This set builds vocabulary for measuring user cohorts, retention, and acquisition sources.
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At standup, a dev mentions grouping users by shared characteristics, like signup date or plan tier, to compare their behavior over time. What is this analysis technique called?
Cohort analysis groups users who share a characteristic, like signing up in the same week or being on the same plan tier, and tracks how their behavior, such as retention, evolves over time relative to other cohorts. This reveals trends that an aggregate, ungrouped metric would obscure. It's a foundational technique in product analytics platforms for understanding how changes affect different user segments differently.
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During a design review, the team wants to measure the percentage of new users still active 30 days after signup. Which metric are they defining?
Retention rate measures the percentage of users who remain active a defined period, like 30 days, after a starting event such as signup, directly reflecting whether a product is delivering ongoing value rather than just acquiring one-time users. This is one of the most closely watched metrics in product analytics because it correlates strongly with long-term business health. It's typically visualized alongside cohort analysis to see how retention trends shift over time.
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In a code review, a dev configures an event property capturing which marketing campaign referred a given user at signup. What does this enable?
Capturing a property like the referring campaign at signup links a user's later behavior, such as conversion or retention, back to the specific acquisition channel that brought them in. This attribution lets the team evaluate which marketing channels actually produce valuable, retained users rather than just raw signups. It's a common integration point between marketing and product analytics data.
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An incident report shows a retention chart looked artificially high because bot traffic was being counted as active users. What practice would prevent this?
Filtering out known bot or automated traffic before computing a retention metric prevents non-human activity from inflating the numbers and giving a misleadingly rosy picture of actual user engagement. Without this filtering, decisions based on the metric could be built on a false premise. This data-quality step is a standard part of setting up trustworthy product analytics.
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During a PR review, a teammate asks why the team analyzes cohorts instead of just looking at a single aggregate active-user count. What is the reasoning?
A single aggregate active-user count can mask the fact that one segment of users is thriving while another is churning, since the numbers blend together into one overall figure. Cohort analysis separates users into meaningful groups so those diverging trends become visible. This distinction matters most when trying to diagnose why an overall metric is moving in a particular direction.