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Model-supported business-to-business prospect prediction based on an iterative customer acquisition framework

Author

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  • J. D’HAEN
  • D. VAN DEN POEL

Abstract

This article discusses a model designed to help sales representatives acquire customers in a business-to-business environment. Sales representatives are often overwhelmed by available information, so they use arbitrary rules to select leads to pursue. The goal of the proposed model is to generate a high-quality list of prospects that are easier to convert into leads and ultimately customers in three phases: Phase 1 occurs when there is only information on the current customer base and uses the nearest neighbor method to obtain predictions. As soon as there is information on companies that did not become customers, phase 2 initiates, triggering a feedback loop to optimize and stabilize the model. This phase uses logistic regression, decision trees, and neural networks. Phase 3 combines phases 1 and 2 into a weighted list of prospects. Preliminary tests indicate the good quality of the model. The study makes two theoretical contributions: First, the authors offer a standardized version of the customer acquisition framework, and second, they point out the iterative aspects of this process.

Suggested Citation

  • J. D’Haen & D. Van Den Poel, 2013. "Model-supported business-to-business prospect prediction based on an iterative customer acquisition framework," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/863, Ghent University, Faculty of Economics and Business Administration.
  • Handle: RePEc:rug:rugwps:13/863
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    File URL: http://wps-feb.ugent.be/Papers/wp_13_863.pdf
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    References listed on IDEAS

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    1. YongSeog Kim & W. Nick Street & Gary J. Russell & Filippo Menczer, 2005. "Customer Targeting: A Neural Network Approach Guided by Genetic Algorithms," Management Science, INFORMS, vol. 51(2), pages 264-276, February.
    2. Wagner Kamakura & Carl Mela & Asim Ansari & Anand Bodapati & Pete Fader & Raghuram Iyengar & Prasad Naik & Scott Neslin & Baohong Sun & Peter Verhoef & Michel Wedel & Ron Wilcox, 2005. "Choice Models and Customer Relationship Management," Marketing Letters, Springer, vol. 16(3), pages 279-291, December.
    3. J. D’Haen & D. Van Den Poel & D. Thorleuchter, 2012. "Predicting Customer Profitability During Acquisition: Finding the Optimal Combination of Data Source and Data Mining Technique," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/818, Ghent University, Faculty of Economics and Business Administration.
    4. Bose, Indranil & Chen, Xi, 2009. "Quantitative models for direct marketing: A review from systems perspective," European Journal of Operational Research, Elsevier, vol. 195(1), pages 1-16, May.
    5. Danielson, Mats & Ekenberg, Love, 2007. "Computing upper and lower bounds in interval decision trees," European Journal of Operational Research, Elsevier, vol. 181(2), pages 808-816, September.
    6. Van den Poel, Dirk & Buckinx, Wouter, 2005. "Predicting online-purchasing behaviour," European Journal of Operational Research, Elsevier, vol. 166(2), pages 557-575, October.
    7. D. Thorleuchter & D. Van Den Poel & A. Prinzie, 2011. "Analyzing existing customers’ websites to improve the customer acquisition process as well as the profitability prediction in B-to-B marketing," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 11/733, Ghent University, Faculty of Economics and Business Administration.
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    Cited by:

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    2. Vlačić, Božidar & Corbo, Leonardo & Costa e Silva, Susana & Dabić, Marina, 2021. "The evolving role of artificial intelligence in marketing: A review and research agenda," Journal of Business Research, Elsevier, vol. 128(C), pages 187-203.

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    Keywords

    customer acquisition; sales funnel; prospects; nearest neighbor; decision tree; neural network;
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