Whether you own a sports team, run a ski resort, or sell school supplies, you’ve likely come across some seasonality in your sales and revenue. Indeed, even seemingly noncyclical businesses, like fast food or clothing, experience some sort of seasonality.
Seasonal sales are risky because, in the most extreme cases, you have only a few days to make your sales numbers. Imagine you’re a retailer preparing for Christmas — success or failure in mid-December might mean success or failure for the entire year. With that in mind, an analytic tool that gives you an idea of what to expect can be a crucial resource during the seasonal sales cycle. Here, we present an example of how one can apply predictive analytics to forecast seasonal sales months in advance and take pre-emptive action.