Estimate daily usage volume for content discovery feed in a large digital product

Problem Statement Description

You are asked to estimate the daily usage volume for a content discovery feed in a large-scale digital product serving international users. The feed may surface articles, videos, products, posts, recommendations, or other personalized content depending on the product context, but the core task is to size how much feed activity happens in a typical day.

Your estimate should define what “usage volume” means before calculating it: for example, daily feed sessions, feed opens, impressions, scrolls, content views, or meaningful engagements. You should make clear which unit you are estimating and why that unit is useful for understanding long-term engagement.

This is a hard guesstimate because the product is global and likely has diverse user segments, regional adoption differences, varying internet access patterns, different content formats, and different engagement frequencies. The interviewer is looking for a structured estimation approach, not a precise factual number.

The experience should consider:

- Scope: what type of digital product and feed experience you are assuming, and whether the estimate covers all international users or only active users.

- Unit of volume: whether you are estimating feed visits, sessions, impressions, scroll depth, content clicks, or another measurable activity.

- Population sizing: total users, monthly active users, daily active users, and international share.

- Adoption and frequency: how often users encounter the feed, how many feed sessions occur per user per day, and how usage differs by region or user maturity.

- Engagement depth: expected items viewed, time spent, scroll behavior, or interactions per feed session.

- Assumptions: explicit benchmarks, ranges, and simplifications used to handle missing data.

- Sensitivity: which assumptions most affect the final estimate, such as DAU rate, feed penetration, or sessions per active user.

- Sanity checks: comparison against expected app usage patterns, infrastructure scale, notification-driven visits, or monetization/engagement benchmarks.

Your goal is to produce a defensible daily usage estimate with transparent assumptions, clear math, and a final range that would be useful for product planning, capacity forecasting, experimentation, or evaluating the feed’s role in long-term user engagement.