How Do Companies Correlate Discovery Attributes With Acceptance Rates?

Log the rejections with a reason attached. Acceptance analysis is impossible without the noes, and the reason field is what turns a count into something you can act on next quarter.

Separate a refusal from a non-reply. They have entirely different causes and entirely different fixes, and combining them hides the one that is actually within your control.

Correlating discovery attributes with acceptance rates turns outreach from guesswork into a learning system. The method is to record two things together: the discovery attributes of each creator you contacted, such as size tier, niche, engagement rate and whether they already do sponsored work, then the outcome, accepted, declined or ignored.

With enough of these logged, you can look for real correlations, perhaps mid-tier creators in your niche accept far more often than mega-influencers, while those with a public media kit reply at double the rate. Those patterns tell you which attributes to weight when building the next shortlist, lifting your acceptance rate over time.

The honest caveats are that correlation needs a decent sample to be trustworthy and can shift, which means treating it as a living signal rather than a fixed rule. Flinque supplies the attribute side of this cleanly, consistent discovery and audience data per creator, which means that when you pair it with your own outreach outcomes, the correlations rest on reliable inputs rather than messy, inconsistent data.