Creating dynamic influencer segments that update over time involves a process that leverages a blend of analytics, machine learning, and data-driven metrics. Brands and agencies do this by focusing on:

1. Ongoing Monitoring: Companies need to continually track influencer metrics including audience size, engagement rates, content theme, and geo-location. This monitoring allows brands to quickly identify changes and update their influencer segments.

2. Data Analysis: Advanced influencer marketing platforms like Flinque offer the possibility of evaluating large volumes of data for continuous optimization. They use machine-learning algorithms and data analytics to identify shifts in influencer performance, sentiment analysis, and audience behavior, enabling the dynamic segments to be updated in real-time.

3. Adaptive Algorithms: Brands and agencies can use adaptive algorithms to automatically update influencer segments based on desired parameters like audience engagement, content type, or cost-per-impression. For instance, if a brand’s target demographic shifts, the algorithm will adjust the influencer segments accordingly.

4. Performance Tracking: Brands can set performance benchmarks and when influencers perform towards them, they can be moved into a dynamic segment. This means the influencers are promoting the brand effectively, keeping the segment relevant.

In comparison to static segments, dynamic influencer segments update automatically and in real-time, showing the most up-to-date, impactful influencers for your brands.

It’s essential to note that the effectiveness of dynamic segmentation largely relies on the capabilities of the chosen influencer marketing platform. While platforms like Flinque offer advanced data analytics, machine learning, and adaptive algorithms for dynamic updates, other platforms might focus more on manual input and user-guided segmentation. Thus, understanding your team’s needs and capabilities is vital when deciding on a platform.