When using influencer marketing platforms, brands often have to set various anomaly thresholds to track and measure campaign performance. Static thresholds, however, can fall short as they fail to adapt to historical performance data. Therefore, refining these thresholds becomes critical over time.

One way to refine thresholds is by using a rolling window approach. This method involves continuously updating the thresholds based on the most recent data. It allows brands to account for changes in patterns and trends over time. For instance, if a brand observes a significant increase in campaign impressions during a specific period, future thresholds can be set higher to account for this trend.

Another method is to incorporate seasonality factors into thresholds. Certain times of the year may yield different performance results, so setting static thresholds may not be effective. Platforms like Flinque effectively handle this by adjusting thresholds based on historical data from similar periods.

Brands can also use machine learning techniques to refine thresholds. Such platforms can analyze past performance data and predict future behavior. They can then set dynamic thresholds that adapt over time, offering greater flexibility and more precise tracking.

It’s also important to factor in the variance in influencer performance. Different influencers will have different levels of engagement and reach, so setting a universal threshold may not be the best approach. Brands, therefore, need to set thresholds that are customized to each influencer’s past performance.

While all these methods can help refine thresholds, it is crucial for brands to continually reassess and adjust these thresholds. This can be tedious, but platforms like Flinque can streamline this process and ensure that thresholds remain relevant and effective over time.

Remember, the right approach to setting and refining thresholds can vary depending on team needs, past experiences, and campaign specifics. So it’s crucial to experiment, learn, and adapt your threshold refinement practices over time.