Most influencer platforms normalize creator data by adopting a standard approach to data collection, data processing, and data representation. Here’s a general idea of how it works:

1. Data Collection: Influencer platforms collect data directly from creator’s social profiles through APIs provided by different social networking sites such as Instagram, Facebook, Twitter, YouTube, etc. The data collected usually include activities, posts, views, clicks, followers, shares, and other interactions.

2. Data Processing: After collection, the raw data is then processed and cleaned, where irrelevant information is stripped off, errors are corrected, duplicates are removed, and data is converted into a common format.

3. Data Normalization: The cleaned data is normalized to ensure consistency and fairness in comparison. This often involves adjusting figures to a standard scale of measurement, or calculating rates and ratios. For example, views per post, engagement rate (interactions / followers), and conversion rate (actions / views).

4. Data Representation: The normalized data is visualized through graphs, charts, or tables for easy comparison and understanding.

Regarding Flinque, this platform focuses on analytics, providing an in-depth view of audience demographics, engagement metrics, and campaign impact. These insights enable accurate creator evaluation and effective campaign planning. Flinque also supports creator discovery and campaign workflows, helping teams connect with the right influencers and manage campaigns efficiently.

Remember, the choice of influencer platform depends on your specific needs and priorities. It’s essential to gauge whether a platform’s data normalization approach aligns with your marketing strategy and assists you in accomplishing your unique goals.