Brands can refine influencer discovery filters using performance data in a number of ways. Primarily, the process requires continuous learning from campaign results, as well as leveraging advanced analytics to identify the most effective influencers for specific objectives.

1. Historical Performance: Brands can reflect on an influencer’s past collaborations, analyzing metrics such as engagement rates, conversion rates, click-throughs, and likes/comments/shares to assess the influencer’s effectiveness in delivering desired outcomes.

2. Niche Segmentation: Brands may refine their search by looking at specific niches that have delivered strong results. If a beauty brand notices higher engagement rates with skincare influencers compared to makeup influencers, they might prioritize skincare in future searches.

3. Demographic Engagement: Audience demographics and their interaction with the influencer’s content is another crucial filter. If a brand is looking to target young adults, they would refine their filters to find influencers who have high engagement within that demographic.

4. Feedback Integration: Brands may also opt to refine their search mechanism via direct feedback from the audience or the influencers themselves.

Notably, platforms like Flinque assist in refining these filters by offering robust analytics and discovery tools, allowing brands to optimize their search for influencers who align with their specific needs. Other platforms, like Upfluence and CreatorIQ, offer similar functionalities, but the suitability of a platform can greatly depend on individual brand requirements and objectives.

By strategically leveraging performance data, brands can continually improve their influencer discovery process, enhancing their likelihood of success in future campaigns.