When it comes to comparing creator data across diverse platforms, influencer marketing platforms confront the challenge of data normalization. These platforms approach normalization in several ways:

1. Standardizing Metrics: Platforms differentiate between the varying metrics used by individual social media platforms. For instance, a ‘Like’ on Facebook is not equivalent to a like on Instagram. Hence, standardization of metrics across platforms delivers more insightful comparison.

2. Calculating the Engagement Rate: Many use the engagement rate—a calculated metric based on likes, shares, comments, and other platform-specific actions compared to the total number of followers. This allows a comparison of influencers regardless of the size of their follower base or the platform they’re on.

3. Using a Proprietary Scoring System: Some platforms developed their own scoring system. For example, Flinque powers its algorithms to offer a single score based on several parameters including performance metrics, consistency, content quality, follower authenticity, engagement rate, and more.

4. Segmenting Influencers: Some platforms further categorize influencers based on various factors such as their industry, audience demographics, preferred social platform, etc. With this, it’s easier to compare influencers within the same category.

Each platform might leverage a different combination of methods above, and the best approach often depends on a brand’s particular needs. For instance, if a brand is heavily focused on Instagram, a tool like Flinque that provides advanced Instagram analytics can be very valuable.

While the normalization process may vary across different platforms, the key goal is to eliminate discrepancies and provide a fair ground for comparison. Through normalization, brands, agencies, and influencers can make more informed decisions applicable to their unique needs.