Key Takeaways

  • Tom Verrilli, CPO of Whatnot, admits his most common career blunder is over-reliance on average product metrics.
  • He cautions that while a feature might show only 3% overall usage, it could be 100% of the core functionality for a specific, critical user cohort.
  • Deprecating features based on low average adoption risks "blowing up" the essential experience for these loyal groups, especially in complex marketplace businesses.
  • Verrilli uses the analogy of a “Westfield Mall turning off the power in the lead-up to Christmas” to highlight the real, downstream impact on people's livelihoods.
  • At Whatnot, his team aims for a "batting 500" success rate, openly embracing the reality that many product decisions, often based on averages, will be wrong.

The Lie in the Middle

Tom Verrilli, CPO of Whatnot, doesn't pull punches when reflecting on his biggest professional screw-ups. His "Fail Corner" confession from Lenny's Podcast cuts straight to the core of a pervasive product management blind spot: the deceptive comfort of averages. As Verrilli puts it, "Averages mean nothing to the individual is probably the thing that I've like really scarred by."

He recounts a common scenario: you see a feature with only 3% adoption across your entire user base. Your first instinct might be to ditch it, labeling it underused. But Verrilli warns, “if you don't go a layer deeper... there's a group of people for whom it's 100% of what they do. This is their core use case.” For expediency, or to cut maintenance costs, you deprecate it. “And then it turns out you like blow up the use case of that group of humans.” It's a decision he’s made that led to “genuinely like I'm disappointed in myself levels of decisions” because he relied on aggregate data without understanding the individual human stories underneath.

When Averages Kill Livelihoods

This isn't just about small product tweaks; it's about real-world consequences, especially in platform and marketplace businesses. Verrilli thinks about it explicitly in an e-commerce context. “This is somebody's business,” he states. Imagine a seller whose entire workflow hinges on that "3% feature." Removing it isn't an inconvenience; it's a catastrophic operational failure.

He paints a vivid picture: “it's kind of like a Westfield Mall just turning off the power in the lead-up to Christmas without thinking about it.” The downstream impacts are not abstract; they hit people's income, their ability to run their operations, and their trust in your platform. These are the stakes when product leaders miss the forest for the trees – or, more accurately, miss the critical saplings for the average canopy. The data might tell you one story, but “they just lie to you all the time” if you don't dig in to find the outliers who are, in fact, your power users.

What to Do With This

Stop taking your product analytics at face value. Tomorrow morning, identify your last three proposed feature deprecations or low-adoption features. Instead of just looking at average usage, segment your data to find cohorts with abnormally high engagement for those specific features. If you find a segment using a 'low adoption' feature 100% of the time, immediately schedule qualitative interviews with 3-5 users from that group. Understand their core job-to-be-done and how your feature serves as their primary workflow. Before making any deprecation decision, quantify the specific, individual impact on these critical cohorts – not just the average user.