on the marketing ops job market with the question they've been building toward: what do you ask the company? Darrell shares a firsthand account of taking a job under financial pressure, ignoring red flags he recognized in the moment, and landing in a toxic environment within months. What follows is a structured set of interview questions across 6 categories, from leadership self-awareness to what happened to the last person in the role, designed to help you separate the job offer from the job reality. If the only question you've ever asked at the end of an interview was about growth opportunities, this episode is going to change how you think about that conversation.In This Episode:(00:00) - Intro (01:09) - In This Episode (01:42) - Sponsor: MoEngage (02:40) - Sponsor: Knak (06:06) - What to Figure Out Before You Ask a Single Interview Question (12:19) - How to Test a Hiring Manager's Self-Awareness in a Single Question (18:14) - How to Find Out If a Hiring Manager Can Handle Being Wrong (24:37) - Sponsor: GrowthLoop (25:41) - Sponsor: Mammoth Growth (26:46) - Why "When Did You Last Take a Vacation?" Is the Most Revealing Culture Question (32:09) - How to Find Out If a Company Sticks to Its Priorities or Changes Them Every Quarter (36:31) - How to Find Out What a Marketing Ops Role Actually Requires Before You Accept It (46:04) - Why Fear in a Peer Interview Is the Red Flag You Should Never Ignore What to Figure Out Before You Ask a Single Interview QuestionThe US healthcare system has a way of making bad career decisions feel necessary. When you're laid off with a family depending on employer-sponsored coverage, the clock starts immediately. Every week without an offer is another week closer to COBRA. That pressure doesn't make people irrational. It makes the math of a job offer feel different than it normally would.Darrell Alfonso was in that position last year. A few months after getting laid off, he received what looked like a career comeback: a higher title, more responsibility, better pay, and benefits. The package was attractive enough that he pushed aside doubts surfacing during the process. He knew some things felt off. He took the job anyway. Within 2 months, he was having near-anxiety attacks, sleeping poorly, and barely present with his family. He left quickly. He has no regrets.Most interview prep points in a single direction: getting the offer. Candidates research companies, rehearse answers, and practice looking calm under pressure. The harder question, whether the offer is worth taking, gets almost no airtime. Phil frames this episode as being for people with enough options to ask both. That might mean multiple offers in play, the ability to keep searching while still employed, or simply enough runway to be selective. If you're in survival mode, some of this will still apply. But the questions work best when you have the leverage to actually act on the answers you get.Before choosing which questions to ask, decide what you're trying to find out. Phil and Darrell use what makes you happy at work as the starting filter. For some people it's ownership and interesting problems. For others it's stability, predictable hours, or family-friendly flexibility. Darrell puts the manager relationship at the top. Your boss marks your performance, sets your priorities, and shapes whether it feels safe to admit you're stuck or struggling. Career advice tends to understate how much that single variable determines whether someone thrives or burns out, regardless of how strong everything else looks on paper. The candidates who ask the sharpest questions are usually the ones who did that harder internal work first.Key takeaway: Before your next round of interviews, write down 3 things that would make you miserable in a role. Be specific: not "bad culture" but things like "a boss who overrides my work constantly" or "no flexibility on hours." Use that list as your filter when deciding which questions to prioritize. If a company can't answer those 3 things in a way that gives you confidence, the decision gets harder than it needs to be.How to Test a Hiring Manager's Self-Awareness in a Single QuestionThe most common reason people leave jobs is their manager. That gets cited often but rarely changes how candidates behave in interviews. Most people assess for chemistry from the vibe of the conversation, look for red flags in the standard answers, and hope the hiring manager turns out to be reasonable. Phil uses a more deliberate approach.His bank of questions for probing leadership self-awareness:What's something leadership got wrong in the last year?, What feedback do you get most often as a hiring manager?, What decision would you revisit if you could?, What's changed about how you lead over time?, What's something you're still figuring out about your leadership style?The first 1 does the most work. Every leadership team makes mistakes. If a hiring manager can't name 1, they're either hiding something or genuinely can't reflect on their own decisions. The answer that matters isn't the mistake itself. It's whether they can describe it clearly, explain what they took from it, and say what changed.Darrell pushes the same idea with a different angle: ask what issues a hiring manager has had with a former leader, or with a former direct report. If the answer sounds carefully managed, nothing too specific, nothing too negative, that polish is informative. People who have actually led teams through difficult stretches can name them. They have timelines, outcomes, and lessons. Vague answers suggest either limited experience or a preference for impression management over honesty.Phil's version of the final question in this category is direct: describe your worst boss ever, and why were they the worst? A hiring manager who answers with a real story, including what it cost their team and how they changed as a result, is giving you the most reliable signal available in a 30-minute conversation. Darrell used a version of this in a recent interview. He was upfront with his prospective boss about coming from a toxic environment. She responded by citing 2 specific bosses who had made her professional life difficult, described what each 1 got wrong, and connected it to how she tries to lead now. That answer built more confidence than the rest of the process combined.Leadership self-awareness is a practice developed through confronting moments where instincts were wrong and the team paid for it. The managers worth working for have had those moments and can talk about them specifically. The ones who can't usually haven't processed them.Key takeaway: Ask your next hiring manager: "What's something leadership got wrong in the last year?" Write down the answer verbatim as soon as the conversation ends. If the response is vague, hedged, or completely absent, you now have a data point that no amount of external research could give you. The managers worth working for have made real mistakes and can describe them specifically.How to Find Out If a Hiring Manager Can Handle Being WrongThere's a version of leadership that gets tolerated more than it should: the manager who hires people with deep expertise and then ignores them. The org chart implies delegation. The day-to-day contradicts it. You spend months delivering work that gets overridden by someone who hired you for your judgment and then second-guesses every call you make.Phil's set of questions for this goes directly at the pattern. Rather than asking whether a hiring manager is open to feedback in the abstract, ask for a specific instance: can you describe a time when s...

