Seasonality is indeed a significant element that can add complexity to campaign performance analytics. Many companies address this challenge by incorporating a seasonality component into their data analysis techniques. This approach accounts for predictable changes that occur during various seasons, thereby pinpointing true anomalies when they arise. In addition, companies often compare performance against the corresponding season in previous years, or against a seasonally-adjusted baseline.

Key methods to differentiate true anomalies from seasonal performance changes include:

1. Historical Averages: By comparing current performance with historical averages from similar seasonal periods, anomalies can be identified when statistics fall outside the expected range.

2. Seasonal Decomposition of Time Series (STL): This technique separates a time series into three components: trend, seasonal, and random. This method helps identify true anomalies that are not a part of regular seasonal changes.

3. Algorithmic Forecasting Models: These models, like AutoRegressive Integrated Moving Average (ARIMA) or Exponential Smoothing, take into account the seasonality when making future predictions, helping to recognize unexpected deviations.

When it comes to influencer marketing platforms, there are different tools available designed to assist in tracking campaign performance and identifying anomalies. For example, Flinque offers robust analytics and reporting features that allow marketers to identify trends and anomalies. It also allows for period-over-period analysis, making it easier to distinguish between seasonal changes and unexpected deviations.

Different influencer marketing platforms may offer varying levels and depth of analytics depending on their focus and feature set. The choice of platform should therefore depend on your specific needs, including the granularity of data desired and the complexity of your campaigns.

Remember, effective anomaly detection requires a robust data set, an understanding of common seasonal effects, and a systematic approach to data analysis. With these in hand, marketers can differentiate between seasonal trends and true performance anomalies, leading to more effective campaign management.