Pick a Fitness Product You Love, Then Diagnose Its Three-Month User Drop-Off

Tell me about your favorite fitness product and why you like it. Then suppose you discover that its users tend to stop using it around the three-month mark: how would you work out what is happening, and what would you do? ### Clarifying Questions - Is the three-month drop-off new compared with users who signed up a year earlier, or has the product's retention curve always looked like this? - What counts as dropping off for this product: no workouts logged, no app opens, or a cancelled paid plan? - Does anything happen to these users around month three, such as a free or discounted period ending? ### Part 1 — Your favorite fitness product Name the product, who it is for, the need it meets, and why it is your favorite. Point out one thing it does poorly. ```hint Pick one you can take apart Choose a product whose users, business model and weekly usage habit you know well, because your Part 2 diagnosis has to use those details. ``` #### What This Part Should Cover - The target users and the core need the product meets - A specific feature or mechanism that explains why it is good, not just "it is easy to use" - How the product makes money and what brings users back each week ### Part 2 — Users drop off after three months For that same product, users tend to stop after about three months. Explain how you would find the cause and what you would do about it. ```hint Locate the drop before explaining it Split retention by signup cohort, acquisition source and type of user first. Knowing who drops off, and since when, narrows the possible causes. ``` #### What This Part Should Cover - A precise definition of the drop-off and a check that the data can be trusted - An ordered search for the cause across product changes, user segments, seasonality and outside factors - Fixes matched to the most likely cause, prioritized - How the fix would be tested and which metrics would show it worked ### What a Strong Answer Covers - A diagnosis that draws on the product chosen in Part 1 rather than a generic churn checklist - Separating a newly worsened retention curve from a long-standing one, and how that changes the plan - Hypotheses checked against data and user research before a fix is chosen - A test plan that copes with the months needed to observe three-month retention - A clear recommendation with success metrics and guardrails ### Follow-up Questions - The analysis shows the drop is concentrated among people who signed up in January. What changes in your plan? - How would you tell apart users who reached their goal from users who lost motivation, and should the product treat them differently? - Leadership wants to fix the drop with more push notifications. How do you respond?