What’s up everyone, today we continue with part 2 of a 3 part series we’re calling The Martech Job Hunt Survival Guide. Part 2 is: How to stand out as a candidate with AI prep, portfolios and tools.Summary: Phil and Darrell spent this episode breaking down what actually moves the needle when you’re searching for a role: building the portfolio that almost no marketing ops professional bothers to save, navigating the AI experience question, knowing when to take a contract role instead of holding out, and skipping the AI job-search tools that make you look like everyone else. The honest observations from Darrell’s own recent job search make this one worth listening to, including why the colleagues most reluctant to make a lateral move are still searching months later.In this Episode…(00:00) - Intro (01:01) - In This Episode (01:30) - Sponsor: Mammoth Growth (02:36) - Sponsor: GrowthLoop (05:24) - Why Hiring Managers Can't Actually Evaluate Your AI Experience (08:26) - How to Build a Marketing Ops Portfolio When Your Work Is Buried in Tools (17:56) - Why Creating LinkedIn Content Works Even When Nobody Is Watching (25:32) - What Hiring Managers Notice First on Your LinkedIn Profile (30:10) - Sponsor: Knak (31:13) - Sponsor: MoEngage (34:13) - Why Contract Work Is a Strategic Move for Marketing Ops Job Seekers Right Now (44:02) - Which Job Search Tools Help and Which Ones Waste Your Time (56:18) - How a Video Introduction or Visual Resume Gets You Into the Next Round Why Hiring Managers Can't Actually Evaluate Your AI ExperienceEvery marketing ops job posting in 2026 has the same line buried somewhere in the requirements: "proven experience delivering results with AI." Walk into any interview and within the first few minutes someone will ask you to describe what you've actually done with it. That question sounds reasonable until you realize the person asking usually has no idea what a good answer looks like.Darrell came out of a recent job search with a clear read on this. The interview questions had shifted entirely. The old MarTech interview, the 1 that asks about your tool stack and campaign history, has been replaced. AI is now the primary filter. Companies want proof of results. But AI-driven marketing ops, as an actual practice, barely existed 3 years ago. Phil put the absurdity into 4 words: "5 years of AI experience." Everyone in hiring knows it's a joke. They're writing it anyway.The talent pool has gotten harder at the same time. Amazon's most recent layoffs displaced over 10,000 people. Layoffs at Google and across the broader tech sector added more. You're competing against that cohort now, which means the undifferentiated application is in worse shape than it's ever been. Everything has to be sharper.But the opening Darrell is pointing at is real. The hiring managers writing "proven AI experience required" often can't define what good AI usage looks like for a marketing ops role. They're expressing a priority while lacking any rubric to test it. When they ask the interview question, they're listening for someone who sounds like they know what they're talking about. Most candidates coming through don't. You feel it during prep, that uncomfortable awareness that you don't know exactly what they want from you. The honest truth is they don't either.That gap is yours. Research what AI actually does in marketing ops workflows: lead scoring automation, campaign orchestration, data governance, intent signal processing. Build 1 small example if you have the time. Frame your existing work in terms of where AI would fit and how you'd measure it. Darrell's framing: you can position as a credible AI enthusiast with very little preparation, because the bar inside most marketing orgs is low and most candidates aren't clearing it.The industry required AI fluency before building any way to evaluate it. That's not a problem. For candidates willing to do the homework most skip, it's the whole advantage.Key takeaway: Research 3 specific AI use cases in marketing ops before your next interview: lead scoring automation, campaign workflow agents, and CRM data deduplication are good starting points. Prepare 1 concrete story connecting 1 to work you've done or would do. If you haven't built anything yet, describe the workflow you'd build and how you'd measure its impact. Candidates who speak specifically and confidently about AI applications win these conversations, because they're often the only ones in the room who prepared.How to Build a Marketing Ops Portfolio When Your Work Is Buried in ToolsMost marketing ops professionals have spent years doing meaningful, complex work. They've built lead scoring models, managed platform migrations, architected multi-channel campaign workflows. And if you asked them to show you any of it in an interview, most couldn't. The templates are gone. The diagrams were never made. The results are a rough number someone mentioned once in a meeting.Darrell has sat on the interviewer side of enough conversations to be direct: the portfolio problem in marketing ops is almost universal. Candidates describe their work verbally, and the person asking often can't follow it. There's nothing to point to, nothing to walk through, nothing that makes the experience tangible. In a field full of technical, visual, process-driven work, almost no 1 has anything to show.The bar to stand out is genuinely low. Darrell's starting point: if you've built a custom GPT, a Google Gem, or a basic AI agent using Zapier, that alone puts you ahead of most candidates. It takes about 10 minutes to build 1. It demonstrates something concrete about how you think and