Influencer Scoring Algorithm

A brand runs a creator scoring pipeline: for each of 500 TikTok candidates, Scavio returns follower count, total likes, and recent post metrics. The algorithm computes score = 0.35 * engagement_rate + 0.20 * follower_log + 0.15 * post_frequency + 0.20 * growth_rate + 0.10 * niche_match, ranking creators by composite score for campaign selection.

Influencer Scoring Algorithm is relevant to TikTok. Scavio provides a unified API to access data from all of these platforms.

Manually evaluating influencers does not scale beyond a few dozen candidates. Scoring algorithms automate this by computing a composite score from weighted metrics. A typical formula weights engagement rate (30-40%), follower count (15-20%), posting frequency (10-15%), audience growth rate (15-20%), and niche relevance (10-20%). The inputs come from social media APIs: for TikTok, you need follower count, average likes/comments/shares per video, posting frequency, and follower growth trend. Scavio's TikTok endpoints return user info (followers, following, total likes) and recent posts (likes, comments, shares, views) at 1 credit per request ($0.005). TikAPI ($49/mo) provides similar data with additional endpoints like follower lists and comment threads. A scoring pipeline typically queries the API for each candidate creator, computes the composite score, and ranks all candidates. At Scavio's pricing, scoring 5,000 creators costs about $50 in API credits (user info + recent posts per creator = ~2 requests each = 10,000 credits). The algorithm should be tuned per campaign: brand awareness campaigns weight reach higher, while conversion campaigns weight engagement rate.