Coinbase Metric Diagnosis: Finding Why Daily Active Users Fell 10%
Company: Coinbase
Role: Product Manager
Category: Analytics & Experimentation
Difficulty: medium
Interview Round: Other
Coinbase's daily active users (DAU) have fallen by 10%. As the product manager, walk through how you would work out why.
```hint Order the checks
Confirm the number is real first, then find where the drop is concentrated. For a trading app, keep causes outside the product on the list alongside releases and outages.
```
### Clarifying Questions
- How is a daily active user counted here: any signed-in visit to the app or website, or only users who buy, sell or move funds? Are the advanced trading product and the separate wallet app included?
- Is the 10% measured against the previous day, the same weekday last week, a longer average or last year, and did it appear suddenly or over several weeks?
- Is the drop spread evenly across platforms and countries, or concentrated somewhere?
- What did crypto prices and trading volumes do over the same period?
### What a Strong Answer Covers
- Validation of the data and the DAU definition before any diagnosis
- The drop's size, timing and shape against a sensible baseline
- Segmentation that locates the drop and how much of it each segment explains
- Hypotheses that separate external drivers specific to crypto from internal product causes, each with the data that would confirm or rule it out, checked in a deliberate order
- Next steps once a cause is found, including how recovery is measured and who is kept informed
### Follow-up Questions
- The drop lines up with a fall in crypto prices, and comparable apps saw the same. Is there anything for the product team to do?
- Nearly all of the drop is in one country where new crypto rules are being debated. What do you do next?
- How would you change Coinbase's engagement metric so it reflects product health rather than market swings?
Overview: A metric diagnosis question in which daily active users on Coinbase have fallen 10% and the product manager must find out why. It tests validating the data, describing and segmenting the drop, separating market, regulatory and product causes, and ordering the checks sensibly.