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Scoring methodology

Live platform data + ML scores. Every number on a report maps to one of the sections below.

Live from APIs
  • Profile, bio, followers, following
  • Likes, comments, shares, saves, views
  • Posting frequency (last 30 / 90 days)
  • YouTube · X APIs
Modeled and inferred
  • RankMint, growth, campaign success - trained ML
  • Brand match - embeddings + optional OpenAI upgrade
  • Demographics - inferred from location and content niche
  • Authenticity - heuristics, not a fraud-vendor API

Model credibility

Dataset

campaign_labels.csv

Rows

403

Accuracy

95.1%

AUC

1.000

F1

0.957

Five scoring engines

Authenticity

0-100
Heuristic
  • Purchased followers
  • Engagement pods
  • Bot activity
  • Artificial spikes

Growth potential

0-100
Modeled
  • 90-day follower growth
  • Engagement growth
  • Audience expansion

Brand match

0-100
Embeddings + RAG
  • Creator to brand similarity
  • pgvector retrieval
  • Commerce signal rerank

Campaign success

0-100%
ML
  • Logistic regression
  • Engagement and consistency features
  • Trained on campaign labels
R

RankMint

Ratefluencer Score

0-100
ML composite
  • Ranks creators by business impact
  • Not follower count alone
  • Tier calibration for mega creators

Audience demographics

Age, country, gender on every report

Inferred

Built from creator location, content niche, and platform benchmarks.

Add brands in Brand workspace · Retrain model with npm run ml:train

Analyze a creator