Definition
Holt-Winters is a forecasting method that extends simple exponential smoothing to account for both trend and seasonality at the same time, making it one of the more capable classical forecasting techniques for contact centre volume.
It works by tracking three components separately: the current level, the trend direction, and a repeating seasonal pattern. These are then combined to project forward.
It comes in additive and multiplicative variants depending on whether seasonal swings stay a constant size or grow proportionally with volume.
Holt-Winters generally outperforms simpler methods, such as moving averages and basic exponential smoothing, on data with a clear repeating pattern, like weekly or monthly seasonality. It needs enough historical cycles to learn the pattern reliably, usually at least two to three full seasonal cycles of clean history. This method is available in the Forecasting Toolkit on this site.
Why it matters
- Holt-Winters captures both growth and repeating patterns, which makes it a strong choice for contact centre volume with weekly seasonality.
- Choosing between additive and multiplicative variants matters: multiplicative is usually better when volume grows but the seasonal percentage stays stable.
- It still needs clean history; a few months of noisy data will not produce a reliable seasonal pattern.
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