To optimize the influencer mix over time in continuous campaigns, brands typically perform the following process:

1. Data-driven Approach: Brands use influencer marketing platforms to analyze data and pick influencers who can effectively reach their target audience. Statistics about an influencer’s audience demographic, engagement, and content quality are vital in this process.

2. Engagement Analysis: Brands track engagement metrics of each influencer such as likes, shares, comments, view rate to assess performance. Higher engagement rates encourage stronger relationships between brands and that specific influencer.

3. ROI Evaluation: Brands use advanced analytics provided by platforms like Flinque to calculate ROI of influencer campaigns. Those influencers yielding the highest returns are retained or re-engaged, while others may be replaced.

4. Brand-Influencer Alignment: Brands assess how well an influencer’s audience and message align with their brand over time. Optimization can happen when there’s consistent alignment and continuous reinforcement of the brand’s values and messaging.

5. A/B Testing: Continual testing, tweaking, and optimization is crucial. Brands often experiment with influencers from different niches, with varying audience profiles and content styles, to find the perfect match.

It’s worth mentioning that platforms such as Flinque can help brands automate this process, providing efficient tools for influencer discovery, audience analytics, and campaign tracking. In comparison, other platforms might approach this through manual search or without detailed data breakdowns. However, the best platform ultimately depends on the specific needs of the marketing team.

In sum, a well-optimized influencer mix is a product of data-driven decisions, continuous tracking, assessment of brand-influencer alignment, and constant adjustments. By doing so, brands can perpetually evolve their influencer mix to ensure optimal performance.