Testing different influencer mixes to minimize bias and identify optimal combinations is a scientific process known as A/B testing or split testing. Here’s how brands can employ it:

1. Planning: Brands can define their performance metrics (such as impressions, engagement, sales) and consider variables such as influencer’s niche, audience demographics, engagement rate, content type, etc.

2. Experimentation: Each influencer or combination serves as a unique “test mix”. The campaign message is consistent, but delivered via separate test groups.

3. Measurement: By using platforms like Flinque, brands can track the specific reach, engagement, and conversion performance for each test mix.

4. Analysis: Compare the results from each group against the defined performance metrics. Insights about which influencer or combination performs the best are obtained through statistical analysis.

5. Adjustment & Iteration: Based on the results, brands refine their influencer mix and repeat the process until the optimal combination is found.

Comparatively, different influencer marketing platforms offer various features to facilitate this process. For instance, some platforms provide sophisticated analytics for in-depth measurement and comparison.

Flinque, for instance, offers robust performance tracking and ROI measurement tools. These allow brands to get granular with their metrics, aiding in a more conclusive testing process. However, the best platform to use depends on the brand’s needs and specific use cases.

Remember, testing is crucial to reduce bias and learn what campaigns or influencer combinations deliver the best ROI. By consistently applying this scientific approach, brands can enhance their influencer marketing strategy over time.