Practise vocabulary for cohort analysis: cohorts, retention curves, the flattening tail, and revenue vs user retention.
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1 / 10
A group of users who signed up in the same period and are tracked together is a ___.
A cohort groups users by a shared start time so you can compare how each group behaves over its lifetime.
2 / 10
A chart showing what fraction of a cohort is still active over time is a ___ curve.
The retention curve plots active share against weeks/months since signup, revealing how well the product keeps users.
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When a retention curve levels off rather than dropping to zero, it has ___.
A flattening curve indicates a loyal core that sticks around; a curve heading to zero signals no durable retention.
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Cohort retention measured in dollars rather than active users is ___ retention.
Revenue retention weights each user by spend, so expansion in a cohort can offset losses, sometimes exceeding 100%.
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Comparing newer cohorts to older ones shows whether the product is ___ over time.
Stacking cohorts reveals trends: if recent cohorts retain better, product or onboarding changes are working.
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Code Review Comment: Sarah is reviewing a pull request for a new user onboarding flow. She notices the team is segmenting users by signup date to analyze retention. Which of the following best describes this grouping of users? cohort
A cohort represents a specific group of users defined by shared characteristics, most commonly their initial acquisition timeframe. This definition aligns with the scenario where the team is grouping users by signup date to track retention. Options A and B present misunderstandings about data tracking; option C is the accurate definition, while option D suggests an irrelevant segmentation strategy.
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Slack Message: Mark from Product says, "We need to see how our new feature adoption is trending for the 'June 2024' cohort. Let's pull a retention curve that shows what percentage of them are still active after 30 days."
Mark's message describes a cohort retention curve. This type of chart visually represents the proportion of users within a specific cohort who remain active over a defined period (in this case, 30 days). The request for data segmented by signup date reinforces that it's focusing on a cohort. Options A and B misinterpret the purpose of a retention curve; option D suggests a misguided approach to product analysis.
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PR Description: The team is preparing a pull request detailing changes to their user engagement model. They include the following statement: 'We observed a plateau in retention for the Q3 2023 cohort after 60 days, suggesting users are settling into the platform.' What does this indicate? a stabilization effect
The statement describes a stabilization effect. When a cohort retention curve plateaus after a certain period, it signifies that the initial drop-off rate has slowed down. This doesn't necessarily mean users are *leaving*, but rather they've reached a point where their engagement is no longer decreasing significantly – they're 'settling in'. Options A and B present incorrect interpretations; option D incorrectly frames the situation.
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Standup Update: During a daily stand-up, David reports, "We're tracking cohort retention based on revenue generated – that's 'dollar retention'. We've seen a higher dollar retention for the July 2024 cohort compared to previous months."
David's statement describes dollar retention. This approach measures the value retained by a cohort over time based on revenue generated, providing a more nuanced understanding of long-term user engagement than simply tracking the number of active users. It highlights that some users may remain engaged but not contribute significantly to revenue. Options A and B misinterpret the context; option D is an unnecessarily redundant statement.
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API Response: The analytics API returns data showing that cohorts acquired in Q1 2024 have a higher retention rate than those acquired in Q2 2024. What does this imply about the product? it is improving over time
Comparing newer cohorts to older ones allows us to assess whether the product's performance is *increasing* or decreasing over time. If newer cohorts exhibit higher retention rates than older ones, it indicates that the product has become more engaging and valuable for new users. This suggests an improvement in the product's value proposition. Options A and B present flawed assumptions; option D reflects a negative trend.
What will I learn from the "Cohort Analysis Vocabulary" exercise?
Practise vocabulary for cohort analysis: cohorts, retention curves, the flattening tail, and revenue vs user retention.
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