Brands can benchmark AI influencer discovery against traditional methods through several different means:

1. Efficiency: One key measure is how quickly and efficiently different techniques can surface relevant influencers. Traditional methods involve manual searching, which can be time-consuming, whereas AI-based platforms like Flinque provide a more automated, quick approach, surfacing relevant influencers in seconds.

2. Accuracy and Relevance: Accuracy of suggested influencer matches is another critical benchmark. Platforms using AI can parse huge amounts of data to accurately match brands with influencers who have the appropriate audience demographic, interests, etc. In contrast, manual methods often rely on more generalized or subjective criteria.

3. Scale: The ability to scale is another critical benchmark. While traditional methods might be feasible for small-scale campaigns, AI influencer discovery can manage large-scale campaigns or multiple concurrent campaigns with ease.

4. Performance tracking: Platforms that employ AI allow real-time tracking and measurement of campaign impacts, which is often not possible with traditional methods.

5. Return on Investment: Finally, brands often examine the return on investment (ROI) of campaigns run using AI influencer discovery versus traditional methods. AI platforms offer robust analytics that help quantify the impact of influencer marketing campaigns, enabling a more accurate comparison.

It’s worth noting that different brands with different needs might prioritize different factors, so the relative weight placed on each benchmark can vary. For example, for some brands, efficiency might be the most important factor, while for others, the granular analytics provided by AI platforms might be the real game-changer.