Skip to content
Sign in
Artwork for Humans of Martech
BusinessMarketingCareers

Humans of Martech

Phil Gamache

Future-proofing the humans behind the tech. Follow Phil Gamache and Darrell Alfonso on their mission to help future-proof the humans behind the tech and have successful careers in the constantly expanding universe of martech.

Play
Open in
  • 33 episodes
  • weekly
  • Avg 1 hr 1 min
  • English

Episodes 33

NewestOldestLongest
All20Unplayed20In progress0Played0
Counted on this page — what you have heard stays on this device, so it is not something the list can be paged by.
  • Tuesday · 1 hr 6 min

    245: Single source of truth is becoming pointless because AI doesn't need one, with Arjun Pillai

    What's up everyone, today we have the pleasure of sitting down with Arjun Pillai, Co-founder and CEO of Docket. (00:00) - Intro (01:25) - In This Episode (06:50) - The 3 Things Every AI Agent Needs to Be Taught (10:30) - Why B2B Contact Forms Convert at Only 1% (16:34) - AI Doesn't Need Clean Enterprise Data (23:11) - What a Buyer Context Graph Understands That Your CRM Misses (28:22) - Why RevOps Doesn't Need One System of Record for AI (40:37) - Why Real-Time AI Agents Need a Different Data Architecture (46:05) - How to Get AI Agents to Stop Asking You the Same Question (53:19) - Why a Buyer-Facing Agent Needs a Different Safety Bar (58:18) - What Agentic Marketing Actually Means for Marketers (01:03:32) - How This Founder Defines Success Without Regret Summary: Arjun Pillai has built and sold 2 companies before landing on the biggest one yet: fixing the 1% fill rate on every B2B Contact Us form with a buyer-facing AI agent. In this episode he breaks down why the model powering an agent barely matters anymore, why messy enterprise data isn't the blocker everyone thinks it is, and why he believes marketers need to build an entire factory of agents instead of doing the marketing themselves. He also drops a genuinely useful trick for getting Claude and Codex to argue with each other until they land on a better answer. Stick around for his take on who actually gets to be the brain of the revenue stack, because it isn't the answer most vendors are selling. About Arjun Pillai Arjun Pillai is the co-founder and CEO of Docket, where he's building buyer-facing AI agents that turn messy revenue stacks into structured buyer context. He previously served as chief data officer at ZoomInfo after selling Insent.ai, the conversational marketing and sales tool he built, to the company. Before that, he built a sales intelligence startup to over 200,000 users and sold it to FullContact. Arjun has spent more than a decade watching sales tech and martech evolve, from early sales intelligence through the ABM wave to today's AI-driven revenue stack. The 3 Things Every AI Agent Needs to Be Taught Every martech homepage says the same thing now. AI powered. Agentic. We have an agent. None of it differentiates anything anymore. You can sit through 2 demos from 2 different vendors and watch them do almost the exact same thing in 15 minutes, right down to the interface. The physical experience that used to separate one product from another is getting commoditized too, folded into the same handful of underlying protocols every vendor is building on. Arjun, co-founder and CEO of Docket, doesn't think that's an accident. Outside of 4 or 5 labs, no company building an agent is training its own model. Everyone is choosing from the same shelf and picking whichever one fits their use case best. Docket uses audio-native, real-time models because its agent talks to buyers live on a website. Other vendors pick differently. But the model itself isn't where the advantage lives. That's Arjun's frame for the whole problem. A high-IQ hire still needs training before that intelligence turns into value. What that person becomes at your company depends on what you teach them and how much context you give them to make good calls. He breaks down what an agent actually needs into 3 layers. Knowledge: the agent needs your product lines, your customers, your objections, your case studies, your testimonials. This is the baseline any new hire would need on day one., Buyer context: who the buyer is, where they're coming from, which account they belong to, whether they're a good fit, and how far along they are in their journey., The control plane: what the agent is and isn't allowed to do. Arjun calls this the harness, and it's different for every agent type. An internal knowledge agent has a different harness than a customer-facing demand agent sitting on your website. None of these 3 layers are static. Arjun's point is that they compound. Feed an agent buyer context over months and it gets sharper at reading a new buyer. Teach the control plane what good and bad look like enough times and it starts making better calls on its own. He compares it to a new hire 2 years in. The person who once needed constant oversight is now the one you'd trust with a promotion, because the judgment accumulated over time. The same compounding shows up in agents, if you build the teaching loop right. Which brings the whole thing back to the moat question. If every vendor has access to the same models, and the conversational layer is commodity infrastructure everybody can license, the only thing left to compete on is what's underneath: how much real buyer context an agent has access to, how well its control plane is tuned, and how long it's had to compound. A slick 15-minute demo can't show you any of that. It's the one part of the pitch a buyer actually has to dig for. Key takeaway: Stop evaluating AI agent vendors on what the demo shows you. Ask each vendor to walk through their agent's 3 layers specifically: what knowledge it's been given, what buyer context it has access to, and what its control plane actually restricts. A vendor who can only describe the conversational experience hasn't shown you the part that's actually hard to build. Why B2B Contact Forms Convert at Only 1% Every B2B campaign you run, no matter the channel, pushes people to the same place. Paid ads land on the website. Webinars link back to the website. Social clicks route to the website. Organic traffic ends up on the website. It's the one hub every marketing dollar eventually funnels into. Arjun has spent more than a decade watching what happens once people get there, and the number he keeps coming back to is brutal. The Contact Us form, the only real way out of a website and into a conversation, hasn't meaningfully changed in 20 years. Neither has its fill rate. Run the math and it gets worse. If 99% of the traffic you paid for is bouncing without converting, then every dollar spent driving people to that page is mostly funding a bounce. Arjun's read on this is that it's the single biggest low-hanging opportunity left in the entire B2B stack. Improve that fill rate by even a fraction of a percent and it moves everything downstream of it. He's seen this problem from more angles than most people get the chance to. He built a sales intelligence startup to over 200,000 users and sold it to FullContact. He then built Insent.ai, a conversational marketing and sales tool, and sold that to ZoomInfo, where he went on to build ZoomInfo Marketing with a team of about 15 people. That path through the last era of sales tech, SaaS, and the ABM wave is what convinced him this particular problem, solved with today's AI, was worth starting a third company over. Why hadn't anyone fixed the Contact Us form sooner? Arjun's answer is that the fix needed a human, and a human on every website conversation was never going to pencil out financially. He compares it to buying a Dyson. Chatbots tried to fill that gap back in 2015 and mostly failed. Arjun remembers them as glorified forms: a widget in the bottom right corner that pops up to ask for your email in case the conversation drops, then goes quiet. It was ad real estate wearing a conversation as a costume. What's changed is that a human-quality experience is finally affordable at scale. An agent with a face, a voice, and text can pull up slides, show a video, run a full product demo, handle routing and calendaring, and write straight back to the CRM. That's the entire job of a salesperson, done for every single visitor instead of the handful a rep could reasonably talk to in a day. Key takeaway: Pull your own Contact Us form fill rate before you eval...

    • Transcript
    • Chapters
  • November 3 · 55 min

    244: How Anthropic thinks about build vs buy in the AI era, with Alexey Komissarouk

    What's up everyone, today we have the pleasure of sitting down with Alexey Komissarouk, Member of the Technical Staff at Anthropic. (00:00) - Intro (01:22) - In This Episode (04:44) - How Engineers Actually End Up Specializing in Martech (08:47) - Security Approval Is the Real Bottleneck at Anthropic (14:34) - Why Anthropic Builds Its Own CDP Instead of Buying One (16:50) - How the Vibe-Code Era Is Rewriting Build Versus Buy Math (22:18) - Who Owns a Vibe-Coded App After Its Builder Leaves (28:25) - Why the Data Warehouse Beats Every Other Part of Your Martech Stack (37:49) - Which Martech Tools Survive Even Though You Could Replace Them (41:59) - How to Avoid Drowning in Shadow Martech Tools (48:07) - What Happens to Marketers When Claude Can Just Do the Work (51:36) - How Alexey Decides What Deserves His Energy Summary: Alexey Komissarouk builds martech infrastructure at the company with the most reason to trust AI to build it for them, and the picture he paints is refreshingly unglamorous. Terraform pull requests, a data warehouse doing more work than any vendor, and a security review process that's genuinely harder than the code itself. He explains why Anthropic still keeps Linear around, why the company builds its own CDP instead of renting one, and why an old thesis about IKEA shelves needed a serious rewrite. He also admits he doesn't actually know who owns half the internal tools people have built inside Anthropic, and he's not alone in that. If you've ever wondered what build versus buy looks like once building costs almost nothing, this is the episode that answers it. About Alexey Komissarouk Alexey Komissarouk leads growth engineering and martech at Anthropic, where he's spent the past year and a half rethinking which tools the company builds versus buys as AI changes the economics of both. He previously led Growth Engineering at MasterClass from 2020 to 2023 and spent four years at Opendoor as an early engineer and later a Growth Engineering manager. Before that, he ran a product engineering consulting practice for companies including Dropbox, Pebble, and Binti, and in 2014 he left San Francisco to co-found Hacker Paradise, a work-and-travel company that has since run trips to more than a dozen locations for over 800 alumni. He graduated from the University of Pennsylvania with a computer science degree, where he started the PennApps hackathon. Why the Best Martech Engineers Never Planned to Specialize Ask most engineers what they want to work on, and martech rarely makes the list. It carries a reputation problem, unglamorous and marketing-adjacent, the kind of work that gets assigned rather than chosen. Alexey built an entire career on being the exception, and the path there had nothing to do with a plan. He grew up in Israel reading Joel Spolsky, came to the US for college, and started a company right out of school that ended in a messy founder breakup. A stretch of consulting followed, along with a bootstrapped travel company, Hacker Paradise. Then came the moment every founder-adjacent engineer eventually hits: watching former classmates land at Google and Facebook while he was, in his own words, futzing around for half a decade. He needed something that paid well and still felt like solving a real problem. That search led him to growth. Martech specifically was never the plan. No engineer, Alexey says, wakes up excited to work on it. It sounds boring until you've felt the pain yourself, and he had. At his own startups, he was the one setting up email automations, and he remembers exactly what it felt like to get pulled off real work because a marketer needed a template changed in code. That irritation is what turned into a career. Opendoor hired him to rebuild their long-term email sequence, a problem he already understood cold from years of iterating on Hacker Paradise's own systems. He ran that function at Opendoor, took it over again at Masterclass, and now runs it a third time at Anthropic. He describes himself as the engineer who never minded doing the work everyone else avoided, and that pattern has built the same career three times over. The engineers who end up owning martech well are rarely the ones who set out to specialize in it. They're the ones who got annoyed enough by a blocked marketer that fixing the problem became more interesting than avoiding it. Key takeaway: Identify the recurring request that annoys your engineering team most, whether it's an email template edit or a landing page tweak, and treat it as a real problem worth solving instead of an interruption to survive. Build the tool that makes that specific annoyance disappear for good. The engineers who end up running martech well are usually the ones who fixed their own irritation first. Security Approval Is the Real Bottleneck at Anthropic Every fast-moving company claims to move fast, right up until security gets involved. At Anthropic, that friction shows up in a specific place: the moment a marketer's vibe-coded app is ready to connect to something real. The upside is a real departure from a typical martech org. Marketers at Anthropic don't file a ticket and wait. They build the tool themselves, and martech's job shifts from building it to making sure it holds up and plays well with everything else. Alexey says that's been the pleasant surprise: instead of a request landing on his desk, a finished app does. Security is the part that moves slower. Anthropic builds technology that's explicitly dual use, and that changes the math on every internal tool. Add an MCP connector to a martech app, and that app becomes a genuine attack surface. If it gets compromised, someone could use it to reach Anthropic itself. Approvals that would have taken a day at a previous job, the kind of thing nobody would have thought twice about, now run through a real review process. Why Anthropic Still Buys Some Vendors Part of that scrutiny comes from who Anthropic sells to. It has enterprise customers in health tech and fintech, which means it inherits a lot of the same compliance obligations as a vendor to them. That context shapes how the team decides what to build versus buy. Security reviews get treated as an afterthought in a lot of engineering cultures, something to route around rather than respect. Anthropic's own product makes that posture harder to justify. When the thing you build is the reason attackers would want in, slowing down to check becomes part of the job rather than an obstacle to it. Key takeaway: Map every internal martech tool that connects to an external service through an API or an MCP connector, and treat each connection as a potential attack vector instead of a convenience. Route anything handling customer or model data through a real security review before it ships, even when building it only took an afternoon. The friction is real, but skipping the review is worse. Why Anthropic Builds Its Own CDP Instead of Buying One Look at almost any martech category right now and you'll see the same thing happening. Vendors that started out solving one problem keep bolting on adjacent features until they all start to look like the same product wearing a different logo. Alexey has a name for it. He speaks well of the category too. He's used Census and Hightouch at past jobs and as a consultant, and says he'd reach for one of those tools again at a growth-stage startup. Anthropic ended up building instead, mostly because so much of what the company needs is custom enough, and it has the headcount and engineering capacity to do it well. The Tool They Replaced: the CDP Asked what Anthropic replaced despite having a perfectly good vendor solution, Alexey doesn't hesitate. An...

    • Transcript
    • Chapters
  • October 27 · 1 hr 1 min

    243: Is Chief of Staff the next big career move for marketing ops? with Lauren Stein

