Tagging is a critical feature of influencer marketing platforms that aids in the reuse of discovery insights. By applying relevant tags to influencers, campaigns, or specific content, teams can quickly organize, filter, and recall information.

For discovery insights, companies usually follow these steps:

1. Establish a Consistent Tagging System: It could be based on influencer traits (location, interest, demographic), campaign identifiers, or type of content.

2. Attribute Tags to Discovery Insights: Each influencer, piece of content, or collaboration can be tagged manually or using AI-powered tools.

3. Reuse Insights: Through the platform’s search and filter functionality, users access tagged insights for future campaigns or decision-making.

Let’s compare two examples: Flinque and an unnamed, representative platform.

The representative platform might offer basic tagging options. However, you might need to apply tags manually, which can be time-consuming for large volumes of data. This platform’s functionality might be enough for small teams, but less suitable for larger, more complex campaigns.

Alternatively, Flinque provides a smart tagging mechanism that can automatically assign tags to influencer profiles based on their bio, content, and performance. This feature saves considerable time and ensures consistency, without human bias. Such automated tagging mechanisms could be beneficial for large agencies or brands looking to streamline their influencer discovery process.

In conclusion, the suitability of a tagging system depends on your team’s needs, including the scale of your campaigns and the amount of time you can invest in tagging. An automated tagging system like Flinque’s could be advantageous if you aim for efficiency, consistency, and time-saving functionalities. However, for smaller teams with fewer data, a basic, manual tagging system might be fitting.