How we find the perfect creator match using network graphs (not hashtag search)

Most influencer marketing tools work like a phone book — search "fitness," filter by follower count, hope for the best. We kept running into the same problems: audience overlap between creators, aesthetic mismatch, and hours wasted vetting profiles in spreadsheets.

So we built the Three Circles Method into Celavii. Instead of keyword search, it maps three data layers:

1. Circle 1 (Audience) — Who follows your brand or your competitors? Which creators already have attention authority over those people?

2. Circle 2 (Brand DNA) — Who does your brand follow? Your "following" list is a fingerprint of your taste and aspirations. We decode it to find creators who already live in your aesthetic world.

3. Circle 3 (Intersection) — Where do these two graphs overlap? That's where the "perfect creator" lives — proven audience trust + natural brand alignment.

When we tested this across 2,400 profiles during our beta, intersection-matched creators outperformed keyword-matched ones by 3.2x on engagement quality.

The key insight: "Following" lists are 3x more predictive of campaign retention than "Follower" demographics. Everyone looks at followers. Almost nobody looks at who the brand follows — and that's where the signal is.

We wrote the full breakdown here: https://celavii.com/blog/network-intelligence/three-circles-method

Would love to hear how other founders approach creator/influencer discovery — are you still doing it manually?