    What's up everyone, today we have the pleasure of sitting down with Lauren Stein, former Chief of Staff at GrowthLoop. Summary: Lauren Stein spent four years living the chief of staff job at GrowthLoop before most companies had a real name for it, and now that title is showing up explicitly in job postings recruiting straight out of marketing ops. This episode covers the scaffolding analogy for why growing companies actually need the role, why seeing the whole chessboard means learning to make decisions with data that's never fully clean, and why product management is becoming a real requirement for chief of staff hires. Lauren also gets into what happens to organizations once AI makes every individual faster but nobody's coordinating the pieces between them. If you've ever felt like your marketing ops seat puts you closer to the executive table than your title suggests, this one maps out exactly how to test that. About Lauren Stein Lauren Stein spent four-plus years at GrowthLoop, rising from HR coordinator to operations lead to chief of staff, working directly with leadership on strategic planning, hiring, and company-wide execution. She built her career at the intersection of people, operations, and execution, managing cross-functional work that touched customers, partners, boards, and internal teams. Before GrowthLoop, she held roles at Contently, AdHawk, and Arkadium, building a foundation in people operations and startup environments. Today she works as an ops lead and head of ecosystem, known across the martech community as a networker, community builder, and startup mentor. Is Marketing Ops Already Preparing You To Be A Chief Of Staff Marketing ops job titles have been in flux for a while now. Marketing engineer, GTM systems owner, RevOps lead, the label keeps shifting depending on who's writing the job description. Underneath all of it sits the same tension: marketing ops people understand the systems, the data, the workflows, and the handoffs better than almost anyone in the building. They often know how the business actually runs better than finance does. And they still get treated like a downstream function, pulled in after the decisions that matter have already been made. Chief of staff roles are now explicitly recruiting from that exact pool. ServiceNow has posted openings looking for marketing ops and RevOps backgrounds. The pitch is straightforward: you already sit close to how revenue actually moves through an organization, so you're a step closer to the room where decisions get made, even if you haven't made one yet. Lauren spent four years watching that requirement shift in real time. The chief of staff hiring bar used to run through a specific pedigree: biz dev, consulting, investment banking. If you didn't have one of those three on your resume, you weren't getting a look. That bar has moved, not because the job got easier, but because the skill set that actually matters changed. A product manager who learned what the customer needed and what a sales team needed builds the same muscle a former banker used to bring, just from a different direction. That shift matters more for marketing ops and RevOps than for almost any other function, because those roles already sit on top of the thing every chief of staff eventually has to master: how the business actually drives revenue. You don't need to have made the strategic calls yet. You need to understand why they get made, and marketing ops people spend their whole job inside that logic. Lauren calls it auditioning for a role before you technically have it. The catch is that understanding the mechanics of revenue and being trusted to make trade-offs about it are two different skills. Marketing ops has the first one by default. The second one is what separates a systems person from an executive, and it's the entire subject of what comes next. Key takeaway: Audit your own role for revenue fluency before assuming you need a title change to get taken seriously. List every place you already have visibility into how decisions get made, who influences them, and what data feeds them, even if you're not the one making the call. That list is your existing case for a broader mandate, and it's stronger than most marketing ops people give themselves credit for. Chief Of Staff Is About Trade-Offs, Not Just Bigger Scope Put a chief of staff job description next to a marketing ops job description and they look almost identical from a distance. Both sit in the connective tissue between departments. Both deal with messy, cross-functional problems that nobody else wants to own. Both carry responsibility with no real authority to back it up. The overlap is real enough that plenty of marketing ops people could probably do a decent chunk of a chief of staff's job tomorrow without changing a single skill. What separates them comes down to what you're allowed to sacrifice, and how comfortable you are sacrificing it, not scope on paper. Marketing ops optimizes a function. Get the systems right and revenue moves, teams get unblocked, the machine hums. But fixing one function can break another without anyone noticing right away. A cleaner attribution model might starve a channel that was working for reasons the dashboard never captured. Better lead scoring might overload a sales team that isn't staffed to handle the new volume. Marketing ops rarely has the full picture to see that trade-off coming, and even when it does, it usually doesn't have the mandate to choose which function gets protected and which one absorbs the hit. A chief of staff's entire job is making that choice on purpose. Lauren describes it as thinking a few steps ahead: focus engineering on product this month, accept that another team falls behind temporarily, because the executive team already knows where the business needs to be in two years and can absorb the short-term cost. That's a different relationship with risk entirely, one where you're accountable for what you didn't fix as much as what you did, not a bigger version of marketing ops. Marketing ops without that context defaults to what Lauren calls the local maxima: optimize what's in front of you because that's the only thing you have visibility into. That's a structural limitation of the seat itself. Key takeaway: Before your next optimization project, map who else touches the outcome you're about to change. Ask marketing ops leaders one question before greenlighting a fix: what does this break somewhere else in the stack, and who owns that consequence? If you can't answer that question, you're optimizing for the local maxima instead of the business. Building Trust With ICs Is The Real Entry Point To Chief Of Staff Ask most people what a chief of staff actually does day to day, and the answer usually lives at the executive level: running OKRs, sitting in leadership meetings, prepping the CEO's board deck. That's real, but the job actually starts on the ground floor, with the individual contributors who have zero interest in a seat at the table and every interest in someone actually listening to them. Lauren's first move in any new chief of staff role has nothing to do with strategy decks. She meets with every single person on the team and asks the same handful of questions: what's important to you, what do you value, what should I focus on, where are the gaps, what shouldn't I do. When she joined GrowthLoop, the company was around fifteen people, so that meant every employee, no exceptions. Some had sharp answers to all five questions. Some had none. She met with them anyway. The logic underneath that ritual is simple. The person in front of a customer all day, or watching a deal fall apart in real time, is usually the first to spot what the business actually needs. If that observation never travels past their manager, the execu...

    • Transcript
  • October 20 · 1 hr 5 min

    242: It's the end of Lifecycle Marketing as we know it and here's what's next, with Anna Gelfand

    What's up everyone, today we have the pleasure of sitting down with Anna Gelfand, Director and Head of Lifecycle & Retention Marketing at Miro. (00:00) - Intro (01:14) - In This episode (04:29) - Why Lifecycle Marketing As We Know It Has No Future (08:53) - How To Codify The Qualitative Side Of Lifecycle Marketing (14:52) - Testing Email Campaigns On Digital Twins Of Your Customers (25:05) - Shipping The Criteria Instead Of The Campaign (34:23) - Why Lifecycle Teams Should Stop Calling Themselves The Email Team (42:32) - Why Your Workflow Should Define Your Martech Tools (49:12) - What Lifecycle Marketers Will Do When Agents Run Campaigns (51:58) - The Skills Lifecycle Marketers Should Double Down On In The AI Era (01:00:33) - How To Decide What Deserves Your Energy At Work (01:02:38) - Fiction Book Recommendations From A Marketing Leader Summary: Anna has a take she'll defend on stage: lifecycle marketing as we know it has no future. She breaks down what codification actually does to the job, why she built digital twins of 100 real customers to test campaigns without ever bothering a live user, and how the smartest lifecycle teams are quietly refusing to be called the email team. She gets into the 20/80 flip between building and thinking, why she'd rather own responsibility than a channel, and the one belief that survived 3 ESP migrations and 7 years at Miro. Stick around for the part where she admits she kinda likes suffering, intellectually speaking. About Anna Gelfand Anna Gelfand is the Director and Head of Lifecycle & Retention Marketing at Miro, based in Amsterdam, where she's spent close to 7 years building and running lifecycle and retention for a global product-led company at real scale. She leads a team responsible for email, in-app, and the wider customer journey, and she's known for treating lifecycle as the connective brain of the organization rather than a single channel. Before Miro she worked in lifecycle and retention at Carrot Quest. She's also comfortable holding a contrarian view worth arguing over: lifecycle marketing as the industry knows it doesn't have a future, and its center of gravity is already moving toward strategy, content, and codified systems that agents run. Why Lifecycle Marketing As We Know It Has No Future Spend enough years in lifecycle marketing and the work starts to rhyme. You build the segments, ship the campaign, wait for the numbers, and a large share of the time the test comes back saying nothing at all. Next quarter you run a version of the same thing with a slightly different subject line. When the uplift arrives, it's a fraction of a percent for months of effort. Anna hit that wall at Miro, and she's careful to point out it started before AI was disrupting everything left and right. She's been there a long time, long enough to watch the product and the market reinvent themselves more than once. By that point you've already picked the obvious wins, then the not-so-obvious ones, and what's left is weeks of work for a result that refuses to tell you anything. That line sounds strange until you've lived it. A clearly negative result kills an idea and hands you a lesson. An inconclusive one burns the same 3 weeks and teaches you nothing, so you're right back where you started with less time on the clock. Anna's take on the plateau is that manual, human-level optimization simply can't reach the relevancy customers deserve anymore. That's especially true at Miro, where the product is an open canvas and users can build almost anything on it. You know your personas and your core use cases, but hand-tuning content one segment at a time won't make an email feel genuinely built for the person opening it. Here's the part that keeps Anna in the craft anyway. Lifecycle is intimate, even when you're sending to millions and thinking in the billions. The human aspect of the communication is the whole point. So the real question she's chasing isn't whether to throw technology at the problem, because everyone will. It's what actually survives once you do. The plateau isn't a Miro problem or a tooling problem. Any mature lifecycle program eventually runs out of manual optimization road, and the teams still treating inconclusive tests as a normal cost of doing business are the ones that get lapped first. Key takeaway: Pull your last 2 quarters of lifecycle experiments and count how many returned a clear win or a clear loss. If most came back inconclusive, stop feeding new tests into the same tired playbook. Redirect that time toward a faster way to kill weak ideas before they eat a full test cycle. How To Codify The Qualitative Side Of Lifecycle Marketing Codification is the word doing the rounds right now, borrowed from Luke Kline's writing on the topic. The idea is simple enough: take the marketing knowledge locked in your head and your docs, make it accessible to AI agents, and let them operate on it directly. It works beautifully for the quantitative half of lifecycle. Propensity models, churn scores, who bought what and when, all of that is already data, so agents eat it easily. The harder question is the other half. How do you codify a gut feel, the sense of what makes a subject line bad, a nudge too pushy at the wrong stage, or a campaign a little too clickbaity? Anna's answer starts with a shrug and a strong opinion at the same time. Codification is inevitable, she says, because lifecycle was always the most technical, most data-informed corner of marketing. Expecting AI to leave that part alone would be strange. So the work becomes feeding agents the right tools and the right context. That tension runs through everything she describes. Anna trusts data and segmentation when the warehouse and the models are in good shape. She does not yet trust a machine to write the message. Her example makes the stakes concrete. Miro is a B2B2C company with a wall of self-serve users on one side and large enterprises on the other. She's always wanted lifecycle communications that feel like a partnership rather than a vendor broadcast, and doing that by hand only scales to the biggest accounts. Codification changes the math. Now an agent can pull a public customer's stated goals from their own reports, cross them with Salesforce and Gong data, and paint the email in that customer's colors with their use cases baked in. The account-based treatment she could only afford for a handful of logos becomes something she can run wide. The catch she keeps circling is judgment. She won't call it control, but she wants a clear moment where a human applies quality and qualitative feedback before anything ships. Part of that is buying back time to actually talk to users again, which lifecycle teams almost never do enough of. The other part is a bet on content. As AI floods every inbox with more messages than anyone can read, the scarce thing stops being the ability to produce and becomes the ability to stand out. The uncomfortable implication for most teams is that codifying the qualitative layer isn't a copywriting problem, it's a context problem. If your brand voice, your stage-by-stage rules, and your customer intelligence live only in senior people's heads, an agent has nothing to operate on and will happily generate confident garbage at scale. Key takeaway: Write down the judgment calls your team currently makes by instinct. Document what a bad subject line looks like, what's too pushy at each stage, and the brand rules you'd never let slip. Feed that to your agents as context, then set one named human checkpoint where quality gets approved before anything sends. Testing Email Campaigns On Digital Twins Of Your Customers Plenty of marketers have built a digital twin of them...

    • Transcript
    • Chapters
  • October 13 · 1 hr 2 min

    241: Rebuilding how marketing works around AI and avoiding random acts of AI, with author Melissa M. Reeves

    What's up everyone, today we have the pleasure of sitting down with Melissa M. Reeve, author of Hyperadaptive: Rewiring to Become an AI-Native Organization and co-founder of the Agile Marketing Alliance. (00:00) - Intro (01:09) - In This Episode (04:33) - What Marketing Looks Like If AI Had Always Existed (08:47) - Why AI Is Easy to Use but Not Easy to Learn (15:34) - Why Marketing Ops Sits Where Friction and Fidelity Break (19:20) - How to Measure Your Own Coordination Tax (29:16) - The 5 Capabilities of an AI Native Organization (35:18) - Why AI Sensing Fails When You Measure the Wrong Things (40:00) - Where Ops Jobs Go When Anyone Can Create Fields (45:42) - What a Response Framework Has That a Dashboard Doesn't (52:24) - Why Marketing Needs a Learning Integration Manager (55:56) - A Human Shift Enabled by Technology (58:08) - Happiness, Satisfaction, and the Act of Noticing Summary: What would your marketing team look like if AI had always existed? Melissa spent 30 years studying how organizations actually change, from the floor of a Japanese truck factory to the front lines of agile marketing, and her answer starts with ripping out an org chart designed in 1911. Along the way she explains why "go play with AI" splits your team in 2, how to put a hard number on the coordination tax eating 60 to 70% of managers' time, and the 5 capabilities that separate AI native companies from companies with AI licenses. Stick around for the McFlurry machine that explains predictive sensing better than any martech deck ever has. About Melissa Reeve Melissa M. Reeve is the author of Hyperadaptive: Rewiring to Become an AI-Native Organization, published by Gene Kim's IT Revolution. She studied the Toyota Production System at Waseda University in Tokyo and learned it for real on the factory floor at Hino Motors, watching workers pull the andon cord that lets any one person stop the whole line. She went on to become the first VP of Marketing at Scaled Agile, the company behind SAFe, and co-founded the Agile Marketing Alliance, where she's been pioneering agile marketing since 2011. Hyperadaptive distills 3 decades of systems thinking into a playbook for rebuilding organizations around AI. What Marketing Looks Like If AI Had Always Existed When factories swapped steam for electricity, almost nothing changed for decades. The buildings kept the same layout steam had forced on them, one big engine driving belts and pulleys through the floor, and the efficiency gains stayed embarrassingly small. The revolution only arrived when somebody asked what a factory would look like if electricity had always existed. That question is the spine of Melissa's book, with AI playing the role of electricity. Most marketing teams today are running a copilot bolted onto the old layout and wondering where the transformation went. Melissa's answer starts a century back. In 1911, Frederick Winslow Taylor invented scientific management, and with it the idea that there's one best way of doing something and that it's management's job to impose that way on everyone below them. Every marketing org chart still runs on it. You start as a junior copywriter, become a senior copywriter, then a director, then the head of content, and at each rung your job is to pass the one best way down the ladder. That structure is so embedded in how marketing works that most of us have never thought to question it. Now drop AI into that world. A system that can deploy an entire campaign at once, text, graphics, and programmatic advertising included, makes the whole ladder look strange. And history tells us exactly what happens to the people on it. Melissa has studied enough automation waves to trust the pattern, from factory floors to the washing machine. Nobody she's talked to wants to go back to beating laundry on rocks. In her AI native marketing org, an AI completes campaigns end to end while more humans babysit the automation. Somebody builds and rebuilds it as models change. Somebody watches for brand drift and data drift, and catches the agent before it ships its own OpenAI-and-Hugging-Face moment, the cheerful "oops, didn't mean for that to go out" post nobody approved. She's realistic about the timeline too. Reimagining the process is the easy part; the org also has to realign job roles, incentives, and the entire structure around them, and those things move slowly because Taylor's ladder is bolted into all of them. The teams that win will be the ones willing to redraw the factory floor, not the ones with the best copilot licenses. Key takeaway: Pick one campaign workflow and redesign it on paper as if AI had always existed, with no approval ladder inherited from the pre-AI org. Then list the new jobs that redesign creates: who builds the automation, who monitors brand and data drift, who rebuilds when the model changes. That list is your team's next set of roles, and drafting it now beats discovering it during a reorg. Why AI Is Easy to Use but Not Easy to Learn November 2022 hit marketing first and fast. Melissa had spent a decade bringing agile software principles into marketing, learned lean the hard way on the floor at Hino, a Japanese truck maker, watching workers pull the andon cord that lets any one person stop the whole production line, and was running the Agile Marketing Alliance when ChatGPT sucked all the oxygen out of the room. She calls that moment the great forcing function. Her first reaction was the same one you probably had: the ground shook a little, and a blog post got written awfully fast. What happened next is a journey map she's watched play out across the whole industry. Year one was polarizing. Some people geeked out and leaned in, the headlines screamed that all the jobs were going away, and the resisters dug in hard; she lost a friendship to a flat "don't bring it up, I'm anti-AI." Melissa herself spent 6 months in what she calls the confusion zone, in mild denial, until her business partner Jim Uhl told her to pay attention because this one was big. Then came the early wins era: enterprises bought licenses, handed them out, and delivered the 4 most dangerous words in corporate AI adoption, go play with AI. Anybody can walk up to a piano and bang on the keys. Becoming a concert pianist takes years of deliberate practice, and the people getting real results out of AI have put in exactly that kind of time learning what these systems can and can't do. That gap is why "go play with AI" felt to Melissa like screaming into the void. Play produces random acts of AI, scattered experiments that never compound. Worse, it produces what she calls the bifurcation problem: a handful of power users pulling away from everybody else, while the everybody-else group waits for support that never comes. By last year the chorus had moved on to agents, agents, agents. But teams still don't know how to get from here to there, and that gap is the reason her book spends most of its pages on the path itself: the systems you need to put in place, the named roles and named responsibilities that actually shift the mindset and the work. Factories weren't rewired overnight, and neither are marketing orgs. The rewiring happens incrementally or it doesn't happen at all. Key takeaway: Replace "go play with AI" with structured support before your team bifurcates into power users and everyone else. Name the roles responsible for AI adoption, give each experiment an owner and a goal, and build a shared place where learnings land so they compound instead of evaporating. Treat AI fluency like piano lessons: scheduled, deliberate, and cumulative, never left to whoever happens to noodle around on the keys. Why Marketing Ops Sits Where Friction and Fidelity Break

