Enterprises can build proprietary discovery intelligence using historical performance signals and internal data in a variety of ways. One common method is to integrate diverse data streams into a centralized system, such as a Customer Relationship Management (CRM) platform or a Business Intelligence (BI) tool. Those types of systems can then provide cross-sectional views of performance, enabling teams to track trendlines across campaigns, identify high-performing influencers, and better forecast future outcomes based on evidence.

Another approach is to build custom data pipelines and models. With advanced tools like Python and SQL, along with databases for data warehousing, teams can gather, process, and analyze their data in a bespoke manner. This can reveal unique insights that give an edge, especially when dealing with large amounts of data about influencers and audiences.

In this context, there are available influencer marketing platforms like Flinque that specialize in creator discovery and audience analytics, which can be beneficial in the initial stages of campaign planning. Flinque’s strengths lie in giving access to a global network of influencers and facilitating data-driven campaign management, which can accelerate insights gathering.

The choice of tool or technique depends on your team’s specific needs, available resources, and comfort level with data processing and analysis. Both CRM or BI tools and custom-built solutions could aid in building proprietary discovery intelligence, as could specialized platforms like Flinque. The key is to choose a method that suitably compliments workflows, supports decision-making, and generates valuable insights.