Investigating Lost Homepage Visits at the New York Times: Metric-Drop Diagnosis
Company: Carta
Role: Product Manager
Category: Analytics & Experimentation
Difficulty: medium
Interview Round: Other
Visits to the New York Times homepage have fallen by 15%. As the product manager, explain how you would figure out what caused the decline.
```hint Lost readers or a different route in?
A falling homepage count could mean fewer readers, or the same readers reaching stories another way. Plan how you would tell those two situations apart, and check the counting itself before either.
```
### Clarifying Questions
- How is traffic measured: page views, visits or unique visitors, and does it include the apps' home screens or only the website?
- Over what period did the 15% decline happen, against which baseline, and was it sudden or gradual?
- Is the decline worldwide, or concentrated in particular countries, devices or referral sources?
- Did total site traffic, article reads and subscriptions fall too, or only homepage visits?
### What a Strong Answer Covers
- Checking that the drop is real rather than a tracking, bot-filtering or definition change.
- Placing the drop in context: timing, shape, seasonality and the news cycle behind the baseline.
- Breaking homepage visits down by traffic source, platform, region and reader type to localize the decline.
- Internal and external hypotheses tested in a sensible order, each with the evidence that would confirm or rule it out.
- Judging whether readers were lost or simply arrived another way, and what that means for the business.
### Follow-up Questions
- The decline turns out to be gradual, concentrated in one country, and largest in visits from paid campaigns. What do you do next?
- Homepage visits are down, but article reads and new subscriptions are flat. Is this a problem worth solving?
- How would you separate a quieter news cycle from a genuine loss of readers?
Overview: A metric-drop diagnosis prompt about a 15% decline in visits to the New York Times homepage. It tests validating the data, comparing against seasonality and the news cycle, segmenting by traffic source, platform and region, and ranking internal and external hypotheses to find the root cause.