Evaluate Facebook Dating launch and validate success
Overview
This question evaluates a data scientist's competency in product analytics, experimental design, causal inference, and launch validation by requiring definition of primary and guardrail metrics, test unit and spillover considerations, power/duration planning, behavioral signal expectations, quasi-experimental alternatives, and scale/readiness criteria. Commonly asked in Analytics & Experimentation interviews because organizations need assurance a pilot can be scaled safely and reliably, it tests practical application of experimental methods alongside conceptual understanding of statistical power, spillover risk, diagnostic checks, and operational concerns such as privacy and abuse monitoring.
Validation Plan: Scaling Facebook Dating from Pilot to Broader Rollout
Context: You are a data scientist evaluating whether a limited-market pilot of Facebook Dating is ready to scale. Design a rigorous validation plan covering metrics, experimentation, power, behavioral expectations, alternatives to RCTs, and go/no-go criteria.
1) Hypotheses and Success Metrics
Define:
- Primary objective metric (e.g., successful matches per active seeker).
- Guardrail metrics (e.g., core app retention, message abuse reports, privacy complaints).
- Counter-metrics (e.g., cannibalization of core social engagement). For each, specify clear metric definitions and acceptable deltas.
2) Experimental Design
Propose either a market-level rollout test (geo-ramp) or user-level randomization. Justify:
- Choice of unit of randomization and expected spillover/interference risks.
- Steps to mitigate cross-market contamination (e.g., geo-fencing, intent-to-treat analysis).
3) Power and Duration
Provide a back-of-the-envelope sample size and duration plan under realistic baseline rates and minimum detectable effects (MDE). Describe:
- Power assumptions and calculations.
- How you’ll monitor novelty effects and avoid winner’s curse.
4) Expected User Trends and Anomalies
Describe expected shapes for:
- New-user activation.
- 1/7/28-day retention.
- Match-to-message conversion.
- Seasonality. List anomalies that would be concerning (e.g., spike in sign-ups without matches, gender-imbalance driven drop-offs).
5) Validation Without Full RCT
If an RCT is infeasible, propose a quasi-experimental approach (e.g., synthetic controls or staggered DiD with pre-trend checks). Detail diagnostics required before trusting the estimate.
6) Scale/Readiness Criteria
Define quantitative and qualitative gates to expand, pause, or roll back. Include considerations for privacy readiness (e.g., DPIA/SOC 2 for any third-party services), abuse tooling, and on-call load.
Solution
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