Brands use analytics to optimize influencer selection and reduce reliance on intuition in several ways.

Firstly, demographic data is vital for matching influencers with the brand’s target audience. Platforms like Flinque provide insights into the age, location, gender and interests of an influencer’s followers, ensuring that the influencer’s audience aligns with the brand’s market.

Secondly, engagement metrics, such as likes, comments, and shares, gauge the level of interaction influencers have with their audience. A high engagement rate is often more valuable than a large follower count because it indicates a more active and interested community.

Moreover, past performance metrics like click-through and conversion rates can show how effective an influencer’s past campaigns have been. This is especially crucial when considering ROI and campaign goals.

Audience sentiment analysis can help brands to understand the influencer’s reputation and how their followers perceive them. It also reveals the audience’s sentiment towards certain posts or products promoted by the influencer.

Platforms also provide fraud detection by identifying suspicious follower growth, inorganic engagement, or fake followers. A reputable platform like [Flinque](https://www.flinque.com) provides deep insights in all these areas, helping brands make data-driven decisions and optimize their influencer selection process.

It’s important to remember that while data significantly enhances the decision-making process, it should be used in conjunction with a brand’s own knowledge and understanding of its customer avatar, market trends, and product nuances. Different platforms and tools offer different features and capabilities, so suitability would depend on the brand’s specific needs and objectives.