I went straight to churn analysis and segmenting users by usage frequency, which felt right in the moment.
Start by defining what retention means for DashPass (subscriber churn vs. order frequency) and why it matters to DoorDash's business. Then propose a data-driven framework: identify churn drivers through cohort analysis and user research, prioritize high-impact levers (e.g., pricing, benefits, personalization), and design experiments to validate improvements. Emphasize cross-functional collaboration and measurable outcomes.
Pro tip: Anchor your answer in DoorDash's three-sided marketplace: retention improvements must balance subscriber value with merchant and Dasher incentives. Mention that you'd track leading indicators like monthly order frequency and benefit utilization, not just churn rate.
Clarify whether retention means subscriber retention (renewal) or order retention (frequency). Segment users by tenure, order behavior, and demographics to identify at-risk cohorts.
Analyze data (e.g., declining order frequency, low benefit redemption) and conduct user research to uncover why subscribers disengage or cancel. Prioritize root causes by impact and feasibility.
Brainstorm interventions across pricing, benefits, personalization, and engagement. Use an impact/effort matrix to select high-potential ideas, such as targeted promotions or new perks.
Propose A/B tests with clear success metrics (e.g., retention rate, order frequency). Ensure statistical power and guardrail metrics to avoid negative side effects.
Analyze results, learn from failures, and scale winning solutions. Establish a continuous improvement loop with cross-functional teams (product, data, marketing).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.