Introduction

Most retailers derive significant revenue from items sold on promotions.  Yet, not many retailers have successfully implemented a promotional forecasting solution.  The reasons stem from several challenges associated with managing promotions and accurately predicting their effects.  Below is a list of three key challenges and proven solutions that make promotional forecasting more practical and successful.  This post is written in the context of forecasting and store replenishment at SKU/Store/Week but the concepts are general.

1. Calculate promotional lifts at an aggregate level

A common challenge to statistical retail demand forecasting is historical sales sparsity at the SKU/Store/Week level.  The well known solution to this challenge is to generate forecasts at an aggregate level.  In RDF, the forecast generation level is referred to as Source Level.  Source level forecasts are then pushed back to the final SKU/Store/Week level using spreading ratios.  The spreading ratios are derived using some form of historical data aggregated over time and possibly other hierarchies as well.  With an appropriate source level, this aggregation/spreading process is well known to generate more accurate forecasts than generating forecasts directly at the final SKU/Store/Week level.
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