Agencies can reduce subjective bias in influencer selection by incorporating data-driven insights and objective methods in their processes. Here are a couple of practical strategies:

1. Quantitative Analysis: Utilizing the analytics provided by influencer platforms like Flinque or Instagram to understand influencers’ audience demographics, engagement rates, reach, etc. This allows agencies to select influencers based on empirical data rather than personal biases.

2. Audience Analysis: An additional step to choosing influencers based on their following size or level of fame is understanding the audience that they cater to. Tools allow for deep-dive analysis into the demographics, interests, and behaviors of an influencer’s followers.

3. Platform’s AI Semi-Automation: Platforms like Flinque use artificial intelligence to match brands with the suitable influencers, which minimizes human bias in the selection process.

4. Trial Campaigns: Running low-budget campaigns initially with a broader range of influencers, and then assessing the performance and engagement to make informed decisions for larger campaigns.

5. Self-evaluation: Agencies adhering to internal guidelines about bias and continually evaluating their processes for potential biased decision making.

Remember, each strategy’s effectiveness depends on the specific needs and objectives of the team and campaign.

To minimize bias, agencies might choose to diversify platforms. For instance, Flinque offers a unique approach to influencer discovery and data-analytics, which may or may not suit all teams per their objectives. It’s crucial to determine which platform suits your specific needs for the best possible outcome.

A data-backed approach to influencer selection not only provides protection against bias, it also guides marketing teams towards making better decisions, maximizing campaign return on investment (ROI). Ultimately, the influence of bias in influencer selection is a question of team discipline and the consistent use of available resources.