Judging whether Instagram's removal of like totals from posts is working
Company: Meta
Role: Product Manager
Category: Analytics & Experimentation
Difficulty: medium
Interview Round: Other
Instagram has decided to stop showing like counts on posts. As the product manager, how would you measure whether this change is a success? Explain what it is trying to achieve, which metrics you would track, and how you would judge the result.
```hint Start from the problem
Before naming metrics, work out what problem visible like counts caused and for whom; success should be defined by that problem shrinking.
```
### Clarifying Questions
- Are counts hidden only from other people, with authors still able to see the count on their own posts, or hidden from everyone?
- Does the change cover ads and video posts as well as regular feed posts?
- Is this a test in a few markets or a global launch, and can people choose to see counts again?
- What prompted the change: pressure and comparison felt by people who post, people liking whatever is already popular, or likes being bought to look popular?
### What a Strong Answer Covers
- The goal behind hiding counts, tied to a specific problem and the people it affects
- The groups the change affects, including those who could lose from it
- A primary success metric that reflects the goal, with supporting and guardrail metrics
- A test or rollout design that isolates the change's effect and allows time for behavior to settle
- How the results would be read, including the outcome that would lead you to adjust or reverse the change
### Follow-up Questions
- Suppose casual users post more but the biggest creators post less and some move to other apps. Is the change a success?
- How would you measure social pressure or wellbeing, which no log captures directly?
- How would network effects bias a test that randomizes individual users, and what would you do about it?
Overview: A product metrics question about Instagram's decision to hide like counts on posts. It tests whether a candidate can tie success metrics to the goal behind the change, weigh the effect on posters, viewers, creators and advertisers, set guardrails, and design a test that accounts for network effects and novelty.