See How Customers Actually Use Your Product
The speaker explains dot plots, a visualization tool that displays individual user behavior over time by plotting user actions on a 2D grid. Unlike aggregate metrics like DAU/MAU, dot plots reveal usage patterns, feature adoption, and retention issues that are invisible in traditional dashboards.
Summary
The speaker identifies a critical mistake founders make: relying on aggregate user metrics instead of understanding how individual users interact with their product. While cohort retention curves show whether users stick around, they don't reveal how users actually engage with features or the frequency and pacing of their usage.
The dot plot is introduced as a simple but powerful tool: a 2D grid where rows represent individual users and columns represent time periods (typically days). Dots are placed in cells when users perform a meaningful value-creating action—like listening to a song in Spotify or processing an invoice. First-day usage can be marked with rings to identify onboarding cohorts. This visualization reveals patterns invisible in aggregate data: weekday vs. weekend usage patterns, one-time users who never return, feature correlations with engagement, and usage frequency distribution.
The speaker illustrates the limitation of DAU graphs using a Spotify example: a DAU chart showing fluctuation between 0-3 daily active users obscures the fact that specific users have consistent weekday listening habits while others use on weekends. The dot plot reveals this behavioral segmentation instantly.
Dot plots can be enhanced with symbols to track different features (S for search, P for playlists) or states (device type, geography, demographics). They scale from early-stage startups with few users to companies like Google Photos with billions of users through strategic sampling.
A B2B example demonstrates practical value: a company that churned an $80K contract had 10 seats but only 3 activated users with sporadic usage under 2 days/week. A dot plot would have revealed the champion-dependent adoption pattern before churn occurred.
Common mistakes include charting the wrong events (app opens instead of value-creating actions) and choosing time periods too wide (weeks instead of days). The speaker recommends dot plots as the primary dashboard until a company reaches hundreds of users, used in conjunction with cohort retention curves.
Key Insights
- Aggregate metrics like DAU and MAU can mask serious engagement problems because they lump all users together and tend to trend upward even when users aren't actually enjoying the product
- Dot plots reveal usage patterns—such as weekday versus weekend usage—that would be impossible to discover by examining aggregate charts or individual user logs independently
- Specific features like playlist joining can be causal to sustained daily engagement; dot plots allow founders to spot these feature-behavior correlations by examining the raw visualization
- B2B companies with seat-based pricing can use dot plots to detect champion-dependent adoption and predict churn risk before contracts expire, as demonstrated by the example where only 3 of 10 purchased seats were activated
- Founders commonly misuse dot plots by tracking the wrong events like app opens instead of value-creating actions, and by choosing time granularity too wide (weeks instead of days), which obscures actual usage patterns
Topics
Transcript
[0:09] 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 the pacing of how they use [0:40] the product? And most founders just like ignore…
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