A Bayesian Classification Approach to Improving Performance for a Real-World Sales Forecasting Application

Abstract

Many businesses rely on forecasting techniques to detect whether sales opportunities are likely to be won or at risk of being lost. This enables the businesses to respond proactively. This paper describes a new method of sales forecasting that improves on an existing Qualitative Sales Predictor (QSP) in Hewlett-Packard (HP). QSP is based on a series of qualitative assessments that are made by sales personnel, the results of which are combined using weighted factors. In this research, we have developed an alternative method of forecasting sales opportunities, with three key differences: (1) the qualitative assessments are supplemented with quantitative data describing attributes of the opportunity, (2) we replace the weight factors with a Tree Augmented Naïve Bayes (TAN) classifier that can capture dependences between variables and produces a probabilistic output to which thresholds can be applied, (3) the TAN classifier is of course learned from historical data, whereas the existing QSP has fixed weights. Our approach has an accuracy of 90.6% in predicting whether sales will be won or lost, a substantial improvement on the existing approach's accuracy of 75.6% on the same unseen test data.