work. The same logic applies to documentation that almost no company does well: a clean diagram of your current or former tech stack, before-and-after views of a migration you led, a lead scoring template, a product requirements document for a tool evaluation. These are ordinary outputs of the job. Almost no 1 saves them.Phil's preferred format is the case study. Take a project you led, strip the confidential details, and walk through it as if you were an outside consultant brought in to solve the problem. What was the situation before you arrived? What did you do? What did it look like after? Specific numbers and percentages help, but they're not required. A clean diagram showing a tech stack before and after a migration, or a flow chart of a campaign workflow you built, communicates competence without a single metric. For quantifying impact when the numbers are murky, Darrell's suggestion is to use AI to reverse-engineer the math. If you cut campaign launch time by 20%, work backward through campaigns per quarter, leads generated, and pipeline influenced. You can build an intelligent, defensible estimate, and most candidates don't even try.The format doesn't need to be elaborate. A Google Slides deck linked from your resume, tracked with a Bitly vanity URL so you can see who opens it, is more than enough. The bigger benefit of building a portfolio at all is what it does to your interview prep. Reviewing your own work, articulating outcomes, distilling a project into a problem-action-result narrative means you've already done the thinking before anyone asks the question. Phil's point: the exercise of building the portfolio and the exercise of preparing for interviews are the same exercise.Key takeaway: Start with your most recent project and build 1 case study: the problem you walked into, what you built or changed, and the measurable outcome. Add a tech stack diagram if you don't have 1. Link both as a Google Slides deck from your resume and track opens with a Bitly URL. Even a basic portfolio puts you in ...

What's up everyone, today we kick off part 1 of a 3 part series we’re calling The Martech Job Hunt Survival Guide. Part 1 is: How to find hidden job opportunities.In This Episode:(00:00) - Intro (01:23) - In This Episode (01:59) - Sponsor: Knak (03:06) - Sponsor: MoEngage (04:49) - Why Getting Laid Off Is Always a Business Decision (08:52) - Building Career Security Before You Need It (10:51) - Networking Before You Need Anyone's Help (24:29) - Building Your Dream Company List Before the Job Search Starts (27:04) - Sponsor: Mammoth Growth (28:07) - Sponsor: RevenueHero (29:01) - Why AI Side Projects Give You a Real Edge in Job Interviews (34:50) - The 2026 Martech Job Market Reality Check (42:18) - The Ashby Search Hack and Why Referrals Beat Job Applications (49:58) - Hidden Job Boards and Staffing Firms Most Candidates Ignore (53:52) - Finding Martech Jobs at Stealth Startups Using VC Funding Alerts (55:31) - Why Fast-Growing Martech Agencies Are an Underrated Hiring Path Summary: This episode is a full playbook for martech and marketing ops professionals navigating 1 of the toughest job markets in years. Phil and Darrell cover what to build before you ever need a job: network, dream company lists, freelance income, AI side projects. Then it shifts to the tactical mechanics of finding roles most candidates never see. From the Ashby Google search hack to VC job boards, staffing firm pipelines, and stealth startup cold outreach, the counterintuitive moves are the most useful ones here. If you're currently employed, the early chapters are for you. If you're already searching, skip ahead.Why Getting Laid Off Is Always a Business DecisionBeing laid off in 2025 wasn't rare. It was practically routine. Amazon cut thousands. Friends pinged Darrell with news of their own layoffs while he was still processing his own. He knew what was happening across the industry. He was prepared. And then it happened anyway, and prepared turned out not to mean what he thought it meant.What comes next is something anyone who's been through it will recognize. Identity goes first. The role you've spent years building, the thing that answers "so what do you do?" at every party, just disappears overnight. Then the financial math kicks in. Darrell got 4 months of severance, which is a genuinely good outcome, and it still wasn't as much as it sounded like when he heard the number. But underneath all of it is the worst part: the replay loop. If only you'd been more visible. If only you'd taken a different project. If only you'd made that 1 relationship work. Darrell puts it plainly. Being laid off is a business decision, full stop, regardless of your performance, your visibility, or who liked you. The evidence arrived a few weeks later, when he found out that top performers across his entire organization had been cut, including his direct manager, someone who was by any measure visible, impactful, and doing everything right. When your boss gets laid off, there was nothing you could have done."If you've never been laid off before, you can't help but think it's your fault. The big feeling is: if only I had done something different, if only I was more visible, if only I had taken a different project. And that is just 100% not true. It is all business decisions."Phil's been there. He's been let go in his own career and knows exactly how the severance window tricks you. You have a little runway, so you tell yourself you'll take a month to decompress before getting back into it.That's the trap. The best advice Darrell got came from friends who had already navigated their own