I started with DAU, then tried to segment users by how active they are since a casual lurker and a creator have wildly different follow graphs.
Break down the problem by defining 'mutual follows' and estimating the average number of followers and following per user, then use probability to find the expected number of mutual follows. Consider TikTok's social graph characteristics, such as its emphasis on content discovery over social connections, which may lead to lower mutual follow counts compared to other platforms.
Pro tip: Acknowledge that mutual follows are a measure of reciprocal relationships, and in TikTok's algorithm-driven environment, many follows are one-sided (e.g., following celebrities). This shows you understand the platform's unique dynamics.
Clarify what 'mutual follows' means: a relationship where both users follow each other. This is distinct from one-way follows or friends.
Estimate the average number of accounts a TikTok user follows and the average number of followers a user has. Consider that these distributions are skewed: many users have few followers, while a few have millions.
Assume that for a given user, each of their follows has a probability of following them back. This probability can be estimated based on platform norms (e.g., reciprocal follows are more common among friends than celebrities).
Multiply the average number of follows by the probability of reciprocation to get the expected number of mutual follows. Adjust for skewness by considering median values or segmenting users (e.g., casual vs. creator).
Compare your estimate to known benchmarks (e.g., average Facebook friends ~200, but TikTok is more asymmetric). Refine by considering TikTok's focus on content consumption over social networking.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.