    • Transcript
    • Chapters
  • October 6 · 1 hr 5 min

    240: The Fall of lead scoring and MQLs and the rise of Agent Qualified Leads, with Gary Amaral

    What's up everyone, today we have the pleasure of sitting down with Gary Amaral, marketing leader at Docket. (00:00) - Intro (01:15) - In This Episode (04:35) - What Lead Scoring Was Really Hired to Do (12:13) - Why Lead Scoring Models Turn Into Black Boxes (16:04) - What Is an Agent Qualified Lead (24:02) - How Agentic Qualification Goes Beyond AI Scoring (27:48) - What AI Agents Do That Drift Style Chatbots Never Could (34:35) - Why Buyers Tell AI Agents What They Hide From Sales Reps (40:05) - Who Needs an AI Agent on Their Website (42:48) - How to Keep the AQL From Becoming the Next Vanity Metric (50:11) - How to Introduce AQLs Without Breaking Your Forecasting (56:30) - Will AI Agents Replace Intent Data and Marketing Automation (59:41) - Why Work Life Balance Is the Wrong Goal Summary: Gary built lead scoring so ambitious it crashed a Marketo pod and took dozens of companies down with it, then co-founded a scoring company, sold it, and switched sides of the argument. Now he says the whole category rests on guesswork: scores infer what buyers want, while an AI agent can just ask them. He explains why every rep would trade 20 scored leads for one hand-raiser, how Docket's agent Aura lifted engagement 8X by moving into the homepage hero, and why he refuses to be measured on MQLs anywhere he works. Along the way you get a Waymo analogy, a Meat Loaf methodology, and a bet that agents will eventually replace your bloated marketing automation platform. By the end you'll know exactly how to pilot AQLs without giving your CFO a forecasting heart attack. About Gary Amaral Gary Amaral has spent his career on the question lead scoring was invented to answer: which buyers actually deserve a sales rep's time. He chased it at BlackBerry, then at Hootsuite, where the demand center his team built included a scoring system so complex it took down an entire Marketo pod. That week of hand-routing leads became the genesis of Breadcrumbs, the lead scoring company he co-founded and later saw acquired by MadKudu. Today Gary leads marketing at Docket, the AI agent platform behind Aura and the agent qualified lead, where he is making the case that asking buyers what they want beats inferring it from their clicks. What Lead Scoring Was Really Hired to Do Every marketing ops team has a scoring model somewhere in their instance that nobody fully trusts. Points for a webinar, points for a pricing page, a threshold someone picked in a meeting 3 years ago. Strip away the arithmetic and the model was only ever hired to answer one question: who should sales reach out to right now? Gary has been chasing that question since the BlackBerry days. By the time he landed at Hootsuite, lead scoring had grown into something much bigger, a full demand center with scoring at its core. And the system his team built grew so elaborate that it broke more than his own instance. He then spent a week manually sorting and routing leads while Marketo dug itself out. Imagine the tedium: a demand leader hand-triaging spreadsheet rows because the machine built to do it had flattened the neighborhood. That week became the genesis of Breadcrumbs, the scoring company he went on to co-found. His pitch was simple. He describes himself as "just an average guy" without a PhD in statistics, carrying years of experience feeding sales teams quality leads. Recency, frequency, fit, and intent, combined in a way that tells you where to send a lead and which seller should get it. Breadcrumbs set out to bring that sophistication to everyone else. Then the market moved. Gary names 3 headwinds that hit the company: AI-native competitors arrived, and if you squinted you could already see how agents would change qualification entirely, Legacy platforms finally invested, with HubSpot shipping scoring that was still not great but good enough for teams already paying for it, Consolidation swept the category as vendors hunted for efficiencies and expanded offerings That last headwind doubled as an exit, and MadKudu acquired Breadcrumbs. The pattern says something uncomfortable about martech point solutions: when your product's output is a number that feeds another team's queue, you are one platform release away from becoming a feature. Key takeaway: Audit your scoring model against the job it was hired to do. List the last 20 leads it routed to sales and ask a rep which ones deserved the outreach. If the rep's answer and the model's answer disagree on more than a handful, the model is scoring activity, and you should rebuild the weights around closed outcomes instead of page values someone negotiated in a meeting. What Made Advanced Lead Scoring Different From Point Systems The classic model runs on decree. Somebody decides the pricing page is worth 20 points and a blog post is worth 10, usually during a horse-trading session where sales insists that anyone who looks a certain way and clicks a certain thing must be hot. Marketing goes along with it because keeping the peace is cheaper than arguing. The weights encode office politics, and everyone quietly knows it. Advanced scoring, the kind Breadcrumbs built, flipped the direction of authority. Instead of a human declaring what matters, the model watched signals over time, tied them to actual outcomes, and surfaced the combinations of behavior that genuinely preceded a purchase. More data sources feeding the model, and math deciding the weights. Harder to explain, Gary admits, but honest in a way the point system never was. That last part deserves more attention than it got at the time. Because the tool learned from outcomes, you could point it at any outcome you cared about. A model for webinar attendance, another for churn risk, a third for upsell readiness. The same machinery, aimed wherever the business needed a prediction, while most teams were still arguing about whether an ebook download deserved 5 points or 15. Key takeaway: Pick one outcome beyond lead routing and build a scored view of it this quarter. Churn and upsell are the natural candidates because the outcome data already sits in your CRM. Pull 12 months of closed outcomes, find the 3 behaviors that most often preceded them, and hand that shortlist to the team that owns the motion. Why Lead Scoring Models Turn Into Black Boxes If you have ever stared at the stars and flames in Marketo Sales Insight, or an intent dashboard announcing that an account is "surging," and wondered what any of it actually means, you have met the transparency problem. Darrell raised it directly on the show: as a user, he often had no idea what was going on inside these tools and no way to change it. Gary's answer starts with an admission. Breadcrumbs never fully solved this either. His best explanation for why the totems exist comes from riding Waymos in Austin. The first driverless ride freaks you out. By the third, you barely notice, and Gary credits the giant screen in the front of the Jag that renders the cars and pedestrians the vehicle sees. The flames work the same way. They translate a complex system into something a human can glance at and trust. The trouble is that really smart marketers refuse to stop at trust. They want to know what drives the representation, and then they want to override it. So every scoring vendor ends up refereeing between what the algorithm proved from historical data and what the marketer feels should count, and Gary says there is some validity on both sides of that fight. The model only knows where the business has been. A 10-year-old company that trains on all 10 years of data is muddying the waters, because who you sold to in year one barely resembles who you sell to now. And the moment you make a big deci...

    • Transcript
    • Chapters
  • September 29 · 1 hr 3 min

    239: Personalization is a creativity problem not a data problem, with Christina Garnett

    What's up everyone, today we have the pleasure of sitting down with Christina Garnett, Fractional Chief Customer and Communications Officer at Neuemotion. (00:00) - audio-Christina (01:19) - In This Episode (05:37) - Why Your Tech Stack Cannot Fix Siloed Customer Teams (11:10) - Why Getting the Name Right No Longer Counts as Personalization (18:08) - Why A/B Testing Programs Ignore Qualitative Customer Data (24:14) - How Brands Build Core Memories Customers Cannot Buy (29:55) - How B2B Brands Build Core Memories Without a Budget (35:46) - What to Do When Transactional, Behavioral and Feedback Data Disagree (42:12) - Why Spotify Wrapped Works and What Gen AI Took Away (49:17) - Why Personalization Feels Predatory Even When You Have Consent (53:58) - Where Influence Ends and Manipulation Starts in Marketing (58:57) - Why Audiences Are Going Back to the Humanities in the AI Era Summary: Christina spent 5 years teaching math before she ever wrote a subject line, and it shows in how ruthlessly she takes apart the personalization playbook. Getting someone's first name right used to be impressive and now it just proves you didn't get fired, which leaves marketers chasing hooks and tricks while customers quietly learn to read the pattern. She makes the case for core memories a brand cannot buy, walks through what actually happened when Spotify Wrapped got the AI treatment, and introduces earned context, the idea that having someone's data and having the right to use it are 2 completely different things. Along the way she calls CX and brand the same job, refuses to bet her life on NPS, and explains why the B2B move nobody is making costs almost nothing. If you have ever shipped a perfectly segmented campaign that landed with a thud, this one will tell you why. About Christina Garnett Christina Garnett is a fractional Chief Customer and Communications Officer at Neuemotion and the founder and principal at Pocket CCO, where she builds customer trust systems for agencies and SaaS companies. She's the author of Transforming Customer-Brand Relationships, which won the Independent Press Award and the International Impact Book Award and was a Foreword INDIES finalist, and she's currently writing a second book built on the Customer Trust Equation she created. Before going independent she built customer advocacy at HubSpot, growing the HubFans program from 0 to 37,000 members and more than 4,000 active advocates, and she led social and customer strategy at global scale through ICUC. She's a contributing writer at Campaign US, was previously a byline at Adweek, and she started her career as a teacher. Why Your Tech Stack Cannot Fix Siloed Customer Teams Every few years a software category shows up promising to end the silo problem. The CRM was going to do it. Then the customer data platform. Then the customer 360, the composable stack, and whatever the warehouse-native vendors are calling it this quarter. Marketing, support, social, and CX all get wired into the same tables, and everyone assumes shared data will produce shared behavior. Buy the integration, get the alignment. Christina has been on enough of those teams to know how that ends. She came into marketing sideways, an English major who taught math for 5 years, including a stretch at a school built for kids with learning differences where she trained in the Orton-Gillingham method. That method exists to move information from short-term into long-term memory. Marketers have a word for the same thing and call it brand recall. Her husband told her early on that she should go into advertising because she couldn't watch an ad without rebuilding it in her head, tweaking the copy, cutting the music, fixing whatever was broken. She went and took every course, read every book, and kept the teacher's instinct the whole way through: make the hard thing make sense, find the right words for the room you're in. Then she sat at basically every table that claims part of the customer, working as a social listening strategist, a social media creator and a social media strategist, and putting time into support, CX, community and advocacy. What she kept finding was the same organizational failure repeating under different logos, and no amount of shared tooling moved it. Here's the argument that runs under her whole book. Nobody is solving customer experience alone, because nobody is the only person reaching the customer. Support talks to them, so support gets blamed for the relationship. But your UX team is shaping what the customer can and can't do, whether they think of that as CX work or not. Your legal team is writing the fine print the customer reads at the worst possible moment. Finance sets the refund window. All of it builds the relationship, and most of it never appears on anyone's CX dashboard. The result is a company that's genuinely excellent in one place and quietly hostile in another, and it shows. That's why the material became a book rather than a framework or a course. A framework asks you to already agree with its premise before you'll use it. Christina wanted the thing she'd been hunting for on shelves and never found, written for the people at all of those different tables at once. Her words on it: "I think we all wind up writing the books that we wish already existed." The structure follows the problem in 3 moves: Every moving piece that actually builds a brand relationship, most of which sits outside the CX team., Community as the silo killer, the thing that finally forces those teams into the same room., What happens when something lands that you had no way of seeing. A brand crisis, an acquisition by a company your customers hate, or AI arriving and throwing the whole operating model in the air. That third one is where most companies are living right now, and it's the least rehearsed. The martech buying cycle keeps funding integration projects because integration is something you can purchase, and cross-functional accountability is something you have to negotiate. Until one person owns the customer relationship end to end on paper, a new platform just gives 4 teams a shared place to file work they still refuse to coordinate. Key takeaway: List every team that shapes your customer's experience, including the ones that never speak to a customer. Legal writes the cancellation language, UX decides how buried the downgrade button is, finance sets the refund window, and none of them get counted. Put all of them on one page and mark who currently owns the customer's experience of each moment. The blanks are your real CX roadmap, and no platform purchase will fill them in for you. Why Getting the Name Right No Longer Counts as Personalization Accuracy in a lifecycle program has never been cheaper. You can segment by behavior, by lifecycle stage, by predicted churn, by what someone hovered over last Tuesday, and the tooling will do all of it on a mid-tier plan. Ask a marketer whether their email is personalized and they'll show you the merge fields and the branching logic and say yes. Ask the person receiving it and you get a different answer. Christina's position is that martech made accuracy so cheap that creativity started to look like a nice-to-have, and customers noticed long before marketers did. Her frame for the whole industry is the Jeff Goldblum line from Jurassic Park, the one about scientists so preoccupied with whether they could that they never stopped to ask whether they should. We're living inside that meme, she says, running on a lot of we could so we can. And the cost shows up somewhere marketers rarely look, which is in what we've trained people to expect. Conditioning is real, and it doesn't require a marketing degree. Somebody w...

    • Transcript
    • Chapters
  • September 22 · 58 min

    238: The System to make your prospects the hero before they buy, with Aditya Vempaty

