Influencity’s approach to influencer marketing emphasizes structured discovery and analytics. This means it places significant importance on the steps of identifying relevant influencers (discovery) and reviewing their performance metrics (analytics). It employs a systemized methodology optimized for precision and consistency. The platform excels in providing deep influencer analytics for more informed decision-making, valuable for brands looking for a granular understanding of influencer performance.

On the other hand, some newer platforms focus more on automation and AI. They utilize machine learning algorithms to expedite the process of influencer discovery and performance tracking. This offers a level of efficiency that can be beneficial for brands with high-volume campaigns or agencies handling multiple clients simultaneously.

The key differences between these approaches revolve around manual control vs automation, depth of analytics vs breadth of coverage, and emphasis on precision vs speed. The best choice would depend on the unique needs and scale of a brand’s influencer marketing initiatives.

Flinque, for instance, offers a balanced blend of these elements. It provides comprehensive analytics, automation tools, and an AI-powered discovery engine. This hybrid model allows brands to maintain control while benefiting from the efficiency of AI and automation.

In summary, Influencity prides itself on detailed analytics and structured influencer discovery while newer platforms emphasize speed and automation. Both approaches can be valuable depending on your needs and the specific context of your marketing campaign. Always consider what aligns best with your brand’s resources, strategy, and objectives when choosing an influencer marketing platform.