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Choosing the technique that is the best at forecasting your data, is aproblem that arises in any forecasting application. Decades of research haveresulted into an enormous amount of forecasting methods that stem fromstatistics, econometrics and machine learning (ML), which leads to a verydifficult and elaborate choice to make in any forecasting exercise. This paperaims to facilitate this process for high-level tactical sales forecasts bycomparing a large array of techniques for 35 times series that consist of bothindustry data from the Coca-Cola Company and publicly available datasets.However, instead of solely focusing on the accuracy of the resulting forecasts,this paper introduces a novel and completely automated profit-driven approachthat takes into account the expected profit that a technique can create duringboth the model building and evaluation process. The expected profit functionthat is used for this purpose, is easy to understand and adaptable to anysituation by combining forecasting accuracy with business expertise.Furthermore, we examine the added value of ML techniques, the inclusion ofexternal factors and the use of seasonal models in order to ascertain whichtype of model works best in tactical sales forecasting. Our findings show thatsimple seasonal time series models consistently outperform other methodologiesand that the profit-driven approach can lead to selecting a differentforecasting model.

Profit-oriented sales forecasting: a comparison of forecasting techniques from a business perspective