    What's up everyone, today we have the pleasure of sitting down with Aditya Vempaty, VP of Marketing at Coram AI. (00:00) - Intro (01:07) - In This Episode (05:05) - Why Expansion Revenue Beats New Logos When Channels Are Saturated (06:07) - Why Customer Empathy Means Knowing What Gets Your Buyer Promoted (12:38) - How to Tell If Your Case Study Makes the Customer the Hero (18:16) - Why Single Channel Attribution Hides the Journey That Closed the Deal (22:00) - How to Find Real Customer Problems by Paying for 30 Minute Interviews (30:29) - The 3 Levels That Connect Marketing Strategy to Tactical Execution (36:32) - Why Implementation Speed Is the Real Answer to a Migration Objection (42:12) - The ABM Play You Can Build From Your Closed Won List Today (48:46) - How Marketing Leaders Get a Team to Actually Adopt an AI Tool (53:26) - How Marketing Leaders Decide What Deserves Their Energy Summary: Aditya has built marketing functions and named categories from nothing at Synthego, Amplitude and Nutanix, and he shows up here to argue that the loud top of funnel work is now the cheaper half of the job. He takes apart the make your customer the hero cliche with a test you can run on any case study in about 4 seconds, then explains why he pays customers $100 for 30 minutes before he will even look at the product he was hired to market. There's a story about harvesting a competitor's free tier badge off customer websites, a telco onboarded in 6 weeks, and a VP of marketing spending 15 minutes making a poster so his team would actually use AI. He also has a very short answer for what your ChatGPT persona document is worth. Stick around for the part where 95% product parity turns into the only argument that reliably wins a migration deal. About Aditya Vempaty Aditya Vempaty is the VP of Marketing at Coram AI, an AI physical security platform running across more than 1,500 locations. Before that he spent 3 years as VP of Marketing at MoEngage, where the company grew from $58M to $100M in ARR. Earlier in his career he built the marketing function and named the category at Synthego, Amplitude and Nutanix, work that spans 2 IPOs and several unicorns. He also invests as an angel, mentors at First Round Capital, and is known for 2 rules he repeats often: market the problem first, and distribution is the strategy. Why Expansion Revenue Beats New Logos When Channels Are Saturated Every marketing team has a number on the board for new logos. Far fewer have one for what happens to a customer 6 months after the contract gets signed. That made sense when acquisition was cheap and a paid dollar reliably bought you a lead. Channels are crowded now, budgets are flat, and the math has moved. Aditya has spent his career at the loud end of marketing. He helped name the product analytics category at Amplitude. He helped define the agentic customer engagement platform at MoEngage. Those are top-of-funnel plays, the kind where you plant a flag and get the industry to repeat your words back to you. So it's a fair question why the category creation guy now spends his time talking about heroes, lifecycle and retention. His answer is arithmetic. For a marketer defending a budget, the second half of that sentence matters more than the first. Cheaper is the obvious part. Easier to show impact is the part that should change how you plan. Every acquisition attribution fight you've ever lost, where the CFO wants to know which of your 6 channels made the lead buy, goes away when the account is already in the building and you can see exactly what they did and didn't adopt. The shape of the products matters too. Amplitude and MoEngage are both multi-product suites. Customers land on one thing, get value, and then expand into the rest. So the second, third and fourth sale are already sitting inside your customer base, and nobody has to be convinced you exist. Aditya points to HubSpot's version of this, where the funnel is a loop rather than a line, and treats the loop as the actual revenue machine instead of a diagram in a deck. Here is the uncomfortable version. Most B2B marketing orgs are staffed and measured around the most expensive, least provable part of the job. If expansion is where the margin actually is, the demand gen team should carry a retention or adoption number, and most demand gen teams would fight you hard on that. Key takeaway: Pull the last 4 quarters of revenue and split it into new logo, expansion and renewal. If expansion is the bigger line and nobody in marketing carries a number against it, claim it before the next planning cycle starts. Pick the one product line your existing customers adopt second and build the campaign for that. Why Customer Empathy Means Knowing What Gets Your Buyer Promoted Customer empathy is the most repeated phrase in B2B marketing and the least examined one. It shows up in every kickoff deck, every messaging workshop, every values page. Ask what it produced and you usually get a persona document nobody has opened since it was made. Aditya spent years on the vendor side of exactly this, at 2 companies that sit in the middle of the funnel where marketing ops and lifecycle people live. He saw how the sausage gets made, and he came out of it with a low opinion of the word. What replaced it is much more specific and much more useful. Sitting inside those companies taught him that the thing worth understanding about a buyer is their job justification. How does this person prove they were worth their salary this year? How do they get promoted? Your product either helps them say that sentence out loud or it does not. At Amplitude the answer was retention. Product managers in B2C couldn't track what they were doing and couldn't justify their own existence and investment. The industry was obsessed with mobile acquisition at the time, so the question that actually kept a PM up at night was how to keep the users they had already paid for and how to show the product was giving those users value. Amplitude built the whole marketing motion around that metric because that metric was the PM's career. At MoEngage the pain had moved. Retention was table stakes by then and everyone was already on mobile and web. The problem had become execution across channels, and the version Aditya describes is painfully familiar to anyone who has run campaigns at a big company. You have every channel available to you and no ability to say what you actually did. He describes marketers who legitimately can't come back and report which campaign hit, where it hit, and which users it reached across the full cycle. Teams operated as channel owners rather than as one coordinated effort. Then he gives the number that makes it real. One customer took 6 weeks to launch a campaign, because getting the segments required 20 people on a SQL query team pulling across 5 databases. 6 weeks. For a campaign. That's the daily texture of the job for the person you're selling to, and no amount of empathy language on a website touches it. Here is the payoff. When your product lets someone explain their value in a simple way that other people understand, they stop being a customer and start being an advocate. As Aditya says, they will rave about you because you made their life easier and their job explainable. Most vendor positioning is aimed one level too high. It addresses the company's pain, churn and efficiency and cost, while the person signing the order form has a smaller and more urgent problem, which is that they can't prove what they did last quarter. That mismatch is why so much martech messaging reads as true and lands as nothing. Key takeaway: Write down the exact sentence your champion would say in ...

    • Transcript
    • Chapters
  • September 15 · 57 min

    237: Designing the utopian marketing leadership role, with Mary Keough

    What's up everyone, today we have the pleasure of sitting down with Mary Keough, Fractional Demand Gen for B2B at DemandLoops. (00:00) - Intro (01:12) - In This Episode (04:30) - How to Write LinkedIn Posts That Hit a Nerve Without AI (06:39) - How to Run an Honest Goal Setting Conversation With Your Founder (14:46) - Why Marketing Always Catches the Scope Creep at Startups (18:39) - How New Marketing VPs Establish Credibility in Their First Month (28:30) - How Women in Marketing Leadership Get Out of the Marketing Girl Box (33:49) - How to Tell if a Founder Who Doesn't Get Marketing Will Trust You (36:54) - What a VP of Marketing Job Description Should Actually Say (39:12) - When to Fight for a Broken Marketing Role and When to Leave (44:43) - Why Fractional Marketing Leadership Works as a Stopgap and Fails Long Term (49:53) - How to Vet a Marketing Role by Talking to People Who Left (53:17) - How to Use a Family Value System to Decide What Deserves Your Energy Summary: Mary wrote a LinkedIn post listing every fear a senior marketer has about taking a VP of Marketing job, and it hit a nerve loud enough to become this entire episode. We take the fears apart one at a time: the goalpost that moves the week after your team clears it, the executive dinner that quietly becomes 6 more events, the launch strategy your boss never opens. Then we build the other side of it, including the 3 sentence job description she'd sign tomorrow and the written SLA that stops sales from eating 4 days of your designer's week. She also explains why the fractional model everyone is rushing toward is a stopgap, and why her own contracts are designed to end. Stick around for the performance review where nothing went wrong and it still nearly took her out. About Mary Keough Mary Keough is a fractional demand gen marketer at DemandLoops, where she builds B2B demand programs that are designed from day one to be handed back to a permanent hire. She has spent 15 years across B2B SaaS and industrial manufacturing marketing, most recently as Director of Demand Generation at CoLab Software, and before that as Head of Marketing at Map My Customers and Senior Marketing Strategist at Gorilla 76. Her roots are in industrial and manufacturing demand gen, a niche she cut her teeth on at Spraying Systems Co. and still speaks about at events like the Industrial Marketing Summit. She's built an audience of roughly 30,000 on LinkedIn writing candid, fundamentals-first posts, and she co-hosts the Purposeful Marketing Podcast. How to Write LinkedIn Posts That Hit a Nerve Without AI You can spot them before you finish the first line. Same hook shape, same clipped one-line paragraphs stacked into a wall, same closing question nobody answers. B2B LinkedIn has gotten very good at producing posts that read like posts, and the feed is worse for it. Then something like Mary's shows up. Her post about VP of Marketing roles was a stacked list of fears, one after another, written against the line founders keep repeating about not being able to find good candidates. It went around because a lot of senior marketers had been carrying those fears for years without the words for them. That feeling of reading your own thought in someone else's sentence is rare, and it's almost never something a hook formula produces. Originality is the entire filter for her, and applying it kills most content calendars on contact. Mary doesn't pre-plan posts. An idea sits with her for a while, usually something that's been bothering her that she can't quite name yet. Then a conversation does it: a marketer venting, a thread she can't scroll past, a call with a potential client. Something tips her over the edge and the post writes itself. Not a single one of her posts has been written with AI. She says it flatly, and you can tell from the drafts: the scar tissue is right there on the page, and the sentences go where an editor would have smoothed them. The cost of that is inconsistency. She posts when she has something, and only then. The return is that when she does post, it carries the one thing a language model cannot manufacture, which is the specific irritation of a person who has actually lived the problem. The volume play is finished. When any competitor can generate a competent, on-brand, keyword-aligned post in 30 seconds, competence stops being a differentiator and originality becomes the only moat left. Most B2B content operations are structurally incapable of producing it, because they're built to hit a cadence rather than to publish the thing only their company could say. Key takeaway: Run every idea on your content calendar through Mary's filter before it gets assigned: if 10 other companies in your category could publish the same piece, kill it. Replace the slots you clear with a standing prompt to your team, asking what has annoyed them at work this month that they can't fully explain yet. Those are the posts that travel. How to Run an Honest Goal Setting Conversation With Your Founder Marketing targets in most startups get set the way weather forecasts get set in a bad novel, which is to say somebody decides what would sound good and then everyone agrees to act surprised when it doesn't happen. The CMO needs a board-friendly story. The investor update needs an AI angle. Almost none of it starts from historical numbers, and the marketing leader gets handed the result and asked to commit. Mary's diagnosis starts a layer above the number. Companies keep saying they need a great VP of Marketing, an awesome VP of Marketing, an amazing director of demand gen. Very few of them are willing to do the work that makes a great one want the job. And the work starts with a company vision that has actual business goals underneath it. The version she wants runs down a ladder. There's the grand vision, the thing that got the company funded in the first place. Under it sits a 1-year and a 3-year picture of what changes in the market if the company wins. Under that sit the business goals that get you there, segmented by department, so product, marketing, and sales each know what they own. She walks through it with an attribution example, mostly because it's easy and partly because she can't resist. Say the company vision is to get founders, CEOs, and marketers to start caring about attribution. Fine. Now what does product build in the next 12 months to make that true? What does marketing publish? What does sales say on calls? Every function can answer that question, and the answers can be checked against each other. That ladder is almost completely gone from the startups she sees. What's left is the revenue multiple, which is a wish, and a set of quarterly targets that get reverse-engineered from it. Marketing is usually the first function asked to sign for the gap. The line that stuck with me is her framing of the whole discipline: everything about marketing is simple and fundamentals driven, and simple has nothing to do with easy. The hard part isn't figuring out what to do. The hard part is that you're doing it inside politically driven motivations, shifting strategies, and goalposts that move whenever the board asks a new question. How to Push Back When Product Wants to Change the ICP Anyone who has worked at a startup knows the move. You spend a year building a content engine around marketers, the quarter goes well, product decides the real opportunity is salespeople, and suddenly your entire library is pointed at the wrong buyer. Product changes a few things in the roadmap. Marketing starts over, and the results of that restart won't show up for 12 months. Mary's honest about the limits here. Preventing the shift is difficult, so you mitigate it...

    • Transcript
    • Chapters
  • September 8 · 55 min

    236: What AI replaces in data work and when not to reach for it, with Julie Beynon

    What's up everyone, today we have the pleasure of sitting down with Julie Beynon, Head of Analytics at Augment Code. (00:00) - Intro (01:07) - In This Episode (04:13) - Breaking Into Data Without A Computer Science Background (06:05) - Why AI Made Building Cheap But Maintenance Expensive (09:25) - How To Push Back On AI Projects Without Killing Momentum (12:28) - Building AI Agents That Multiply A Two Person Data Team (16:29) - When Not To Reach For AI At Work (22:23) - Running A Lean Data Team As A Force Multiplier (27:28) - The Spreadsheet Test For Keeping Your Data Stack Simple (31:49) - How To Prove The Value Of Invisible Data Work (39:14) - Why Data Analytics Is Becoming GTM Operations (41:47) - What Part Of The Data Analyst Job AI Is Replacing (47:15) - The Data Skills AI Can't Replace (52:07) - How To Decide What Deserves Your Energy Summary: Julie Beynon came into data sideways from content marketing, got cut down to 2 words by a CEO, and turned that into a 15-year career running lean data teams. In this episode she breaks down how she cloned her best analyst into an AI agent named JimBot, why she pushes back on AI projects without ever saying no, and how she gets companies to fund the invisible foundation work nobody claps for. She makes the case that AI knocked down the SQL wall, so the real bottleneck moved to the data model underneath, and that simple is now the hardest skill in the room. There's a spreadsheet test, a team of 2 that runs like 10, and a surprisingly hopeful take on AI making us more human. Stick around for the part where she explains why the boring option takes the most nerve. About Julie Beynon Julie Beynon is the Head of Analytics at Augment Code, an AI coding assistant company, where she runs a lean data team and builds AI agents that let the whole company self-serve trusted answers. She's spent roughly 15 years in data and go-to-market analytics, previously leading data at Census and analytics at Clearbit, with earlier stops at Customer.io and other startups. She's self-taught and came into data sideways from content marketing, with no computer science background. Alongside the day job she writes a series of short essays on data, AI, and the discipline of knowing when not to reach for a tool. Breaking Into Data Without A Computer Science Background Most people assume the path into data analytics runs through a computer science degree or at least a few years buried in SQL. Julie's route looked nothing like that. She started as a content marketer, and by her own account she was bad at it. Her CEO once edited a piece of her writing down to 2 words. That kind of feedback breaks a lot of people. It did the opposite for her. Getting cut down that hard built a habit of raising her hand for whatever nobody else wanted to do, and the thing nobody wanted to touch was data. The real turn came at Customer.io, where a new tool called dbt showed up and someone offered to train her on it. She figured saying yes would be silly to pass up, and within a few sessions the whole job changed shape. The moment she describes is the one every self-taught analyst recognizes. You take a pile of raw rows nobody trusts, run it through a model, and suddenly you're holding something a whole company can use. She calls it realizing what power she had just yielded. The intuition that makes her good now got built out of the failures that came first, then an open door she was willing to walk through. Coming at data from content rather than engineering is why she reads the business side so well, because she learned the questions before she learned the syntax. Key takeaway: Volunteer for the messy, unowned work on your team, especially the data nobody wants to clean. Pair it with one modern tool you can learn hands-on, like dbt, and get a colleague to walk you through a real project rather than a tutorial. The reps you build on work nobody else will do become the skill set that's hardest to replace later. Why AI Made Building Cheap But Maintenance Expensive AI has made it trivial to spin something up. A dashboard, a workflow, a scrappy internal tool, all of it now takes an afternoon and a few prompts. The problem is that the fun part and the expensive part were never the same part. Julie's been making this argument since before AI showed up, in a build versus buy post she wrote 5 years ago, and the tooling change only sharpened it. Here's the trap she watches teams fall into. Someone builds a slick dashboard because they can, everyone's impressed, and then 6 months later sales walks up asking why a number moved. There's data drift, nobody remembers how the thing was wired, and the person who built it isn't the one who has to answer for it. She's the one who answers for it. So before anything gets built on her watch, she runs it through a short set of questions: Is this mission-critical, or just fun to make?, Who owns it and who maintains it when it breaks?, Is there an off-the-shelf tool that costs less per month than the tokens it takes to build and run this? That last one lands hard. You can burn $1,000 building something that replaces a tool costing $5 a month, and feel productive the whole time. The cheap build is a real cost hiding as a win. Maintenance never got cheaper, which means the discipline that matters now is refusing to build the thing you're perfectly capable of building. Key takeaway: Before you build anything with AI, price the maintenance, not the build. Ask who owns it in 6 months, whether it's mission-critical, and whether a paid tool would cost less than the tokens you'll spend building and running your own version. If the honest answer is that you're building it because it's fun, buy the tool instead. How To Push Back On AI Projects Without Killing Momentum Ops and data people carry a reputation as the team that says no. Every shiny new tool, every "we should build this," runs into someone whose job is to point out that you already own 2 things that do the same job. That instinct matters more than ever now that anyone can build anything. It also curdles fast into being the person nobody wants to bring ideas to. Julie's answer is to almost never say no at the door. There's a real constraint underneath her patience, since on the data side you have an actual duty to protect the data and the security around it, so some requests genuinely can't fly. Outside of that, she lets people run. That quote comes from a specific win. One of her engineers built a proof-of-concept dashboard for a new product. It worked. Instead of telling him to stop, she treated it as a starting pad and let him take the first pass as far as he wanted. AI helped him productize and visualize what he was thinking. Then, when the question became how to make it sustainable, she stepped in and rebuilt it as a stable dashboard in their BI tool with observability and role-based access control baked in. The engineer knew every data point and how it was tracked, so she pulled the context straight out of his code instead of extracting it from him in a meeting. She calls it one of the most productive data cycles she's had. Letting someone go a few steps before you take the wheel turns pushback into a handoff, and the person feels seen instead of shut down. Key takeaway: When someone brings you an AI-built prototype, resist the reflex to kill it. Let them take the first pass all the way, then offer to productize it yourself, pulling the logic and context out of their code rather than dragging it out of a meeting. Reserve your hard no for genuine security and data-protection lines, and treat everything else as a draft you can finish. Building AI Agents That Multiply A Two Pers...

