Fitbit PM: justify a key success metric, then build a feature that raises it

You are the product manager for Fitbit. Pick the one metric you would care about most, explain why it deserves that place, and then describe a feature you would build to improve it, including how you would get it built, launched and evaluated. ### Clarifying Questions - Does "Fitbit" here mean the wearable devices, the companion app, the paid Premium subscription, or the product line as a whole? - Which business outcome matters most right now: device sales, subscription revenue, or keeping existing owners engaged over time? - Must the feature work on devices people already own through an app or software update, or may it depend on new hardware? ### Part 1 — Choose and justify the metric Name the single metric you would watch most closely as Fitbit's PM, define it precisely enough that two analysts would compute the same number, and argue why it beats the alternatives. ```hint Start from the value moment Ask what has to keep happening for an owner to get value from a Fitbit, and which number would fall first if it stopped. ``` #### What This Part Should Cover - Several candidate metrics from different stages of the owner's journey, compared - A precise definition: who is counted, what counts as success, and over what window - Why the metric reflects user value and leads Fitbit's business outcomes - Counter-metrics that would expose gaming or harm to users ### Part 2 — Design and ship a feature that moves it Propose one feature aimed squarely at the metric from Part 1. Explain which user problem it addresses, how it works for the user, how you would build and roll it out, and how you would know it worked. ```hint Look just before the drop Study the owners your metric counts as lost and what happened in the days just before they slipped, and design for that moment rather than for owners who are already doing well. ``` #### What This Part Should Cover - A diagnosis, checked against data, of what pushes the metric down for a specific group of owners - The feature's experience across the device and the phone app, with alternatives considered - A build plan from a first version to a fuller one, including dependencies - An experiment with a primary metric, guardrails, sample size and a realistic readout window ### What a Strong Answer Covers - A consistent thread from what owners value, to the metric, to the feature - Prioritization among candidate features against stated criteria - Risks particular to wearables and personal health data, with mitigations ### Follow-up Questions - If your feature lifts the metric but subscription renewals and repeat device purchases are flat six months later, what do you conclude? - How would you keep the surge of new owners after the holiday season from distorting your experiment? - What evidence would make you replace your metric with a different one?