Humans of Martech

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...

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

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...

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...

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 ...