    • Transcript
    • Chapters
  • September 1 · 55 min

    235: Why consent banners, tags and privacy policies never match, with Stéphane Hamel

    What's up everyone, today we have the pleasure of sitting down with Stéphane Hamel, Founder and Product Architect at MANTIS. (00:00) - Intro (01:07) - In This Episode (04:07) - How To Audit Which Trackers Are Firing On Your Website (07:00) - Why Nobody On Your Team Owns The Tags Firing On Your Site (11:22) - Why Your Consent Banner Does Not Match What Your Site Actually Does (16:45) - Does Server Side Tagging Actually Improve Privacy Governance (22:30) - Why Loading A Third Party Script Is Already A Privacy Risk (27:30) - What Happens When You File A Privacy Complaint In Canada (36:42) - Why AI Output Is Useless Without Domain Expertise (41:58) - Is AI Removing The Training Path For Junior Marketers (44:16) - When Vibe Coding Works And When It Falls Apart (47:56) - Why Marketing And IT Still Fight Over Who Maintains The Stack (52:02) - How To Decide Which Projects Deserve Your Energy Summary: Stéphane Hamel built the first web analytics QA tool back in 2006, watched an ad blocker quietly borrow his logic, and spent the next 20 years learning that tracking got harder to see every year while the industry got better at documenting it. Now he's building MANTIS, a privacy observability platform that watches what a website actually does instead of what its policy claims. Along the way he audited his own credit bureau account after a breach, found trackers from companies that no longer exist sitting on the page displaying his credit file, and spent 2 years fighting to get them removed. He also coined the term vegetative AI, sold his house and furniture after one good vacation in the Rockies, and will tell you your consent banner is a receipt for a transaction nobody actually agreed to. Wait until you hear what a third party script collects before it fires a single tag. About Stéphane Hamel Stéphane Hamel is the founder and product architect of MANTIS, a privacy observability platform built to show what a website actually does at runtime rather than what its policy claims. He's spent 35 years across the full arc of digital analytics, building WASP in 2006 as the first web analytics quality assurance tool, publishing the Digital Analytics Maturity Model in 2009, and creating Da Vinci Tools, which was later acquired by Supermetrics. He teaches MBA and EMBA students at Université Laval and advises privacy tech startups including Supermetrics, Caden, and Masthead Data. He's currently writing his first book on digital analytics concepts, and having been a victim of 2 of Canada's largest data breaches, he treats privacy as a trust problem rather than a legal one. How To Audit Which Trackers Are Firing On Your Website Open the network tab on your own company's homepage and count the outbound requests. Most marketers who try this land somewhere between 40 and 100 calls going to domains nobody on the current team approved. In 2006 that number was small enough to check by hand, and the only question worth asking was whether your analytics tag fired on the right page at the right moment. That's the question Stéphane built WASP to answer. He was on the technical side back then, implementing trackers on client websites, and nobody in the room was talking about privacy or where any of this data ended up. The tool checked whether tags fired when they were supposed to and whether they collected the right information. It was quality assurance work, and it was the first tool of its kind. That shift wasn't academic. Stéphane got hit by data leaks more than once, watched fraud follow, and spent 35 years consulting and teaching through every generation of the tracking stack. MANTIS is what came out the other side, and it asks a very different question than WASP did. The new question has 3 parts: What is actually loading on this website in terms of trackers, Whether any of those trackers fire without consent, What kind of data each one collects once it does The hard part is the chain. Websites include third party scripts that load other scripts, a pattern Stéphane calls piggybacking, and every hop moves further from anything a marketer ever approved. Fingerprinting rides along in the same traffic. By the time a script 4 hops deep does something it shouldn't, nobody on the marketing team can name what triggered it or say where the data went. So MANTIS is built for people who don't read network waterfalls for a living. Marketers, decision makers, privacy specialists, and the technical folks stuck doing QA on tags all need to see the same picture, and right now they each see a different slice of it. The industry spent 20 years getting very good at deploying tags and almost no time getting good at watching them. That gap is why most privacy programs audit documents instead of traffic, and why the documents keep passing while the traffic keeps failing. Key takeaway: Run your own homepage through a network inspector before you click anything on the consent banner, and write down every third party domain that already received a request. Compare that list against the vendor list your consent management platform declares. Any domain in the first list that's missing from the second is a gap you own right now. Why Nobody On Your Team Owns The Tags Firing On Your Site Anyone can inspect a page now. Right click, open the debug console, watch the calls go out live. That was exotic in 2006 and it's table stakes today, which raises a reasonable objection to the whole category of tag surfacing tools. If the browser already shows you everything, what's left to build? Stéphane's answer is that the console shows you the calls and tells you nothing about who authorized them. Working with technical people for 35 years, he's watched the gap widen. The traffic is visible. The accountability is gone. There's a story from the WASP years he still turns over. Someone messaged him saying the tool should block the tags it found. He said no, that's not the purpose, the purpose is quality assurance. Shortly after, ad blockers took off, and he found some of his own logic sitting inside one of the very first ones. He had no way to prove it and no means to enforce anything. He was one guy solving his own problem, and the industry took the idea somewhere he hadn't intended. 20 years on, the reason tags go unowned has less to do with technology than with how many people handle a single pixel on its way to production. Stéphane walks through the cast: The marketer, who asks for something and moves on, The software engineer, or the data engineer, depending on which fancy name the org uses this year, Legal, who reviews the words and doesn't get the technical constraints, The consent management platform vendor, whose position is that they provide a tool and don't provide legal advice That's 4 groups, and not one of them can describe the whole system. That's before you count the agency your marketing lead handed tag manager access to, the one that said trust us, we know what we're doing, we're just going to put a tag on your website. Then somebody runs an audit 2 years later and finds tags firing that nobody recognizes. Here's the part that should bother you more than it probably does. Every single tag is a privacy risk and a security risk at the same time. The moment you put third party JavaScript on your site, you've given someone else write access to your users' browsers, and that script can change tomorrow without telling you. It might start recording keystrokes. It might start fingerprinting. Stéphane notes that vendors coming out of the US tend to be particularly greedy about collection, and none of that requires a new contract or a new conversation. What MANTIS does about it is draw a timeline. You got consent at t...

    • Transcript
    • Chapters
  • August 25 · 1 hr

    234: How to run a marketing ops audit and turn it into a roadmap with Kelsie Dube

    What's up everyone, today we have the pleasure of sitting down with Kelsie Dube, Director of Marketing Operations and Automation at Unanet. (00:00) - Intro (01:09) - In This Episode (05:31) - Why New Marketing Ops Leaders Should Audit Before They Act (10:09) - How to Run a Marketing Ops Audit When You Join a Company (16:04) - How to Balance a Full Audit Against Your Boss's Priorities (19:00) - How to Spot a Process Problem Disguised as a Tool Problem (23:41) - How to Surface Shadow Processes and Duplicate Tools in an Audit (31:08) - How to Choose a New Martech Tool With a Tiger Team of Power Users (35:30) - How to Turn a Marketing Ops Audit Into a Prioritized Roadmap (41:20) - How to Align Your Team on a Measurement Philosophy (44:22) - What Happens to Junior Marketing Ops When AI Eats the Entry-Level Work (49:26) - How to Turn Marketing Ops From Ticket-Takers Into Business Partners (52:40) - How to Use One Interview Question to Screen for Team Fit (56:57) - How a Marketing Ops Leader Decides What Deserves Her Energy Summary: Kelsie Dube joined Unanet and did the one thing most new ops leaders are too nervous to do: nothing, at least at first. In this episode she breaks down the 30-day audit she runs before changing a single tool, how she sorts 30-plus problems into big rocks, quick wins, and parked FY27 headaches, and why she'd rather build a "digital twin" to argue with than buy another platform. She gets into the tiger-team playbook that makes tool replacements actually stick, the measurement fight every team should have out loud, and what happens to junior marketers when AI eats the entry-level rung. It's a full field guide to walking into a new company and earning trust before you change anything, plus the interview question she uses to know if you'll thrive on her team. Kelsie's Marketing Operations Assessment Framework: https://drive.google.com/file/d/1x3IJf9tHku4-fASS9AQE_F6sJ1FLK3PN/view?usp=sharing Liza Adam's Digital Twin: https://www.linkedin.com/pulse/smartest-ai-teammate-youll-ever-build-liza-adams-c7rnc/ About Kelsie Dube Kelsie Dube is the Director of Marketing Operations and Automation at Unanet, where she joined in 2026 to rebuild the company's measurement and reporting foundation. Before that she spent 7 years at Sophos, climbing 5 roles from Marketing Operations Specialist to Director and leading the team through much of that run. She started her career in data entry, scrubbing lead lists and learning the VLOOKUPs she still uses most days, and that ground-floor view shapes how she thinks about the craft. Outside of work she's a mom of 2, an amateur basketball player, and a snowboarder who guards the hours between 5 and 7 for her kids. Why New Marketing Ops Leaders Should Audit Before They Act The first 90 days at a new company come with a quiet dare. Ship something. Plant a flag. Prove they were right to hire you. Most new marketing ops leaders answer that dare by ripping out a tool they never liked and installing one they used at their last job, all inside the first 2 weeks. Kelsie did the opposite when she joined Unanet. Her first move was to change nothing. The reasoning was twofold. Without a real map of how work gets done, any change you make is a guess, and you'll probably aim it at the wrong target. And Unanet had said in the job description that they wanted someone who'd slow down and understand the business first, which is part of why she took the role. For a marketer who came up through ops and now leads it, that patience is the whole point. You have to understand what's happening well enough to be dangerous, she says, before you can tell anyone what to actually fix. The harder version of this is resisting your own experience. A new CMO walks in and wants a different marketing automation platform because they used one at the last company. Kelsie feels that pull too, since she's learning a few systems here for the first time. Her answer is to make the existing stack work before spending a dollar to replace it. Right now she's rebuilding how campaigns get captured and tagged to campaign members, and she's solving it with a Salesforce workflow or Power Automate instead of a purchase. Replacing the tool would cost more and probably solve less. Then comes the part nobody warns you about, the clock. Listening mode has a shelf life, and everyone, including you, is quietly counting the days until you produce something. Kelsie gave herself a hard deadline of 30 days to finish the audit, then engineered small wins into every week so the learning never looked like stalling. New hires have one thing veterans don't, an empty calendar, and she guarded it. A recurring block every Friday at 2:00 to turn that week's discoveries into something usable. One of those blocks became a 30-minute campaign taxonomy diagram she walked the leadership team through, a quick and visible proof that better structure meant better reporting down the line. The instinct to ship in week one is the most expensive habit in operations, because a fast fix aimed at the wrong problem still has to be undone later. The operators who compound value are the ones who can sit in not-knowing long enough to find the actual bottleneck. Key takeaway: Give yourself a fixed audit window, 30 days is a reasonable default, and block a standing hour each week to turn what you've learned into one small, visible win. Keep a running log of quick wins as you find them so you always have something to ship while the deeper work is still in progress. How to Run a Marketing Ops Audit When You Join a Company "Do an audit" is the most repeated and least defined phrase in marketing ops. Everyone nods along. Almost nobody can tell you what's actually in one, which funnel stages it covers, or where it starts on a Monday morning. Kelsie's version starts before day one. The moment she accepted the Unanet offer, she began mapping ownership lines: what marketing ops owns, what rev ops owns, what IT owns. She wrote out the questions she had and sent them to her leader ahead of her start date, partly out of genuine excitement and partly to get a head start on the RACI. From there it's a series of conversations that snowball. She interviews everyone from her own team to her peers to the level above to her business partners, and she ends every conversation the same way: who else should I talk to? The list never stops growing, but the patterns start to show. Along the way she confirms the systems in her swim lane against the job description, asks what she's missing, and books dedicated working sessions where someone walks her through a live task like a lead import. She records those sessions, then feeds the transcriptions to an LLM to pull out the patterns, a tip she picked up from a mentor that turns hours of shadowing into a usable roadmap. She also runs the work herself. Joining a much smaller team than she was used to, she asked people to hand her a lead import and a campaign creation task so she could run them personally. Part of it was capacity, since a director who can still execute is extra hands when the team is slammed. Part of it was fluency. As she says, a director isn't really a "do-rector," but being right there in the work is how you understand it well enough to change it later. Data privacy rides shotgun through all of this. Coming out of the cybersecurity world, she asks about data retention and opt-in policies early, then files them under "needs struct...

    • Transcript
    • Chapters
  • August 18 · 1 hr 1 min

    233: 4 Mindsets that modern marketing leaders need for unpredictable markets, with author Kathleen Schaub

