Show notes
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 GravityWe conquered the source of truth and discovered that the data warehouse replaces the CRM with portable audiences.Episode 2: The Eye of ContextWe 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 MasqueradeToday, 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 TowerNext, 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 ScaleJason 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 TrapTobias 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 RevenueWhen 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 ...