IDEAS home Printed from https://ideas.repec.org/a/eee/joreco/v17y2010i4p306-312.html
   My bibliography  Save this article

The importance of understanding the exchange context when developing a decision support tool to target prospective customers of business insurance

Author

Listed:
  • Soopramanien, Didier
  • Hong Juan, Liu

Abstract

Companies use decision support tools that enable them to efficiently manage their customers. When targeting new prospective customers, companies need to be able to select those customers who are not only more likely to respond to their marketing activities but are also going to buy their products. There is a lot of research about customer relationship Management and the analytical models that can be used to effectively select and manage customers. There is however less attention that is given to the actual process of developing and building a marketing decision support tool. In this paper, we pay particular attention to the construction process of a marketing decision support tool. A key contribution of the paper is that we propose that companies should pay more attention to studying their exchange context and its unique features when they are developing analytical models to support marketing decisions. We illustrate the research contribution through the story of a company that is involved in the direct marketing of business insurance and faces the need to implement a better targeting model.

Suggested Citation

  • Soopramanien, Didier & Hong Juan, Liu, 2010. "The importance of understanding the exchange context when developing a decision support tool to target prospective customers of business insurance," Journal of Retailing and Consumer Services, Elsevier, vol. 17(4), pages 306-312.
    • Handle: RePEc:eee:joreco:v:17:y:2010:i:4:p:306-312
    DOI: 10.1016/j.jretconser.2010.03.002
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0969698910000172
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jretconser.2010.03.002?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Terho, Harri & Halinen, Aino, 2007. "Customer portfolio analysis practices in different exchange contexts," Journal of Business Research, Elsevier, vol. 60(7), pages 720-730, July.
    2. Farquhar, Jillian Dawes & Panther, Tracy, 2008. "Acquiring and retaining customers in UK banks: An exploratory study," Journal of Retailing and Consumer Services, Elsevier, vol. 15(1), pages 9-21.
    3. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shah, Purvi, 2020. "Managing customer reactions to brand deletion in B2B and B2C contexts," Journal of Retailing and Consumer Services, Elsevier, vol. 57(C).
    2. Mercedes Esteban-Bravo & Jose M. Vidal-Sanz & Gökhan Yildirim, 2014. "Valuing Customer Portfolios with Endogenous Mass and Direct Marketing Interventions Using a Stochastic Dynamic Programming Decomposition," Marketing Science, INFORMS, vol. 33(5), pages 621-640, September.
    3. Todor Krastevich, 2013. "Using Predictive Modeling to Improve Direct Marketing Performance," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 3, pages 25-55.
    4. Coussement, Kristof & De Bock, Koen W., 2013. "Customer churn prediction in the online gambling industry: The beneficial effect of ensemble learning," Journal of Business Research, Elsevier, vol. 66(9), pages 1629-1636.
    5. Thakur, Ramendra & Workman, Letty, 2016. "Customer portfolio management (CPM) for improved customer relationship management (CRM): Are your customers platinum, gold, silver, or bronze?," Journal of Business Research, Elsevier, vol. 69(10), pages 4095-4102.
    6. Steven Debaere & Floris Devriendt & Johanna Brunneder & Wouter Verbeke & Tom de Ruyck & Kristof Coussement, 2019. "Reducing inferior member community participation using uplift modeling: Evidence from a field experiment," Post-Print hal-02990787, HAL.
    7. Kamaal Allil, 2024. "Integrating AI-driven marketing analytics techniques into the classroom: pedagogical strategies for enhancing student engagement and future business success," Journal of Marketing Analytics, Palgrave Macmillan, vol. 12(2), pages 142-168, June.
    8. Muhammad Naeem Anjum & Bi Xiuchun & Jaffar Abbas & Zhang Shuguang, 2017. "Analyzing predictors of customer satisfaction and assessment of retail banking problems in Pakistan," Cogent Business & Management, Taylor & Francis Journals, vol. 4(1), pages 1338842-133, January.
    9. Hossein Hassani & Xu Huang & Emmanuel Silva & Mansi Ghodsi, 2020. "Deep Learning and Implementations in Banking," Annals of Data Science, Springer, vol. 7(3), pages 433-446, September.
    10. Park, Chang Hee & Yoon, Tae Jung, 2022. "The dark side of up-selling promotions: Evidence from an analysis of cross-brand purchase behavior☆," Journal of Retailing, Elsevier, vol. 98(4), pages 647-666.
    11. Gattermann-Itschert, Theresa & Thonemann, Ulrich W., 2021. "How training on multiple time slices improves performance in churn prediction," European Journal of Operational Research, Elsevier, vol. 295(2), pages 664-674.
    12. Yan Dong & Yuliang Yao & Tony Haitao Cui, 2011. "When Acquisition Spoils Retention: Direct Selling vs. Delegation Under CRM," Management Science, INFORMS, vol. 57(7), pages 1288-1299, July.
    13. Ding‐Wen Tan & William Yeoh & Yee Ling Boo & Soung‐Yue Liew, 2013. "The Impact Of Feature Selection: A Data‐Mining Application In Direct Marketing," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 20(1), pages 23-38, January.
    14. Leigh McAlister & Shameek Sinha, 2021. "A customer portfolio management model that relates company’s marketing to its long-term survival," Journal of the Academy of Marketing Science, Springer, vol. 49(3), pages 584-600, May.
    15. Geuens, Stijn & Coussement, Kristof & De Bock, Koen W., 2018. "A framework for configuring collaborative filtering-based recommendations derived from purchase data," European Journal of Operational Research, Elsevier, vol. 265(1), pages 208-218.
    16. Chun, Young H., 2012. "Monte Carlo analysis of estimation methods for the prediction of customer response patterns in direct marketing," European Journal of Operational Research, Elsevier, vol. 217(3), pages 673-678.
    17. Stefan Lessmann & Stefan Voß, 2010. "Customer-Centric Decision Support," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 2(2), pages 79-93, April.
    18. Gitae Kim & Bongsug Chae & David Olson, 2013. "A support vector machine (SVM) approach to imbalanced datasets of customer responses: comparison with other customer response models," Service Business, Springer;Pan-Pacific Business Association, vol. 7(1), pages 167-182, March.
    19. Coussement, Kristof & Buckinx, Wouter, 2011. "A probability-mapping algorithm for calibrating the posterior probabilities: A direct marketing application," European Journal of Operational Research, Elsevier, vol. 214(3), pages 732-738, November.
    20. Talla Nobibon, Fabrice & Leus, Roel & Spieksma, Frits C.R., 2011. "Optimization models for targeted offers in direct marketing: Exact and heuristic algorithms," European Journal of Operational Research, Elsevier, vol. 210(3), pages 670-683, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:joreco:v:17:y:2010:i:4:p:306-312. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/journal-of-retailing-and-consumer-services .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.