    What's up everyone, today we have the pleasure of sitting down with Kathleen Schaub, Author, Strategist, and Advisor in Marketing Management and Organizations. We cover: (00:00) - Intro (01:19) - In This Episode (04:45) - Why Marketing Behaves More Like Weather Than a Machine (08:49) - The Four Mindsets Marketing Leaders Need for Complex Markets (14:06) - Why ROI Doesn't Work as a Marketing Measurement (18:19) - What Causal AI Can Actually Tell You About Marketing (28:37) - Why Your Marketing Budget Works Like an Investment Portfolio (34:21) - What Marketers Should Actually Be Held Accountable For (38:49) - The Surrogation Trap Behind Single Marketing Metrics (44:09) - Why Marketing Managers Should Create Conditions Instead of Commands (50:00) - How Pace Layering Makes a Marketing Budget Adaptable (57:16) - How Intention Decides What Deserves Your Energy Summary: Marketing has spent a century pretending it's a machine you can feed cash and read like a receipt, and Kathleen Schaub is here to take that apart. She makes the case that markets behave more like weather than vending machines, walks through her 4 mindset shifts for leading in the chaos, and explains why a single north-star metric quietly wrecks your decisions. Along the way there's a butterfly in Brazil, a cucumber garden in Canada, a poker champion, and a COO who begged for the book on a cruise. If you've ever had to defend marketing ROI in a boardroom and felt the ground move under you, this conversation names what's really going on. About Kathleen Schaub Kathleen Schaub is an author, strategist, and advisor focused on marketing management and organizations, working through KathleenSchaub.com. She spent 9 years leading IDC's CMO Advisory practice, where she advised hundreds of technology marketing leaders, and she's a longtime contributor to leading martech publications including CMSWire. Her work argues that markets are complex adaptive systems rather than predictable machines, and her book lays out 4 mindset shifts, investor, navigator, statistician, and ecologist, for leading marketing in a volatile world. These days she splits her time between writing, advising, passion projects, and being a grandmother of 2. Why Marketing Behaves More Like Weather Than a Machine Every marketing leader has sat across from a finance executive who wants one thing. Put a dollar in, know what comes out. Budgets get built on that promise. Forecasts get defended on it. And every year, the results wander off somewhere the model never predicted. Kathleen has spent more than a decade explaining why that keeps happening. Her line is that marketing is not a vending machine. A vending machine is controllable and predictable. You put your money in, you press the button, you get the exact thing you chose. Executives, she says, would give almost anything for marketing to work that cleanly. Marketing never has. The weather comparison is a humbling one. We carry supercomputers in our pockets, we have decades of atmospheric data, and we still can't say for sure whether it'll rain tomorrow. Marketing runs on something messier than the atmosphere, which is people. Buyers, brands, influencers, partners, and the economy, all reacting to each other at once. Kathleen calls the result a complex system, the kind scientists study, where every interaction feeds back into the next and quietly introduces unknowns into every situation. That's the part most measurement frameworks skip over. Marketers, salespeople, and customer experience teams all work at the edge of the company, the seam where the controllable inside meets the uncontrollable outside. Their whole job is to manage, predict, and measure a world that refuses to hold still. No amount of dashboard polish changes the fact that half the inputs live outside the building. Kathleen keeps the parts of the old factory toolkit that still earn their place. The shift she wants is smaller and harder to swallow. Accept that markets are what the military calls VUCA, volatile, uncertain, complex, and ambiguous, then adopt measurement practices built for that reality instead of pretending the reality is something tamer. The methods already exist. Other fields use them every day. Marketing just hasn't bothered to translate them yet. The uncomfortable implication is that a lot of the dashboards marketing teams present with total confidence are measuring a machine that was never really there. The teams that win the next decade will be the ones who stop apologizing for uncertainty and start building for it. Key takeaway: Audit your current reporting for any number you present as a guarantee. Rewrite each one as a range or a probability, and rehearse saying "here's what's likely and here's what could move it" before your next budget review. Present every forecast as a weather report, a set of ranges you can actually defend. The 4 Mindsets Marketing Leaders Need for Complex Markets You can buy a new attribution platform, restructure the team, bolt on a dozen dashboards, and still land exactly where you started. Kathleen thinks she knows why. You changed your operations without changing the thing that steers them, which is your mindset. She treats mindset as the director of every action a team takes. Leave it untouched, and every operational upgrade delivers roughly the same results you've always had. Her favorite way to explain this comes from Buddhism. Your mind is the ox, and the cart is everything the ox drags behind it, every outcome your marketing produces. The leverage is quieter than it looks. Change your mindset and the dozens of small decisions you make every day start bending, even slightly, and over enough decisions you end up miles from where the old direction would have taken you. That's why the mindset has to move first. The 4 she recommends all come from worlds that are genuinely volatile and complex, but they're worlds anyone can picture, and each one maps onto something marketing leaders already wrestle with. Investor: stop treating the budget like a cost center you spend down, and start treating it like a portfolio you risk for a better future return., Navigator: change the relationship between your plan and your measurement, adapting like a pilot or a sailor who reads the conditions in front of them instead of clinging to the route., Statistician: give up the hunt for certainty and accept that everything in the human and natural world runs on probability. Kathleen calls this the hardest of the 4 because our brains hate it., Ecologist: manage the conditions people work in, because behavior arises from the intersection of an individual and the context around them, so managing the person by themselves only covers half of it. Read them together and a pattern shows up. Every one asks you to trade the illusion of control for the ability to adapt. That trade is where most transformation efforts quietly die, because swapping tools is easy and swapping beliefs is not. Key takeaway: Before you approve the next platform or reorg, write down the belief driving it in one sentence. If that belief still assumes marketing is predictable and controllable, fix the belief before you spend the money. Pick one of the 4 mindsets and name the single daily decision it would change for you this quarter. Why ROI Doesn't Work as a Marketing Measurement Ask a room of marketers what keeps them up at night and ROI lands near the top every time. It's the question waiting in every board meeting. What drove revenue last month, and how much will this next bet add? Kathleen has been chasing that question longer than most. Running IDC's CMO advisory practice for 9 years, she kept asking leaders whether they were actually getting ...

    • Transcript
    • Chapters
  • August 11 · 1 hr 6 min

    232: How taste can be codified into systems and is no longer a durable skill, and what's next with Sharon Gai

    What's up everyone, today we have the pleasure of sitting down with Sharon Gai, author, keynote speaker, and educator. We'll cover: (00:00) - Intro (01:21) - In This Episode (05:20) - How AI Moves Marketing Creativity Up the Abstraction Layer (09:08) - Why Taste Is No Longer a Durable Human Skill (15:33) - What Human Work Looks Like After AI Learns Taste (20:41) - How to Codify Taste Into Your AI Systems (27:20) - Why Hands-On Execution Still Builds Judgment AI Can't Copy (36:48) - How to Build a Personal AI Strategy and Learn How to Learn (39:31) - Why Seeing Your Job as Tasks Rather Than a Role Future-Proofs Your Career (48:12) - Why You Should Tinker With AI Before You Cut Back on Meetings (54:13) - How Students Can Adapt to Graduating Into an AI Economy (01:02:13) - How to Decide What Deserves Your Energy Summary: Sharon Gai wrote a book telling everyone that taste and judgment were the durable human skills AI couldn't touch, then went on record to take half of it back. In this episode she walks through why taste is now codifiable, why originality still belongs to humans, and how she pulled a McKinsey-grade deck out of Claude by feeding it her own best work. We get into the lost generation of junior executors, the beekeeper mindset that separates orchestrators from busy bees, and a token-maxing hot pot dinner that made her swear off wasting agents. She even hands over a deathbed test for deciding what deserves your energy. If you've ever wondered which of your skills actually survive the next few years, this one is a map. About Sharon Gai Sharon Gai is an author, keynote speaker, and educator who helps professionals future-proof their careers in an AI-driven economy. She teaches on Maven and speaks to audiences around the world about how AI is reshaping creativity, work, and the roles people play inside their companies. Her latest book uses the image of busy bees and beekeepers to argue that the future belongs to the people who orchestrate AI rather than execute every task themselves. Before writing and speaking full time, she spent years in enterprise tech, starting out helping IT directors and CIOs build data centers at the dawn of the cloud era. That hands-on background shows up throughout her work, especially in how she thinks about the difference between doing the work and orchestrating it. How AI Moves Marketing Creativity Up the Abstraction Layer When cameras got cheap, painters thought they were finished. Why spend weeks rendering a face in oil when a machine could capture it in a fraction of a second? Plenty of people called photography mechanical, soulless, artless. The threat was real, and the outcome was stranger than anyone expected. Freed from copying the world exactly, painters walked through a door cameras couldn't follow, into Impressionism, Cubism, and everything that made modern art feel alien at first. Sharon sees the same door swinging open for marketers right now, and the word she keeps returning to is abstraction. Walk through any art museum and you can watch this happen on the walls. The 1800s rooms are full of precision: portraits rendered to the eyelash, battle scenes with every horse in place, oceans and landscapes you could almost step into. Then you reach the modern wing, and some people stop and say "I could have done that." The work got harder to read. You have to stand there a moment, wonder who the artist was and why they picked this particular black over a lesser one. The craft survived by climbing a level, from rendering reality to deciding what the piece should mean. Sharon's marketing version is concrete. A few years ago you drew up a Facebook ad yourself, designed the banner, and loaded it into the platform's back end by hand. Now that whole chain can run on its own. So what does the marketer actually do? The real decisions look different now. You choose whether to test one ad or several hundred, each personalized to the person about to see it, whether the creative should be a flat image or a video, and if it's video, what belongs on screen. The manual work shrinks and the number of real decisions explodes. That's the abstracted layer, and it's where the job is heading whether marketers are ready or not. Over the next 2 years, the marketers who struggle will be the ones who still measure their worth by the execution those tools just swallowed. Key takeaway: Write down every part of your last campaign that was pure execution: building the banner, resizing creative, loading it into the platform, pulling the report. Hand those tasks to AI on your next campaign and pour the reclaimed hours into the layer above them. Decide which audiences, how many variants, what format each one takes, and what the creative is actually trying to make someone feel. Why Taste Is No Longer a Durable Human Skill For about a year, the safe career advice sounded identical everywhere. Let AI do the work, but hold onto taste and judgment, because those are the human skills a machine can't reach. Tech CEOs repeated it on every stage. Sharon believed it too, and wrote it into her book. A few months later she started taking half of it back. Her reasoning is uncomfortable if you've built a career on your eye. What is taste, really? For an expert artist it's roughly 10,000 hours of seeing the bad versions and the good versions until they can tell the difference on sight. That makes the taste expert something close to a mini LLM, a person pummeled with enough examples to develop a reliable read on quality. And if taste is just a very large training set, there's no obvious reason you can't hand those examples to a model. Sharon is careful about where she draws the new line. In AI time, she points out, a single day can feel like a year, so any strong opinion has a short shelf life. She'll grant that judgment might still belong to humans, but taste she no longer counts in that column. Her position in June of 2026 is that taste is codifiable, and that alone knocks it off the list of things only people can do. Where Originality Still Separates Humans From AI She used to open talks with a slide that read: creativity does not equal originality. The words sound like twins, and they describe 2 different things. AI can be creative. It can paint in an impressionist style and produce something that would look at home in a modern gallery. Originality means something genuinely novel, something that hasn't appeared anywhere before, and everything a model makes was pre-trained on what already exists. The gap shows up most clearly in how humans move between domains. Narrow AI, the kind we mostly use today, is trained on one field. General intelligence takes a principle from physics class and applies it to biology, or borrows something from construction and uses it while cooking. People do this instinctively from childhood. Machines still struggle with the leap, which is why originality, for now, stays on the human side of the ledger even as taste crosses over. If taste is now a training asset, the marketers still selling "good taste" as their moat are pricing a skill the market is about to commoditize. Key takeaway: Stop treating your taste as something locked inside your head. Start capturing it instead. Save the emails, decks, and campaigns you consider excellent right next to the weak ones, label what makes each good or bad, and feed both to your AI tools so the model learns your specific standard rather than a generic one. What Human Work Looks Like After AI Learns Taste Every few months someone publishes a confident map of what work looks like in 2030. Ask Sharon and she starts by lowering the temperature. Most of it is prediction, and prediction is ch...

    • Transcript
    • Chapters
  • August 4 · 56 min

    231: Why Lattice careers and communication skills are built for the AI era, with Michele Martin from Ticketmaster

    What's up everyone, today we have the pleasure of sitting down with Michele Martin, Senior Director of Global Brand and Integrated Marketing at Ticketmaster. Summary: Most career advice still hands you a ladder and tells you to climb. Michele makes the case for a lattice instead, the crisscross path of sideways and diagonal moves that built her range across banking, retail, a startup, and now Ticketmaster. Along the way she breaks down why the call center job she almost refused changed everything, how mentorship and sponsorship are 2 different engines, and why communication beats coding as the skill that survives the AI era. She even walks through what a genuinely good AI session looks like on her marketing team. If you've ever felt boxed in by the straight climb up, this one rewires how you think about your next move. About Michele Martin Michele Martin is the Senior Director of Global Brand and Integrated Marketing at Ticketmaster, where she leads brand strategy and ties together the channels that reach fans across sports, music, and the arts. Her career is a working example of the lattice she advocates: 11 years at TD Bank moving through corporate sponsorships, communications, a contact center, and sales strategy, then brand and customer roles at L.L.Bean and a stretch at a growth-stage startup. A first-generation college graduate, she builds communities everywhere she lands, from the Kind/Red marketing collective to Live Nation Women. She's also an avid traveler, an amateur golfer, and a self-described nonfiction reader who gets her fiction through Netflix and TikTok. What A Lattice Career Means And Why It Beats The Ladder Most career advice still runs on a single image: the ladder. You start as a specialist, become a manager, then a senior manager, then a director, then a VP, climbing one predictable rung at a time. It's clean, it's legible on a resume, and it quietly convinces a lot of good marketers that any move which isn't straight up is a step backward. Michele has spent 2 decades proving the opposite, and she has a better picture in mind. Walk into any hardware store and find the fencing section. The lattice is that crisscross panel of wood, the one you'd nail under a porch for a little privacy. That shape is the whole philosophy. A lattice career moves diagonally, sideways, sometimes down for a stretch, and then back up from a completely different position than where you started. The freedom in that reframe is the part people miss. When you stop treating vertical promotion as the only valid direction, you give yourself permission to chase the work you're actually curious about. You might take a role that pays the same and reports to the same level because it teaches you something your current lane never will. A year later that detour is the reason you can do a job nobody on the straight-up track is qualified for. The lattice isn't a slower ladder. It's a wider map of where a career can go. The marketers who'll be hardest to replace over the next decade are the ones who collected range on purpose, because range is the thing no single promotion can hand you. Key takeaway: Stop scoring every potential move by whether the title goes up. Draw your own version of the lattice instead: list the 3 skills or domains you most want to understand, then look for roles that hand you those, even laterally. The diagonal move you resist now is usually the one that makes you uncopyable in 5 years. How To Decide If A Sideways Career Move Is Worth It Every marketer has a moment where someone dangles a role that feels wrong. Wrong department, wrong direction, wrong vibe for the brand you're trying to build for yourself. The instinct is to protect the climb and say no. Michele almost did exactly that, and the role she nearly turned down ended up shaping everything that came after. She spent 11 years at TD Bank, a place that treated career growth as a contact sport. The internal logic there was simple: if you want to grow, try a little bit of everything, because that's how you actually learn how the business makes money and how decisions land on customers and employees. So when a contact center role came up, it fit the bank's culture even though it terrified her. She'd never pictured herself there. She didn't know what the work even was beyond the frontline. What tipped her over was a mentor named Matt Chevalier, who passed away young and whose advice she still repeats. He'd watched her get comfortable, and comfort was the thing he refused to let slide. His argument wasn't that her current job was bad. It was that she'd convinced herself the fun she was having couldn't exist anywhere else, and that staying put meant never testing herself in the ways a harder room would. That landed. Inside the contact center she learned phone systems, quality programs, and the guts of customer technology she'd never have seen from a marketing desk. The next jump, onto a sales strategy team, was scarier still, because now she was building an incentive platform alongside genuinely technical people while feeling like she knew nothing. The relief came from a small realization that's stuck with her since. If you can explain a complicated system in plain words, you actually understand it. The roles people resist hardest tend to be the ones carrying the steepest learning curve, which is exactly why avoiding them keeps so many careers thin. Key takeaway: The next time a role feels like a sideways or scary move, separate the fear from the data. Ask what specific skill or exposure it would hand you that your current seat never will. If the honest answer is "a lot," treat the discomfort as a signal you've found a real growth edge, not a reason to pass. Why Integrated Marketing Is A General Contractor Role Marketing invents job titles faster than anyone can define them, and "brand and integrated marketing" is one of those phrases that means something different at every company. At Ticketmaster, Michele uses a metaphor that finally makes the term concrete. Integrated marketing is the general contractor of a campaign. Think about what a general contractor actually does on a build. They don't pour every foundation or wire every outlet themselves, but they understand each trade well enough to know when something's off, and they're the one person responsible for making all of it connect into a house someone can live in. That's the role. You need real fluency across the components of a campaign, whether that's CRM, paid media, organic, or social, and then you need the rarer skill of wiring those parts together. Here's where the lattice pays off in a way no straight-line career could. Because Michele spent years jumping into unfamiliar functions and learning new technology on the fly, she walks into an integrated role already understanding most of the trades she's coordinating. That changes the quality of the work. Without that range, the job quietly degrades into project management, chasing status updates and herding stakeholders. With it, she comes to the table with a real point of view on every channel because she's actually done the work in them. The integrated marketer who can only schedule the trades gets treated like overhead, while the one who understands the trades gets treated like a leader, and the difference is almost always hands-on range. Integrated Marketing Versus Omnichannel Marketing People lump integrated marketing and omnichannel together constantly, and Michele doesn't fight that too hard. Where she sits, the 2 work almost the same way. The job starts with a business challenge for a given category, then asks how marketing can support it, then defines a single concept grounded in data and fan insight. Only after that does the work fan out to the channels. The discipline is in what stays constant ...

