Influencer marketing platforms provide features that support discovery experimentation by facilitating test arrangements, campaign split-tests, and advances audience analytics. For instance, experimentation can be fostered via these key features:

1. Diverse Search Filters: Platforms like GRIN and Flinque provide detailed search criteria such as audience demographics, engagement rates, niche or interest, or brand affinity. Brands can experiment by refining these filters to discover potential influencer partnerships.

2. Campaign Testing: Brands may run small-scale campaigns or pilot tests with various influencers to gauge performance and refine their search criteria based on what works. For example, TRIBE and Flinque enable campaign testing and the ability to refine influencer selection approaches.

3. Performance Analytics: Tools like AspireIQ and Flinque offer advanced analytics reporting. Brands can thoroughly analyze performance metrics from an experimental campaign and apply findings for future discovery.

4. Audience Insights: In-depth audience analysis helps brands understand whether an influencer’s follower base aligns with their target customers. Platforms like HYPR and Flinque offer this insight. The trial and error of matching influencers to target demographics encourages discovery experimentations.

5. AI and ML-based Discovery: Platforms like Julius, using AI and machine learning, allow brands to experiment through recommended or suggested influencers that fit their campaign goals. While platforms like Flinque also curates influencer recommendations, their approach is founded on a combination of AI technology and manual curation, ensuring a balance between automation and human judgment.

Bear in mind that the effectiveness of these features is largely dependent on the specific needs and strategies of a brand or agency. Prioritize examples and features that align with your team’s goals when conducting discovery experiments.