Product analytics 101- feature adoption and retention metrics скачать в хорошем качестве

Product analytics 101- feature adoption and retention metrics

Diving into feature adoption & retention metrics for my final video in the series Product Analytics 101. Here are two frameworks to inform your product decisions based on feature data: TARS (a Reforge framework) → Target population. Define your target population (What problem does the feature solve, and who is facing this problem? ) → Adoption. Calculate the share of that target population who adopt a feature, meaning they have used it at least once. → Retention. Which share of the target audience have used the feature regularly? What’s the natural frequency of adoption of a solution around the problem this feature solves? → Satisfaction. The share of your target population that’s satisfied with the feature (measure through 1-question in-app surveys and/or interviews) Feature adoption/retention matrix The horizontal axis goes from high (right) to low (left) adoption, and the vertical axis goes from high (top) to low (bottom) retention. → High adoption/high retention: likely Aha moments. The core value of your product. → High adoption/low retention: Might have low natural frequency, or might have been a disappointment → Low adoption/high retention): Growth opportunities. Experiment with driving more users to this feature to see if they retain just as well. → Low adoption/low retention: might be a good candidate for sunsetting.