Diagnose and respond to a 35% fall in DoorDash iOS app installs
Company: DoorDash
Role: Product Manager
Category: Analytics & Experimentation
Difficulty: medium
Interview Round: Other
DoorDash's iOS app is getting 35% fewer installs than before. As the product manager, how would you work out what is causing it and what to do about it?
```hint Read the shape first
Before guessing at causes, ask what the drop looks like: when it started, whether it happened overnight or slowly, and whether Android moved at the same time.
```
### Clarifying Questions
- Over what window is the 35% measured, and against which comparison: the previous week, the previous month or the same period last year?
- Did installs fall overnight or decline over several weeks?
- Does "installs" mean first-time installs only, and which source reports it: Apple's store reporting, our attribution tool or our own app-open events?
- Is the drop in every market the app serves, and is the Android app affected too?
### What a Strong Answer Covers
- Confirming the drop is real by checking the metric definition, data sources and recent instrumentation changes
- Describing the drop by timing, platform, market, acquisition channel and store funnel stage before naming causes
- Internal and external hypotheses specific to an iOS install funnel, ranked and paired with a check for each
- Connecting installs to new customers and first orders to size the business impact
- A response plan with fixes, communication and alerting that would catch the next drop sooner
### Follow-up Questions
- Installs from paid campaigns fell while organic installs are flat. What do you check next?
- Installs are down 35%, but first orders from new customers are down only slightly. What does that tell you, and does it change your priority?
- How would you set up monitoring so a drop like this is caught within a day?
Overview: A DoorDash product manager execution question: installs of the iOS app have fallen 35%, and you need to find out why and respond. It tests validating the data, slicing the drop by time, platform, market and channel, ranking internal and external hypotheses, and sizing the effect on new customers and first orders.