This is four questions stapled together and I did not handle the transition between parts gracefully.
Start by triangulating latent demand from messaging data (e.g., frequency of group chats, attempts to start group calls), friend graph (e.g., cluster density, overlapping social circles), and survey results (e.g., stated interest, pain points). Then define success metrics that capture adoption, engagement, and retention, design an A/B test with a holdout to measure causal impact, and use experimental data to determine the optimal participant cap by analyzing engagement and quality metrics across different group sizes.
Pro tip: Focus on behavioral data over stated preferences: survey interest often overstates actual usage. Also, when determining the cap, consider network effects and diminishing returns—the optimal size may be where marginal engagement gains flatten or drop due to coordination costs.
Analyze messaging data for group chat frequency, call initiation attempts, and drop-off points; use friend graph to identify dense clusters and cross-group connections; and incorporate survey results to understand unmet needs and willingness to use.
Select a North Star metric (e.g., weekly active group calls per user) and supporting metrics for adoption (e.g., % of eligible users trying feature), engagement (e.g., average call duration, frequency), and quality (e.g., call success rate, user satisfaction).
Run an A/B test with a control group (no feature) and treatment groups (feature enabled with varying participant caps). Randomize at user or group level, ensure sufficient power, and measure causal impact on key metrics.
Use experiment data to plot engagement and quality metrics against group size. Identify the point where marginal gains diminish or negative effects (e.g., technical issues, social loafing) emerge, and set the cap accordingly.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.