Investigate MAU Drop and Test Coupons

You are interviewing for a Data Scientist role at a ride-hailing company. The company defines **MAU (Monthly Active Users)** as the number of unique riders who complete at least one ride in a calendar month. MAU has dropped by **7% month over month**. Answer the following: 1. **Investigating the MAU drop** - How would you diagnose the cause of the 7% decline? - What metric decomposition, user segmentation, funnel analysis, and operational checks would you use? 2. **External vs. internal causes** - How would you determine whether the drop was caused by **external competition** rather than internal factors such as pricing, product bugs, rider experience, or marketplace quality? - What data, quasi-experimental methods, or market comparisons would you use? 3. **Coupon intervention for retention** - Suppose the team concludes that the problem is mainly **retention among existing users**. - The proposed intervention is a **coupon that expires in 7 days**. - How would you estimate the **cost** and **business value** of this coupon program? - How would you decide which users should be **eligible** for the coupon? 4. **Experiment design** - How would you design an experiment to measure the causal impact of the coupon? - What should be the primary success metric, what secondary metrics and guardrails would you track, and how would you handle the trade-off between incremental rides, retention, revenue, and coupon cost? 5. **Launch decision** - Suppose the experiment shows a **statistically significant** improvement. - Ride volume increases by about **10%**, but **total revenue increases by only a few percentage points** because of the discount. - Would you launch the coupon program? Why or why not? - What are the limitations of making a launch decision from those topline results alone? 6. **Statistical vs. practical significance** - If the result is statistically significant but not practically significant, would you ship the feature? Explain your reasoning.