    • Transcript
    • Chapters
  • July 28 · 1 hr 5 min

    230: Zero-click marketing broke the measurement layer, so what should ops teams do now, with Amanda Natividad

    What's up everyone, today we have the pleasure of sitting down with Amanda Natividad, Chief Evangelist at SparkToro and co-author of Zero Click Marketing. We'll cover: (00:00) - Intro (01:14) - In This Episode (06:37) - Why Attribution Breaks Down in a Zero Click World (16:09) - What the Alligator Graph Means for Ops Teams (22:46) - How to Run a Zero Click Launch Week You Can Actually Measure (32:35) - How Incrementality Testing Works at Enterprise Scale (38:09) - What Audience Listening Adds to the Marketing Ops Stack (42:50) - What Content Leaders Need From Marketing Ops (47:59) - How to Turn Audience Research Into an Operational System (52:00) - How AI Visibility Changes Zero Click Marketing (58:36) - Setting Boundaries to Protect Your Energy and Focus Summary: zero click marketing won the strategy war, and now nobody can prove it's working. Amanda has spent over a decade building marketing that lives where the audience already is, and in this episode she takes apart the measurement crisis that creates. We get into why your attribution dashboard is quietly lying to you, how a viral story about HubSpot losing 80% of its organic traffic actually hid record revenue, and what a 25 million dollar ad blackout taught Dropbox about the gap between credit and cause. She also shows how to turn audience research into an operational system and why winning AI visibility comes down to writing genuinely good stuff. Stick around for the launch-week playbook and the overslept-webinar story that completely reframes how she guards her time. About Amanda Natividad Amanda Natividad is the Chief Evangelist at SparkToro, the audience research startup, and the founder of Zero Click Marketing, a podcast and consultancy built around the framework she co-created with Rand Fishkin in 2022. She spent 4 and a half years as SparkToro's VP of Marketing, where she launched a newsletter that reaches more than 60,000 subscribers at a 35% open rate and built Office Hours, a webinar series that pulls as many as 1,200 registrants a show. She's keynoted at AdWorld, Content Marketing World, and MozCon, and guest lectured at Columbia, Cornell, and Stanford. A Le Cordon Bleu-trained chef and former journalist, she now teaches Content Marketing 201 on Maven. Why Attribution Breaks Down in a Zero Click World Every marketing ops team runs on a dashboard that hands out credit. This lead came from paid search. That demo came from a LinkedIn ad. This signup traces back to the nurture email. The numbers look authoritative, and leadership treats them that way. The trouble is the data underneath has been eroding for years, and most teams still read the output like scripture. Amanda has spent more than a decade building marketing programs that don't depend on the click. Her take on measurement starts from an uncomfortable place. Attribution was never as precise as the industry sold it, and every year it gets less precise. 4 separate forces have chipped away at what attribution can actually see, and they stack on top of each other. Third-party cookies barely function. Only about 30% of users accept them, and Safari rejects them by default., Ad blockers hide a huge share of traffic. Somewhere between 20 and 60% of people run one, and among tech-savvy B2B audiences that number climbs toward 60%., The multi-device journey is untrackable pre-login. People average 3.6 devices each, so stitching a single human across all of them is mostly guesswork., Privacy regulation makes persistent tracking impractical. GDPR, CCPA, and LGPD mean what's legal in the US often isn't legal anywhere else, a real burden for any team with a global audience. None of this means you rip attribution out of the stack. In a mature organization it's already there, already wired into the reports leadership reads, so ignoring it would be its own kind of malpractice. The shift Amanda argues for is one of posture. Go in knowing exactly where the model goes blind, then ask the more useful question of what you can measure next to fill the gaps. That's where incrementality, media mix modeling, geo-testing, and holdout tests start to earn their place. The teams that keep their budgets in a down market are the ones who stopped presenting attribution as ground truth and started presenting it as one flawed witness among several. A single confident number is easy to attack. A converging set of imperfect signals is much harder to argue with. Key takeaway: Audit where your attribution model goes blind before your next leadership review. Write down how much of your traffic Safari blocks, how many of your B2B visitors run ad blockers, and how many touchpoints happen before anyone logs in. Bring that context into the room so a drop in tracked conversions reads as a gap in measurement, not a failure in marketing. Why Dark Social Traffic Shows Up as Direct Open Google Analytics on any given week and a fat slice of your traffic sits in a bucket labeled direct. The polite interpretation is that all those people typed your URL straight into the address bar. Almost none of them did. Most of that direct traffic is dark social, the shares that happen inside messaging apps and closed platforms where the referral information never makes it back to you. SparkToro put real numbers on it. About 2 years ago the team ran an experiment, sending more than 1,100 visits across 11 social networks and then checking what Google Analytics reported. For TikTok, Slack, Discord, WhatsApp, and Mastodon, every single visit landed in direct. Amanda has a theory about why, and it follows the money. The platforms can see the organic referral string. They keep it invisible, because the moment you pay to join their ad network, that traffic suddenly becomes visible and measurable. Organic reach stays in the dark so paid reach looks like the only reach worth buying. And it goes well past the obvious suspects. Facebook Messenger strips the referral about 75% of the time, Instagram DMs about 30%, and even LinkedIn hides it roughly 14% of the time. A share is a share whether it happens in a feed or a private message, but only some of them ever get counted. The practical lesson for an ops team is to stop treating the direct channel as a junk drawer. When word of mouth and private sharing drive a real share of pipeline, a measurement model that files all of it under direct is quietly erasing your best-performing channel. The brands that figure this out start asking where conversations about them actually happen, instead of waiting for a clean UTM that the platform was never going to hand over. Key takeaway: Run your own dark social test before you trust the direct bucket. Push a known batch of clicks through TikTok, Slack, and a few DM channels, then watch how your analytics file them. Use the gap to set expectations with leadership, and lean on post-purchase survey questions like "where did you first hear about us" to recover the attribution the platforms refuse to share. What the Alligator Graph Means for Ops Teams There's a chart making the rounds that content marketers have started calling the alligator graph. Impressions climb while clicks to your website fall, and the 2 lines drift apart until they look like an open set of jaws. For a content team this reads as proof the strategy is working, because more people are seeing the brand. For a marketing ops team staring at the same chart in Google Analytics, it reads as failure, because the dashboard they own is built to count clicks and the clicks are going down. The measurement layer is undercutting the exact thing the content layer is producing. Amanda's first move is to reframe the problem. The j...

    • Transcript
    • Chapters
  • July 21 · 1 hr 6 min

    229: The Privacy lawsuits coming for every marketing team, with Cara Caruso and Dustin Taylor

    What's up everyone, today we have the pleasure of sitting down with Cara Caruso, CEO and co-founder of Sentinel Insights, and Dustin Taylor, counsel at Troutman Pepper Locke. We'll cover: (00:00) - Cara-audio (00:53) - In This Episode (04:40) - 1 — Why Your Website Is Running More Tracking Tools Than You Know (08:17) - 2 — How a Plaintiff's Firm Turns Your Website Into a Lawsuit (17:55) - 3 — The ECPA Wave and the Privacy Policy That Sues You (20:53) - 4 — What Consent Drift Actually Looks Like (25:16) - 5 — Who Actually Owns Privacy Compliance (30:49) - 6 — The Business Case for Privacy-First Marketing (35:52) - 7 — What Marketing Ops Can Do About Privacy This Week (42:35) - 8 — Why Email Marketing Is the Next Privacy Lawsuit (48:04) - 9 — AI, Consent, and Being Forced to Delete Your Data (51:46) - 10 — Why Small Companies Get Privacy Lawsuits Too (58:42) - 11 — How to Decide What Deserves Your Energy Summary: A privacy-software CEO and a litigation defense attorney walk into a podcast and proceed to scare every marketer in the room, in the most useful way possible. Cara Caruso has scanned over 10,000 websites and found nearly 90% of them non-compliant, while Dustin Taylor has defended more than 100 companies against the exact lawsuits that follow. Together they trace how a forgotten tag from three years ago becomes a seven-figure settlement, why your own privacy policy is the document most likely to sue you, and how one month of a broken cookie banner turns into 10,000 dollars per visitor. Then they flip the whole thing and make the case for why clean, consented data actually performs better. Stick around for the part where your email open rates might be illegal and the FTC makes companies delete four years of data. About Cara Caruso and Dustin Taylor Cara Caruso is the CEO and co-founder of Sentinel Insights, where she leads a platform that monitors websites in real time for consent violations and privacy exposure. Before starting the company she spent more than 25 years in data and martech, building and scaling teams across technology and financial services in both B2B and B2C. She pairs strategic planning with hands-on execution, and she's also a certified yoga instructor who has been known to bring a workshop into the office. Dustin Taylor is counsel at Troutman Pepper Locke, where he defends companies at the intersection of privacy law and marketing technology. He's defended more than 100 companies in ad-tech privacy cases involving cookies, pixels, session replay, and website chat, secured dismissals at the pleading stage in federal court, and argued in California, New York, Florida, Texas, and New Jersey. He started out with an advertising degree before law school, which makes him fluent in the martech stack in a way most litigators never are. He also publishes monthly privacy litigation reports and tracks ECPA filings with AI. Why Your Website Is Running More Tracking Tools Than You Know Most marketing teams believe they have a clean inventory of what runs on their website. There's a tag manager, a cookie banner, a vendor list in a spreadsheet somewhere, and a general sense that someone signed off on all of it. Then someone actually scans the site, and the number comes back two, three, sometimes four times higher than anyone expected. Cara has watched this play out thousands of times. Sentinel Insights has scanned over 10,000 websites in the past year, and the pattern barely changes from one company to the next. Nearly 90% of those sites are not compliant. Every new customer gets the same uncomfortable conversation on day one. The gap between what a team thinks is running and what's actually firing comes from two places. The first is history. Somebody three years ago added a tag for a campaign that ended, then left the company, and nobody ever took it down. The team you have today inherited a stack built by people they never met, and most of those decisions were never written down anywhere. Cara calls it the ghost of marketers past, and it's sitting on almost every site she scans. The second is piggybacking. You buy one tool, drop in one script, and that single tag quietly loads four more. Each of those can load more on top. An agency hard-codes a pixel straight into a landing page because they didn't have access to the tag manager, and now your customer data flows to companies nobody on your team could name. None of it shows up in the tidy spreadsheet. All of it shows up in a scan. This is the part most marketers underestimate. The real exposure comes from the tools nobody chose on purpose, the dozen scripts running quietly in the background, each one sending customer data somewhere you've never audited. They pile up while everyone assumes the banner has it covered. No marketing team actually knows what's on its website until a scan proves otherwise, and "we reviewed it last year" is closer to a guess than a control. Why Not Knowing Is Not a Legal Defense The instinct, once you find those orphaned tags, is to assume they don't count against you. You didn't install them. You didn't even know they were there. Dustin spends a lot of his time correcting that assumption. These privacy laws do have a knowledge component, but courts read knowledge very differently than a normal person would. As long as the person who installed the tag three years ago knew they were installing something, the legal requirement is met. The fact that today's marketing team forgot it existed changes nothing. There's a second trap hiding inside the same problem. What marketing knew, what IT knew, and what legal knew are rarely the same thing, and that fragmentation is its own risk. Each group assumes another group is watching the stack. The court doesn't care which department dropped the ball. It only cares that someone, at some point, hit install. Key takeaway: Run a full scan of your live website this month and compare the results against your documented vendor list. Flag every tag you can't immediately explain, especially anything loading third-party scripts you never installed directly. The tools you can't account for are the ones quietly building your legal exposure, and forgetting they exist won't protect you. How a Plaintiff's Firm Turns Your Website Into a Lawsuit Here's the mental model most marketing teams carry around: we're compliant until someone complains. You picture a single annoyed customer who takes the time to file something against your little startup, and you quietly decide the odds are low. Who's really going to sue over one text message or one tracking cookie? That assumption is the most expensive thing on your website, because litigation in this space doesn't start with a complaint. It starts with a scan of your site that you never see. Plaintiffs' law firms run continuous automated audits of company websites, and they're looking for far more than a typo in your privacy policy. Dustin walked through exactly what their scanners check: What loads automatically the moment someone lands on the page, before any consent is given, Whether there's a banner at all, and what it actually does, What keeps firing after a visitor opts out, Whether anything is miscategorized, like a marketing cookie quietly labeled "essential" so it can't be turned off Once a firm finds the exposure, they find a plaintiff. The demand letter arrives, and the dollar amounts climb fast. Cara breaks the pressure into three forces bearing down on marketing teams at once: Trophy-hunting plaintiff attorneys who come after you for small amounts individually, then scale it into a class action, Brand and reputational damage that lingers long after a settlement clears, State enforcement at the atto...

    • Transcript
    • Chapters
  • July 14 · 1 hr 1 min

    228: The Dispatch tower (The Dungeon of martech architecture, part 4)

