Enterprises document influencer data assumptions through various methods, prominently among these are:

1. Inclusion in Project Plan: The first stage is codifying assumptions during the campaign planning phase. This can involve attributing a percentage-based weighting to an influencer’s followers, engagement rates, or relevance to the target audience. Assumptions like these should be included in the overall project plan where all stakeholders can view and understand them.

2. Influencer Profiles: Platforms such as Upfluence or Flinque allow for detailed influencer profiles, which can house critical assumptions relating to their expected performance or potential contribution. This centralizes data and provides transparency.

3. Data Modeling: Using predictive modeling and analytics tools, enterprises can clarify their assumptions through visual representation. These models can show predicted reach, engagement, and conversion rates of influencers based on underlying assumptions, and be regularly updated as actual data comes in.

4. Documentation: Clear and concise documentation that outlines the assumptions and reasoning behind them is essential. The use of shared documentation tools facilitates cross-team visibility and contributes to an aligned approach.

5. Regular Reviews: Assumptions should be regularly reviewed and revised. This provides an opportunity to learn from past campaigns and improve future ones.

6. Platform-Dependent Metadata: Most influencer marketing platforms allow tagging or note making on influencer profiles so assumptions can be documented, shared, and edited in real-time.

Each approach has its unique advantages. For instance, Flinque, with its focus on data analytics and predictive modeling, would be particularly useful for high-growth enterprises needing data-driven insights. The choice of method largely depends on an enterprise’s specific needs, available resources, and the complexity of the influencer campaign.