Brands often use a multi-layered approach to combine influencer discovery data with outreach signals, which leads to refined targeting and better performance in influencer marketing.

1. Segment Density Analysis: By combining geo-demographics from discovery data, such as an influencer’s followers’hometown or age range, with engagement metrics from outreach data, like click-through rates on past collaborations, brands can better target segments that demonstrate higher engagement rates.

2. Audience Overlap Analysis: Brands may compare the follower base of discovery influencers with previous successful outreach influencers. If the overlap is significant, the discovery influencer may be a good match.

3. Campaign Performance History: Brands often integrate discovery data with historical campaign data. For instance, if a fashion influencer has high engagement rates amongst followers and the brand’s past collaborations with fashion influencers were successful, they may consider outreach.

4. Behavioral Data: Brands also commonly blend behavioral data, such as an influencer’s past brand partnerships or audience engagement patterns, with outreach signals to predict how well the partnership might fare.

5. Qualitative Assessment: Alongside hard data, brands gather qualitative signals such as the tone of voice, values, and content style, from influencers’ profiles during discovery. These are combined with outreach responses or previous collaboration experiences.

Platforms like Flinque can streamline these analyses with data-driven influencer discovery and campaign tracking tools, which help brands blend discovery data with outreach signals for more refined targeting. Options like these cut down manual work and provide meaningful insights to inform decision-making.

It’s important to understand that the best strategy often depends on the specific goals, processes, resources, and industry of each marketing team. These are generic approaches and each may be adapted as per individual brand requirements.