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Predicting Customer Profitability During Acquisition: Finding the Optimal Combination of Data Source and Data Mining Technique

J. D’haen, Dirk Van den Poel () and Dirk Thorleuchter

Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium from Ghent University, Faculty of Economics and Business Administration

Abstract: The customer acquisition process is generally a stressful undertaking for sales representatives. Luckily there are models that assist them in selecting the ‘right’ leads to pursue. Two factors play a role in this process: the probability of converting into a customer and the profitability once the lead is in fact a customer. This paper focuses on the latter. It makes two main contributions to the existing literature. Firstly, it investigates the predictive performance of two types of data: web data and commercially available data. The aim is to find out which of these two have the highest accuracy as input predictor for profitability and to research if they improve accuracy even more when combined. Secondly, the predictive performance of different data mining techniques is investigated. Results show that bagged decision trees are consistently higher in accuracy. Web data is better in predicting profitability than commercial data, but combining both is even better. The added value of commercial data is, although statistically significant, fairly limited.

Keywords: marketing analytics; predictive analytics, data source; b2b; web mining; web crawling; bagging; profitability; customer acquisition; external commercial data (search for similar items in EconPapers)
Pages: 12 pages
Date: 2012-10
New Economics Papers: this item is included in nep-for
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