How To Better Understand Your Users
The speaker advocates for using dot plots—a two-dimensional visualization showing individual user behavior over time—to understand how users actually interact with products, rather than relying solely on aggregate metrics like DAUs. Dot plots reveal usage patterns, feature adoption, and retention issues that aggregate data masks, and can scale from small user bases to millions of users through sampling.
Summary
The speaker argues that founders commonly make the mistake of relying on aggregate user metrics (DAUs, MAUs) instead of understanding how individual users interact with their products. Aggregate graphs tend to show upward trends even when users aren't genuinely engaged, masking critical behavioral insights.
The speaker introduces the dot plot as a solution: a two-dimensional grid where rows represent individual users and columns represent time periods (typically days). A dot is placed in each cell when a user performs a key value-creating action (like listening to a song in Spotify or sharing a photo). The first day a user onboards can be marked with a special symbol (like a ring around the dot) to track initial engagement.
When visualized as a dot plot, patterns emerge that aggregate metrics completely obscure. The speaker's Spotify example reveals that some users engage on weekdays while others use the product only on weekends—an insight invisible in DAU graphs. Similarly, the visualization quickly exposes one-time users who never return, signaling onboarding problems.
The tool can be made more sophisticated by using different symbols to represent different features or events, and by encoding user attributes (device type, location, demographics) through color, shading, or annotations. Rows can be sorted by these attributes to compare cohorts.
For B2B products, dot plots prove equally valuable. The speaker describes a YC company with an $80,000 customer contract that churned after the product champion left the company. A dot plot would have immediately revealed that only 3 of 10 purchased seats activated and usage was sparse and inconsistent—red flags about contract health that aggregate renewal data wouldn't catch.
The speaker warns against two common mistakes: charting low-value events (like app opens) instead of genuine value-creation events, and using time periods that are too wide (weeks instead of days), which obscures actual behavior patterns.
Dot plots scale from small user bases (fitting on one screen) to billions of users through sampling—the speaker describes practices at Google Photos where teams would print dozens of dot plots representing different user segments (iOS users in France, US web users earning $80K+) and analyze patterns manually.
The speaker positions dot plots and cohort retention curves as complementary tools: retention curves show in aggregate whether user cohorts stick around, while dot plots reveal how users actually behave within the product, directing founders toward better questions and features.
About this episode
<p>Most founders obsess over dashboards and aggregate metrics, but some of the best product insights come from understanding how individual users actually use their product.</p><p><br /></p><p>In this episode of Startup School, YC's David Lieb walks through one of his favorite tools for better understanding your users, the dot plot. It's a simple two-dimensional grid that reveals usage patterns no aggregate chart can show you.</p><p><br /></p><p>He’ll cover why it gives founders a better sense of product health, what patterns to look for, and real-world exam</p>
Key Insights
- The speaker argues that aggregate metrics like DAUs and MAUs obscure individual user behavior and can show growth curves even when users are not genuinely enjoying the product.
- The speaker claims that dot plots reveal behavioral patterns—such as weekday versus weekend usage preferences—that are entirely invisible when looking at aggregate data or individual user logs separately.
- The speaker demonstrates that dot plots enabled early detection of at-risk contracts in B2B products by showing that an $80,000 customer had only 3 activated seats out of 10 and sporadic usage, which predicted the eventual churn after the product champion left.
- The speaker asserts that choosing the right value-creating event (like 'listen to a song' rather than 'open app') is critical to dot plots' effectiveness, as low-value events generate many dots that create a false sense of engagement.
- The speaker contends that dot plots scale from small products to billion-user platforms like Google Photos through strategic user sampling, allowing teams to print and analyze specific demographic or geographic cohorts rather than requiring real-time dashboards.
Topics
Transcript
One of the biggest mistakes I see founders make is relying on aggregate user metrics instead of understanding how any individual users use their product. In my last video, I talked about cohort retention curves and how you can use those to separate groups of users and track what they do over time throughout using your product. And I think that's the best tool that you've got to figure out if people keep using your product. But what you don't know is how are they using your product? How are they interacting? What features are they using? What's the frequency of use? What's the pacing of how they use the product? And most founders just like ignore this, but I…
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