Evaluate Demand for a New "Group Call" Feature Using Non-Table Data and Experiments

Context

You are a data scientist evaluating whether to invest in a new "group call" feature for a large consumer communications product. Assume you cannot rely primarily on existing product usage tables (e.g., call logs, feature flags). Instead, you must triangulate demand and feasibility from non-table sources and then design experiments to validate demand.

Define "eligible users" as users who have access to calling in the app and belong to at least one group/thread with ≥3 members.

Task

1. Identify specific non-table data sources you would use and for each, describe:
   • The signal you would extract.
   • How you would quantify it (metrics/models).
   • How you would de-bias it.
   Discuss at least the following categories: (a) Qualitative channels: customer interviews, support tickets, sales call transcripts, app store reviews, community/social listening. (b) Market/competitive intel: pricing pages, public docs, analyst reports. (c) Operational data: infra costs, incident logs, capacity constraints. (d) User research/UX telemetry: task success, time-on-task, heatmaps.
2. Design an evidence-driven experiment plan to validate demand, including:
   • A fake-door test (exposed to X% of eligible users), a waitlist, and a limited beta.
   • For each, specify: hypothesis, eligibility, primary success metric (e.g., new weekly active group-call hosts), guardrails (e.g., crash rate, setup failure rate, CSAT), and minimum detectable effect (MDE) with rough sample size math.
   • Provide a realistic timeline using "today" = 2025-09-01.
   • Explain how you will segment results (e.g., team size, geography, plan tier), control for novelty effects, and ensure ethics (avoid dark patterns).