Faculty

In-Season and Out-of Season Promotion Demand Forecasting

Journal of Retailing accepts article of Jannik Wolters and Prof. Dr. Arnd Huchzermeier

The Journal of Retailing (ERIM P: Impact Factor: 5.873) accepted article by Jannik Wolters and Prof. Dr. Arnd Huchzermeier on Joint In-Season and Out-of Season Promotion Demand Forecasting in a Retail Environment. The article will appear in the special issue on Metrics and Analytics in Retailing. The authors acknowledge the discussion rounds and the data provided by Mr. Marcel Uphues, former Finance Director, and Mr. Stefan Länge, Department Head of Customer Information Management, from the Retailer Real.

Title: Joint In-Season and Out-of Season Promotion Demand Forecasting in a Retail Environment

Abstract: Inaccurate forecasts of demand during promotions diminish the already meager profit margins of retailers. No forecasting method described in the literature can accurately account for the combination of seasonal sales variation and promotion-induced sales peaks over forecasting horizons of several weeks or months. We address this research gap by developing a forecasting method for seasonal, frequently promoted products that generates accurate predictions, can handle a large number of sales series, and requires minimal training data. In our method’s first stage, we forecast the seasonal sales cycle by fitting a harmonic regression model to a decomposed training set, which excludes promotional and holiday sales, and then extrapolate that model to a testing set. In the second stage, we integrate the resulting seasonal forecast into a multiplicative demand function that accounts for consumer stockpiling and captures promotional and holiday sales uplifts. The final model is then fitted using ridge regression. We use sales data from a grocery retailing chain to compare the forecasting accuracy of our method with popular seasonal and promotion demand forecasting models at multiple aggregation levels for both short and long forecasting horizons. The significantly more accurate forecasts generated by our model attest to the merit of the approach developed here.

Key words: demand forecasting, seasonality, price promotions, retailing