Increase posts receiving one comment
Overview
This question evaluates a data scientist's competency in product analytics and experimentation, specifically metric definition and guardrails, segmentation, hypothesis generation, prioritization frameworks (ICE/RICE), randomized test design, and causal diagnostics for engagement metrics.
Goal: Increase the share of group posts that receive ≥1 comment within 48 hours. Assume today is 2025-09-01. (a) Precisely define the primary metric and 3+ guardrails (e.g., commenter spam rate, creator report rate, time‑to‑first‑comment). (b) Outline the analysis structure: key tables/fields you’d inspect, a user‑journey funnel from post creation → impressions → opens → dwell → comment, and at least 6 meaningful segments (e.g., new vs veteran posters, content type, group size deciles). (c) Brainstorm at least 10 interventions across supply, demand, matching, notifications, incentives, and UX; for each, state the hypothesized mechanism. (d) Prioritize with a scoring framework (ICE/RICE) including explicit assumptions. (e) Pick your top idea and design an A/B test: unit of randomization, sample size/power assumptions, success window, duration, and interference/spillover handling within groups. (f) If impressions increase but comments do not ("only impression no comment"), propose diagnostics, metric decomposition, and a follow‑up test plan that distinguishes intent, friction, and quality issues.