You've been tasked with improving user retention, but the existing analytics infrastructure is fragmented and provides conflicting data on user churn drivers. How would you approach identifying the root causes of churn and prioritizing growth initiatives with such ambiguous data, and what technical steps would you take to improve data reliability for future growth efforts?

MECE Framework: 1. Define: Clearly articulate 'retention' and 'churn' metrics. 2. Deconstruct: Break down the problem into user segments, touchpoints, and product features. 3. Analyze: Conduct qualitative (user interviews, surveys) and quantitative (cohort analysis, funnel analysis) research despite data ambiguity. 4. Synthesize: Identify recurring themes and potential churn drivers. 5. Prioritize: Use RICE scoring (Reach, Impact, Confidence, Effort) for initiatives. Technical Steps: Implement a unified tracking plan (e.g., Segment.io), establish a single source of truth (data warehouse), and validate data integrity through regular audits and A/B testing.

I'd apply a CIRCLES Framework for problem-solving and RICE for prioritization. First, I'd Clarify the definition of churn and retention for our product. Then, I'd Identify existing data sources, noting discrepancies. Next, I'd Research by conducting qualitative interviews with churned users and support teams to gather anecdotal evidence, complementing fragmented quantitative data. I'd then Create hypotheses about churn drivers based on this combined input. For prioritization, I'd use RICE scoring for potential growth initiatives, weighing the impact of each hypothesis against the confidence derived from ambiguous data. To improve data reliability, I'd implement a unified tracking plan across all platforms, establishing a single source of truth in a data warehouse. This involves defining clear event schemas, instrumenting consistent logging, and setting up automated data validation checks. Finally, I'd Launch A/B tests for high-priority initiatives, using the improved data infrastructure to measure impact accurately, and Summarize learnings to refine our understanding of user behavior and continuously improve the product.