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Sales Prediction for Franchise Stores Using Artificial Neural Networks

The study aims to describe the utility of artificial neural networks as a useful tool for sales prediction modelling for franchise stores. Sales prediction modelling usually involves determining the main sales performance drivers, the magnitude of their influences on sales performance, and develop a model with the best predictive power incorporating this information. This information among other things enables the decision makers to understand the main drivers behind their sales, review the performance of their current stores, and establish new profitable stores by the help of accurate forecasts. Traditionally, statistical models, usually of the regression form have assisted with these predictions. Artificial neural networks are non-parametric statistical estimators and have potential to be used for sales prediction modelling in cases where the data does not necessarily adhere to the low order polynomial form, or for data where little or no a priori knowledge is available. This study uses a multilayer perceptron algorithm with backpropagation to discover the main drivers. The drivers in the final model fall under the broad categories of store characteristics, competition, demographic and generators, these make practical sense and check with historical evidence in this domain. Two dummy variables also have emerged significant in the study which correspond to the 3 types of stores our collaborator operates. Variable selection and weight analysis of final set of variables are done using a method of sensitivity analysis, the combined effects of the drivers on sales revenue are unknown. The model is validated using 3-fold crossvalidation and an R-Square of 79% is obtained. The end result is a simple model with 10 drivers and high predictive power.