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Analyzing existing customers’ websites to improve the customer acquisition process as well as the profitability prediction in B-to-B marketing

Author

Listed:
  • D. THORLEUCHTER
  • D. VAN DEN POEL
  • A. PRINZIE
Abstract
We investigate the issue of predicting new customers as profitable based on information about existing customers in a business-to-business environment. In particular, we show how latent semantic concepts from textual information of existing customers’ websites can be used to uncover characteristics of websites of companies that will turn into profitable customers. Hence, the use of predictive analytics will help to identify new potential acquisition targets. Additionally, we show that a regression model based on these concepts is successful in the profitability prediction of new customers. In a case study, the acquisition process of a mail-order company is supported by creating a prioritized list of new customers generated by this approach. It is shown that the density of profitable customers in this list outperforms the density of profitable customers in traditional generated address lists (e. g. from list brokers). From a managerial point of view, this approach supports the identification of new business customers and helps to estimate the future profitability of these customers in a company. Consequently, the customer acquisition process can be targeted more effectively and efficiently. This leads to a competitive advantage for B2B companies and improves the acquisition process that is time- and cost-consuming with traditionally low conversion rates.

Suggested Citation

  • 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.
    • Handle: RePEc:rug:rugwps:11/733
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    References listed on IDEAS

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    1. Verhoef, Peter C. & Venkatesan, Rajkumar & McAlister, Leigh & Malthouse, Edward C. & Krafft, Manfred & Ganesan, Shankar, 2010. "CRM in Data-Rich Multichannel Retailing Environments: A Review and Future Research Directions," Journal of Interactive Marketing, Elsevier, vol. 24(2), pages 121-137.
    2. Van den Poel, Dirk & Buckinx, Wouter, 2005. "Predicting online-purchasing behaviour," European Journal of Operational Research, Elsevier, vol. 166(2), pages 557-575, October.
    3. Philippe Baecke & Dirk Van Den Poel, 2010. "Improving Purchasing Behavior Predictions By Data Augmentation With Situational Variables," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 9(06), pages 853-872.
    4. P. Baecke & D. Van Den Poel, 2009. "Data Augmentation by Predicting Spending Pleasure Using Commercially Available External Data," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 09/596, Ghent University, Faculty of Economics and Business Administration.
    5. K. W. De Bock & D. Van Den Poel & S. Manigart, 2009. "Predicting web site audience demographics for web advertising targeting using multi-web site clickstream data," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 09/618, Ghent University, Faculty of Economics and Business Administration.
    6. D. Thorleuchter & D. Van Den Poel & A. Prinzie & -, 2010. "A compared R&D-based and patent-based cross impact analysis for identifying relationships between technologies," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 10/632, Ghent University, Faculty of Economics and Business Administration.
    7. K. Coussement & D. van den Poel, 2008. "Integrating the voice of customers through call center emails into a decision support system for churn prediction," Post-Print hal-00788086, HAL.
    8. D. Thorleuchter & D. Van Den Poel & A. Prinzie & -, 2009. "Mining Ideas from Textual Information," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 09/619, Ghent University, Faculty of Economics and Business Administration.
    9. Scott Deerwester & Susan T. Dumais & George W. Furnas & Thomas K. Landauer & Richard Harshman, 1990. "Indexing by latent semantic analysis," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 41(6), pages 391-407, September.
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    Cited by:

    1. 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.
    2. M. Ballings & D. Van Den Poel & E. Verhagen, 2013. "Evaluating the Added Value of Pictorial Data for Customer Churn Prediction," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/869, Ghent University, Faculty of Economics and Business Administration.
    3. P. Baecke & D. Van Den Poel, 2012. "Including Spatial Interdependence in Customer Acquisition Models: a Cross-Category Comparison," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/788, Ghent University, Faculty of Economics and Business Administration.
    4. Taeyeoun Roh & Yujin Jeong & Hyejin Jang & Byungun Yoon, 2019. "Technology opportunity discovery by structuring user needs based on natural language processing and machine learning," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-27, October.
    5. D. Thorleuchter & D. Van Den Poel, 2012. "Technology Classification with Latent Semantic Indexing," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/814, Ghent University, Faculty of Economics and Business Administration.
    6. Byungun Yoon & Taeyeoun Roh & Hyejin Jang & Dooseob Yun, 2019. "Developing an Risk Signal Detection System Based on Opinion Mining for Financial Decision Support," Sustainability, MDPI, vol. 11(16), pages 1-26, August.
    7. D. Thorleuchter & D. Van Den Poel, 2012. "Protecting Research and Technology from Espionage," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/824, Ghent University, Faculty of Economics and Business Administration.
    8. 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.
    9. Sheng, Jie & Amankwah-Amoah, Joseph & Wang, Xiaojun, 2017. "A multidisciplinary perspective of big data in management research," International Journal of Production Economics, Elsevier, vol. 191(C), pages 97-112.
    10. Ballings, Michel & Van den Poel, Dirk, 2015. "CRM in social media: Predicting increases in Facebook usage frequency," European Journal of Operational Research, Elsevier, vol. 244(1), pages 248-260.
    11. D. Thorleuchter & D. Van Den Poel, 2013. "Semantic Compared Cross Impact Analysis," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/862, Ghent University, Faculty of Economics and Business Administration.
    12. D. Thorleuchter & D. Van Den Poel, 2012. "Improved Multilevel Security with Latent Semantic Indexing," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/811, Ghent University, Faculty of Economics and Business Administration.
    13. D. Thorleuchter & D. Van Den Poel, 2013. "Quantitative Cross Impact Analysis with Latent Semantic Indexing," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/861, Ghent University, Faculty of Economics and Business Administration.
    14. D. Thorleuchter & D. Van Den Poel, 2013. "Weak Signal Identification with Semantic Web Mining," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/860, Ghent University, Faculty of Economics and Business Administration.

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    Keywords

    B-to-B marketing; Text Mining; Web Mining; Acquisition; SVD;
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