layoffs, and it was blunt: don't take a break. His instinct was to take a month off, maybe 2, then ease back in. The people who'd lived it told him something he didn't want to hear: it's going to take exactly that long just to get into pipelines. And while you're recovering, everyone else with the same resume and the same experience is making the same choice. You're competing against thousands of people who were also good at their jobs and also got laid off. Darrell ran full steam ahead instead. He ended up with 2 offers.How quickly you start matters more than how long you prepared. The people who figure that out in the first 48 hours have a real structural advantage over everyone else grieving on their couch.Key takeaway: Start your job search the week you're laid off. Reach out to friends who've been through it, get your materials ready, and get into pipelines immediately. Everyone else is planning to take a few weeks off first, and that gap is your only real competitive edge right now.--Building Career Security Before You Need ItMost people don't think about their next job until they lose the current 1. Full-time employment gives you a title and a salary, but career security is something you build separately, independent of any employer. You have benefits, recurring income, and a professional identity anchored to a single org chart, and the company can end any of that without notice.Here's how Phil thinks about it: an active network you can activate, something generating income outside your primary job, and a professional reputation that doesn't disappear when the org chart does. The companies that describe themselves as families are also the ones making headcount decisions when the numbers stop working. Your actual family is at home.This leads to 3 strategies Phil argues everyone in martech should be pursuing whether or not they're actively looking. First: nurture your network. Second: follow your dream companies. Third: do something outside your 9 to 5, whether freelancing, a side project, or anything with even a small amount of income attached. These aren't strategies for when you're in trouble. They're strategies for ensuring you never are. Roles in martech and marketing ops are among the first cut when companies reduce overhead. Operators who treat their current employment as permanent are more exposed than they realize."I've always been a fan of this stoic concept called the pre-mortem. In good times, imagine the worst-case scenario and work out what you'd do. I had always thought: what would happen if I lost my job? I knew I had a big network. So I wasn't as worried. But for many people, networking isn't something you do regularly."Key takeaway: Treat career security as a separate goal from job security. Map 3 building blocks: an active network, at least 1 income source outside your employer, and a professional identity that exists independent of your title. Start with whichever is weakest right now.--Networking Before You Need Anyone's HelpThe most common networking advice is to reach out when you're looking for work. Darrell's approach is the opposite: build and maintain relationships constantly, so the network already exists before you ever need it."I'm always asking for help. If you're figuring out how to integrate a tool, if you're figuring out why your database is so messed up, how are you not reaching out to people in a similar job and saying, hey, what are you doing? Because that, to me, is just a waste of time not to do that."His entry point is the Stoic concept of the pre-mortem. In good times, imagine the worst case scenario and work out what you'd do. For Darrell, that meant regularly asking himself what would happen if he lost his job tomorrow. Because he'd been building across martech and marketing ops communities for years before his layoff, the answer was already in place. He acknowledges most people don't approach it this way.What his version of ongoing networking looks like is less grand than the concep...

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 (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.---## The Programming Concepts Non-Developers Need to Build With Claude CodeAustin 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.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 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 ResearchThos 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 PersonaJohn 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 ReactionsThere’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 produce a response the stakehold...

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 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 ValueMost 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 (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-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 ModelsJust 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 (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. 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 RoleMarketing 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 motionData quality and integration gapsTech stack complexity and ownership linesOrganizational expectations of marketing opsFrom 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 (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 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 SalesAI 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 (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 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 LoopsMarketing 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...