Diagnose Low Retention for FB Light (Android-only, Emerging Markets)

Context

You are a data scientist on the FB Light team. The app launched a year ago in several emerging markets to grow monthly active users (MAU). Despite initial adoption, retention is low.

Assume you have access to product analytics (event logs, device/network metadata), crash/ANR logs, Play Store data, basic market intel, and the ability to run A/B tests.

Task

Outline how you would diagnose and determine the root causes of low retention and propose next steps.

Please cover:

1. Metrics and Definitions
   • Define retention clearly (e.g., D1, D7, D30, rolling monthly retention), activation, and any guardrail metrics.
2. Cohort Retention Analysis
   • How you’d construct cohorts (e.g., by install month) and compute retention. Include how you’d handle seasonality and app version rollouts.
3. Segmentation Strategy
   • Segment by country, acquisition channel, device (RAM/CPU/Android version/OEM), network quality (2G/3G/4G/Wi‑Fi, latency), app version, language, and user type (new vs returning). Explain why each matters.
4. Funnels and Drop-offs
   • Map the core funnel from install → open → sign-up/login → activation → week 1 engagement → return. Identify where to look for major drop-offs.
5. Hypotheses and Diagnostics
   • List top hypotheses (e.g., performance on low-end devices, data cost, sign-up friction/SMS OTP failures, push notification reliability, content/network effects, crash/ANR issues, feature gaps vs full app, acquisition quality). For each, describe the specific analysis you’d run to validate or refute it.
6. Qualitative and Market Inputs
   • How you’d use app reviews, surveys/interviews, support tickets, and competitive benchmarking.
7. Modeling and Causality Aids
   • Any predictive modeling (e.g., survival analysis or logistic regression for D7) to quantify drivers; how you’d avoid common biases.
8. Experiments and Product Changes
   • Prioritized experiments or changes to address likely root causes, with success metrics and guardrails.
9. Validation Plan
   • How you’d confirm fixes generalize across segments and avoid regressions.