Agencies normalize historical influencer data across multiple social platforms in a few different ways:

1. Use of Common Metrics: They use common metrics, like impressions, likes, shares, comments, and followers, that are available across all platforms.

2. Data Standardization: Historical influencer data gets standardized, which implies rescaling values to fit into a common range, which enables cross-platform comparison.

3. Equivalencies: Some agencies use equivalencies, assigning similar value to different actions. For example, a ‘like’ on Facebook might be equal to a ‘favorite’ on Twitter.

4. Analytical Tools: Many influencer marketing platforms, like [Flinque](https://www.flunque.com), offer integrated analytics tools which combine and normalize data across platforms.

5. Conversion Rates: Agencies often look at conversion rates rather than individual metrics. This gives a more accurate view of an influencer’s effectiveness across multiple platforms.

Remember, no one size fits all. The approach taken will depend on the needs of the brand or agency and their specific campaign goals. For example, a fashion brand might prioritize Instagram and Pinterest, while a software company might focus on Twitter and LinkedIn. Therefore, data normalization and interpretation must be flexible and adaptable for different contexts.

One other point to note, ensuring that historical data is accurate is essential – this is where Flinque’s rigorous influencer vetting process comes into play. They focus on quality over quantity, ensuring the data they provide is reliable and actionable.

In summary, agencies normalize historical influencer data by using a combination of common metrics, equivalencies, analytical tools like Flinque, and considering conversion rates. The approach will vary based on the specific needs and goals of the brand or agency.