Identify non-table data for feature demand

You must decide whether to build a "group call" feature without relying solely on existing product tables. What specific non-table data sources would you use, what signal would you extract from each, and how would you quantify and de-bias them? Discuss at least: (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), and (d) user research/UX telemetry (task success, time-on-task, heatmaps). Then design an evidence-driven experiment plan to validate demand: propose a fake-door test (exposed to X% of eligible users), a waitlist, and a limited beta. For each, specify the 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 with rough sample-size math and 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).