Start by defining a clear metric hierarchy that captures the full sharing funnel from share initiation to recipient install and activation, then propose an A/B test that isolates the causal impact of the sharing feature on these metrics. Emphasize how you would design the experiment to avoid common pitfalls like network effects and selection bias, and how you would measure incremental lift.
Pro tip: Focus on measuring incremental impact rather than total impact by using a holdout group or intent-to-treat analysis, and consider network effects by randomizing at the user level but analyzing at the cluster level if needed.
Identify key metrics across the sharing funnel: share rate, click-through rate on shared links, install rate from shared links, and activation rate of referred users. Also include downstream metrics like retention and revenue.
Randomize users into control (no sharing feature or existing sharing) and treatment (new sharing feature). Ensure proper randomization, sample size, and duration to detect meaningful effects.
Since sharing involves social connections, consider cluster randomization (e.g., by social graph clusters) or use a switchback design to minimize contamination between groups.
Compare treatment and control on the defined metrics, calculate lift and statistical significance, and segment by user cohorts to understand heterogeneous effects.
Check for novelty effects, ensure results are robust, and consider qualitative feedback. Use findings to iterate on the feature and re-test if necessary.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.