3. Consider growth trends and seasonality
Before forecasting, it is useful to determine your growth trends—either year-to-year or month-to-month depending on your available data and the period you’re forecasting for. Knowing the overall trajectory of an item’s sales can help you adjust your data in missing or faulty demand months. If you rely purely on overall averages, your data may be skewed and thus lead to inaccurate forecasts.
You’ll want to account for seasonality’s effect on sales rates, too. Using January’s average to cover a stock-out in December will throw off seasonality forecasting for a popular holiday item. Instead, look at data from previous years’ seasonal spike, then adjust with recent growth trends. In the case of a seasonal item, you’ll want to use forecasting models that specifically take seasonality into account when making projections (a topic that we’ll be discussing in a future article).
Depending on the predictive analytics software you are using, adjusting your data can be difficult or simple. A forecasting tool like ForecastRx allows for click-and-type data manipulation, which will let you quickly fix data points and spend more time taking advantage of your forecast results.
To show how convenient ForecastRx makes data manipulation—and the effects data optimization can have on your forecasts—we’re offering a free trial of the program that will allow you to test out all of the tips mentioned here!