    What’s up folks, welcome to our 4 part series of crawling through the dungeon of martech architecture. You’ve arrived at our final episode, part 4: The Dispatch Tower. We’ll tackle: (00:00) - Intro (00:59) - In This Episode (01:30) - Sponsor: Mammath Growth (02:33) - Sponsor: GrowthLoop (03:58) - FLOOR 4: THE DISPATCH TOWER (04:39) - What Happens When 30 Vendors Turn On AI at the Same Time (21:24) - Boss Battle: The Agent Avalanche (22:53) - Why You Need a Central Referee, With Agent Guardrails (26:48) - Sponsor: Knak (27:55) - Sponsor: MoEngage (28:52) - Privacy Compliance and Data Minimization (38:38) - The Dispatch Layer that Controls Routing Logic (44:00) - The Layer Above the Dispatch is the Interface (47:20) - What Production AI Agents Look Like on a Governed Data Foundation (50:27) - The Deterministic Layer: Why the Interface Can't Run on Guesses (53:09) - System Stewardship: The Role That Emerges When You Clear This Floor (59:33) - FINAL ACHIEVEMENT: The Dungeon Is Cleared --------------------------------------------------------------------------- OPENING --------------------------------------------------------------------------- Welcome back to the Dungeon of Martech Architecture. You've arrived at the final floor. Parts 1 through 3 built the foundation, the meaning layer, and the causal memory. Part 4 is the floor most organizations have been living on the whole time, often without knowing it. Episode 1: CRM Gravity We conquered the source of truth and discovered that the data warehouse replaces the CRM with portable audiences. Episode 2: The Eye of Context We learned why AI fails without context engineering and built the shared meaning infrastructure that keeps agents from misinterpreting what they read. Episode 3: The Correlation Masquerade We escaped the correlation trap and built the causal memory layer that separates agents that optimize correctly from agents that confidently scale the wrong behavior. Episode 4: The Dispatch Tower Today, we tackle the governance chaos of 30 vendors all claiming authority, and confront the interface decision that most organizations already made without realizing it. Let's finish our descent. --- --------------------------------------------------------------------------- FLOOR 4: THE DISPATCH TOWER --------------------------------------------------------------------------- Congratulations martech crawler, you’ve arrived to the final floor of the dungeon. Let’s face it, most of your fellow crawlers will never make it this far. They’re still lost in political battles debating why the CRM shouldn't hold product activation data or they ended up falling prey to the correlation trap and I forever stuck in the boomerang room. But you’ve made it. You’re proudly wearing the 3 badges of honor on your jacket: data janitor, context king and causal brainiac. This floor is the hardest because it combines collaborative political battles but some serious technical obstacles as well. The layout of this floor is best described as a hot mess of agent spaghetti. Every vendor in your stack has an agent now. Your ESP has one. Your CDP has one. Your MAP has one. Your CRM has one. Your ad platform has one. Each one has separate logic, separate assumptions, separate permissions, and a roadmap slide that says “autonomous.” None of them knows what the others are doing. And nobody volunteered to referee this stuff. At the top of this floor, there is a door most organizations never noticed. Whoever controls that door controls what instructions get through, which systems can act, and which agent gets authority over the customer. What Happens When 30 Vendors Turn On AI at the Same Time Your inbox is probably already full of emails from every platform in your stack, all saying some version of “Turn on AI today.” Each one promises autonomous intelligence. Each one wants permission to write, recommend, segment, suppress, score, enrich, personalize, or trigger something. Each one is operating from its own model of the customer. None of them can explain what happens when two agents try to update the same record, message the same person, or optimize against conflicting goals. You are now the referee. Congratulations. Rich Waldron, CEO of Tray.ai, describes how this feels on the ground: RICH WALDRON, Episode 162 "If you're the marketing ops leader and your 30 vendors are telling you to go turn on the AI for their application, you sort of become like an AI referee. You have to figure out, well, do I turn it on for this one and not for this one? And is this one gonna overwrite what occurs here? You feel it already. Your inbox floods with vendors begging you to 'just turn on our AI capability' -- 30 different platforms all promising transformation. Suddenly you're an unwilling AI referee asking impossible questions: which AI systems should I activate first? What happens when one AI's decisions contradict another's? Are all these systems feeding sensitive data into the same models?" "Agents backed by an iPaaS give you full governance and control of where execution happens. For teams drowning in vendor AI chaos, the third option -- iPaaS-backed agents that naturally integrate with everything -- provides centralized governance, unified control points, and consistent execution environments." Rich is describing the nightmare version of the final floor: every vendor ships an agent, and marketing ops gets stuck deciding which ones are safe, which ones are redundant, which ones are risky, and which ones quietly conflict with each other. The answer is not to let every team turn on whatever they want. It is also not to create a central AI police force that blocks everything until innovation dies in a committee. The middle path is an operating model: a small central group that sets standards, reviews risk, creates reusable patterns, and lets the business move quickly without turning the stack into a haunted appliance store Funny enough, I spoke with someone who actually did volunteer for that AI referee job – her title is a bit fancier though. Here’s Lindsay Rothlisberger, Director of GTM Innovation at Zapier and also a member of their AI Center of Excellence. At Zapier, she sits at the spoke of a hub-and-spoke AI Center of Excellence, a small central team led by a Chief AI Officer, with Lindsay as the GTM representative responsible for translating governance into go-to-market practice. Her work covers deciding which AI tools get activated, setting the standards for what a shared skill has to pass before it enters the internal library, and keeping track of what people are building before it fragments into 100 unsanctioned experiments. LINDSAY ROTHLISBERGER, Zapier "We have a skill that reviews our skills for data and security compliance. It gives you a red, yellow, green. Red — here's what you definitely need to fix. And in most cases, Claude can just say, 'I can go fix that for you. Would you like me to do it?' And just do it. So it's a pretty simple process. But it is a standard review skill that runs through all of these different things." Every shared skill at Zapier has to have a named owner. If it's used across a team, someone is accountable for maintaining it. Skills covering go-to-market operations are owned by RevOps. New builders who want to ship a skill that runs through a cross-functional workflow pair with a RevOps partner to build it. The governance operates as a co-authorship model, designed to make s...

    • Transcript
    • Chapters
  • July 7 · 1 hr 4 min

    227: The Correlation masquerade (The Dungeon of martech architecture, part 3)

    What’s up folks, welcome to our 4 part series of Crawling through the dungeon of martech architecture. You’ve arrived at Part 3: The Correlation Masquerade. We'll cover: (00:00) - Intro (01:16) - In This Episode (01:36) - Sponsor: Knak (02:44) - Sponsor: MoEngage (04:02) - FLOOR 3: THE CORRELATION MASQUERADE (05:08) - Why Agentic AI Optimizes for the Wrong Thing at Scale (11:30) - The Boomerang Effect on AI that Erodes Revenue (20:40) - Why Marketing Attribution Data Can’t Tell AI Agents What Actually Caused the Result (28:28) - Sponsor: Mammoth Growth (29:31) - Sponsor: GrowthLoop (33:56) - How Bad Signals Masquerade as Evidence (42:27) - BOSS BATTLE: The Correlation Boomerang Archer (43:27) - Reducing Exposure While the Foundation Is Built (49:07) - Building a Causal Memory Layer With a Context Graph (01:02:15) - Achievement unlocked: Causal Evidence Layer Established --------------------------------------------------------------------------- OPENING --------------------------------------------------------------------------- Welcome back to the Dungeon of Martech Architecture. You've arrived at part 3. If you're just joining, go back to parts 1 and 2, where we demoted the CRM, built the warehouse, engineered the context layer, and built the shared meaning infrastructure that keeps agents from misinterpreting what they read. Episode 1: CRM Gravity We conquered the source of truth and discovered that the data warehouse replaces the CRM with portable audiences. Episode 2: The Eye of Context We learned why AI fails without context engineering, built the shared meaning infrastructure, and dug into why the industry built the wrong kind of meaning infrastructure in 2012. Episode 3: The Correlation Masquerade Today, we escape the correlation trap and build the causal memory layer that separates agents that optimize correctly from agents that confidently scale the wrong behavior. Episode 4: The Dispatch Tower Next, we tackle the governance chaos of 30 vendors all claiming authority, and confront the interface decision that most organizations already made without realizing it. Let's continue our descent. --- Okay so we’re making our way down to the third floor with blood sweat and tears. But we’re feeling good. Our data is clean-ish. You’ve built a context bundle that we’re proud of and we collaborated on it with multiple people and shared definitions. We’ve got a nice big fancy data warehouse as our source of truth. Our warehouse holds a complete record of what happened. We can query patterns, correlations, historical campaign data, audience behaviors, outcome signals: all of it is available. But the problem we’re about to find out is that none of what we’ve built so far can tell an agent whether the thing it's optimizing for was ever the right thing to optimize for. None of it explains why an intervention worked, or whether it worked for the reason the model assumes it did. Let’s step through. --------------------------------------------------------------------------- FLOOR 3: THE CORRELATION MASQUERADE --------------------------------------------------------------------------- The layout of the correlation masquerade is like a high speed train to nowhere. You’ve spent two whole floors meticulously cleaning the “atoms” of your historical customer data and building a sturdy warehouse so that you can let AI and agents loose on the data. Maybe you’re starting to play with ‘next best action sequences’, building propensity models predicting the likelihood that certain cohorts of users will churn, maybe running reinforcement learning loops on historical context and doubling down on your best campaigns. Everything looks like it's working... until the world rumbles and you realize you're still in a trap. The layout of this floor is a room where every single action comes back wrong. And it’s not technically AI’s fault, they’re just optimizing for a finish line that’s actually just the end of the first heat of 8 heats. It’s a trap. Why Agentic AI Optimizes for the Wrong Thing at Scale Jason Dobbs, the Head of Marketing Ops and GTM Engineering at Kumo describes it like this: JASON DOBBS, Kumo AI "A warehouse is a record of what happened. It's not a rulebook for what an agent should do next. If you let a generic agent optimize directly on historical correlations with unbound authority, you can absolutely scale the wrong behavior. A product that correlates with high LTV does not necessarily cause high LTV. Prediction is not policy. Once you cross into action, you still need guardrails, business rules, approvals, evidence that it's actually driving business outcomes." An AI system that treats prediction as their gold standard will happily optimize for proxies while the actual outcome you care about degrades. The Prediction Trap Tobias Konitzer spent years studying this failure as a computational social scientist before bringing that lens to marketing. His argument is that predictive models describe what's already happening, and marketing is about changing what's going to happen. Those are different jobs, and the data warehouse doesn't distinguish between them by default. TOBIAS KONITZER, Episode 212 "The nature of predictive models is they represent the status quo. Someone is going to churn, or someone is not going to churn, but that is the status quo. And there is really no point for marketing if everything is just status quo. There is no marketing role here." He describes a CRM head at a billion-dollar outdoor brand who found that high LTV customers had a strong correlation with viewing a specific product, a pair of jeans. The obvious response was to push those jeans into the welcome flow. The correlation ran in the wrong direction, though. Those customers already had high LTV before they saw the jeans. The jeans showed up alongside the relationship, long after it was established. Scaling that logic into the welcome flow pushed irrelevant products onto a broad cohort with nothing in common with the original high-LTV segment. The analysis was reproducible and data-supported; it just never verified whether the jeans caused the high LTV or merely accompanied it. An agent running the same logic would scale the error across every customer who matched the surface pattern, efficiently and invisibly, before anyone stopped to ask. Tobias calls this lazy thinking, and risky thinking: analysis that feels like rigor because it's data-supported and reproducible, but skips the question that would disqualify the conclusion. The Boomerang Effect on AI that Erodes Revenue When you let agentic AI loose on the data warehouse it has access to a TON of data. That's amazing. But raw data and then events and actions often leads to predictions that are based on correlation, and not causation. For example, let's say you task an agent with improving the number of free users that convert to paying users. The agent sees in the past that a discount campaign to a certain cohort of active free users on a certain day resulted in a high % of paying user conversions. The agent concludes: this campaign works. Scale it. But what it can't see: those active free users were already the most likely to convert, they were on the edge of paying with or without the discount. The campaign didn't cause the conversions. It just correlated with them. The agent found the easiest pattern in the data and called it a lever. Run that campaign at scale and a few things happen. You hand discounts to users who would have paid ...

    • Transcript
    • Chapters
  • June 30 · 1 hr 2 min

    226: The Eye of context (The Dungeon of martech architecture, part 2)

    What’s up folks, welcome to our 4 part series of Crawling through the dungeon of martech architecture. You’ve arrived at Part 2: The Eye of Context. We cover: (00:00) - Intro (00:56) - In This Episode (01:28) - Sponsor GrowthLoop (02:32) - Sponsor: GrowthBench (03:32) - Welcome Back (04:09) - FLOOR 2: THE EYE OF CONTEXT (06:15) - Why AI Produces Believable Nonsense (09:00) - BOSS BATTLE: The Hallucination Oracle (10:07) - Data Quality: When Agents Read Your Messy Data (22:33) - Context Engineering: What It Is and Why It's Not the Same as Prompt Engineering (24:28) - Sponsor: MoEngage (25:25) - Sponsor: Knak (26:30) - Context Eng vs Prompt Eng (38:58) - Why the Industry Built the Wrong Semantic Layer in 2012 (46:33) - How Context Rot and Fragmentation Break AI Agent Performance (49:59) - BOSS BATTLE: Rotten Context Mage (50:37) - How to Build a Shared Context Layer for AI Agents (58:17) - Testing Whether Your Context Layer Works (01:01:35) - NEW ACHIEVEMENT: The Meaning Layer Is Live --------------------------------------------------------------------------- OPENING --------------------------------------------------------------------------- Welcome back to the Dungeon of Martech Architecture. You’ve arrived at part 2. If this is your starting point, check out part 1 where we cleared the first floor’s boss in 2 forms: The False Truth King in the CRM, and The Export Hydra that spread it everywhere. That said, if you already have a data warehouse, you might be able to start right here. Episode 1: CRM Gravity We conquered the source of truth and discovered that the data warehouse replaces the CRM with portable audiences. Episode 2: The Eye of Context Today, we learn why AI fails without shared meaning, build the context engineering layer, and dig into why the industry built the wrong kind of meaning infrastructure in 2012. Episode 3: The Correlation Masquerade Next, we escape the correlation trap and build the causal memory layer that separates agents that optimize correctly from agents that confidently scale the wrong behavior. Episode 4: The Dispatch Tower Then, we tackle the governance chaos of 30 vendors all claiming authority, and confront the interface decision that most organizations already made without realizing it. Let’s start our descent. --------------------------------------------------------------------------- FLOOR 2: THE EYE OF CONTEXT — AI Hallucinations, Data Quality, and Context Engineering --------------------------------------------------------------------------- The layout of the second floor down the dungeon of martech architecture actually looks pretty fancy. It’s cozy, it looks modern, the whole palace is lined with mirrors. But it’s a bit creepy because once you look a little closer at the reflections, you notice that some of the details are off. The boss on this floor is low key danger that sneaks up on way too many teams – not like the big flashy monsters from the past 2 floors. Let’s say you have a new AI system running on your marketing data. You’ve got it producing stuff like scores, recommendations, campaign ideas. Initially, it actually looks solid. There’s no obvious AI sentence structures in the summaries, they read well. The scores next to accounts seem to make sense: higher ones next to well known brands and lower ones are gmail accounts. The campaign ideas are actually pretty fresh, you can tell that it’s tailored for your ICP. Every output is delivered with impeccable confidence. So the next step is asking yourself… how would you know if it was wrong? There’s a lot of obvious hallucinations that you probably catch when you chat with GPT or Claude, like totally inventing stuff. I’m talking about the details. The kind of wrong that passes the first glance. We’ve all seen that viral post on r/analytics about a company that found out AI has been making up analytics data for 3 months. Whether this post is from a real story or not, some versions of this are happening inside companies today. But this is happening everywhere right now. Even at some of the top AI companies on the planet. I’ve talked to technical marketing leaders that have greenlit agentic tools at their startups before the data definitions were settled. One of them called it ‘believable nonsense’, and that term kinda stuck with me. It’s the most dangerous form of hallucination, it sneaks up on you. This floor is harder than the last because the traps on the previous level were visible, once you knew what to look for: CRM exports nobody trusted, audience logic duplicated across platforms, copies drifting from the original. You felt that, you saw that. This floor’s failures are designed to look like success, until you dig into the details and look under the hood. Let’s look at the origins of The Hallucination Oracle boss. Why AI Produces Believable Nonsense Humans are wired to trust smooth, confident talkers. It’s actually baked into our evolution and how our brains develop from infancy. Studies on babies and brain scans show this is an innate thing that kicks in early. A person who sounds certain usually knows something, right? LLMs and AI systems break this calibration, they produce fluency without necessarily producing correctness. At first glance, the output sounds right for structural reasons, not evidential ones. And once something sounds right, we engage with it differently. We forward it. We build on it. We present it to stakeholders who don't have the context to question it. This is similar to the The False Truth King boss from the first floor in episode 1, but this is at the intelligence layer: output that has been processed, synthesized, and returned with all the structural markers of a trustworthy answer, but the problem is the reasoning underneath it is hollow. That’s Jason Dobbs, Head of Marketing & GTM Engineering at Kumo. He greenlit agentic analytics and predictive workflows at his startup before the team had settled on shared data definitions. The system produced outputs that looked reasonable right up until someone started asking follow-up questions: JASON DOBBS, Kumo AI "What made it dangerous wasn't really like the obvious hallucination. What we were seeing looked polished enough to be operational. At first glance the scores looked precise, the summary sounded coherent, the recommendations felt data-backed. But the moment you ask the simple follow-up questions -- why did you choose this account? What data drove this decision? -- the logic started to thin out. The lesson wasn't that the data was bad or the warehouse was bad or the model was bad. What was wrong is we were trying to automate ambiguity. We were asking AI to solve for confusion that we hadn't yet ourselves solved for internally. And once you do that, you enter the danger zone because the failure is essentially believable nonsense." This is some scary stuff right? When this believable nonsense gets trusted long enough to make it into a campaign, a decision, a board slide. Obvious hallucinations are easy to catch. Confident, polished, data-backed nonsense gets through more often than we think. The good news is that we already have a weapon perfect to slay this boss, we shaped it in the last episode: the data warehouse. It houses data. Data is how we defeat believable nonsense… but we need to enhance it. --------------------------------------------------------------------------- BOSS BATTLE: The Hallucination Oracle ----------------------------------------...

    • Transcript
    • Chapters
Showing 1–20 of 33 episodes