This study aims to investigate how much value is added to traditional sales forecast- ing models in marketing by using modern techniques like factor models, Lasso, elastic net, random forest and boosting methods. A benchmark model uses only the focal brand's own information, while the other models include competitive sales and market- ing activities in various ways. An Average Competitor Model (ACM) summarises all competitive information by averages. Factor-augmented models incorporate all or some competitive information by means of common factors. Lasso and elastic net models shrink the coecient estimates of specic competing brands towards zero by adding a shrinkage penalty to the sum of squared residuals. Random forest averages many tree models obtained from bootstrapped samples. Boosting trees grow many small trees sequentially and then average over all the tree models to deliver forecasts. We use these methods to forecast sales of packaged goods one week ahead and compare their pre- dictive performance. Our empirical results for 169 brands across 31 product categories show that the Lasso and elastic net are the safest methods to employ as they are better than the benchmark for most of the brands. The random forest method has better improvement for some of the brands.

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Keywords Sales forecasting, high-dimensional data, principal components, factor model, Lasso, Elastic Net, random forest, boosting, data mining
Persistent URL
Series Econometric Institute Research Papers
Li, W, Fok, D, & Franses, Ph.H.B.F. (2019). Forecasting own brand sales: Does incorporating competition help? (No. EI2019-35). Econometric Institute Research Papers. Retrieved from