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Acquiring Insurance Customer: The CHAID Way

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  • P H Anantha Desik
  • Samarendra Behera

Abstract

In view of the recent regulatory changes and volatile market conditions, the insurance and finance industry started focusing more on developing strategies to find new customer segments. Acquiring new customers is difficult especially in a fiercely competitive industry like insurance. Also, the dynamic nature of pricing for insurance products makes it even more challenging. In this context, it is essential for an insurer to have knowledge about customers, plan customer-centric offerings, attract more profitable customers and increase the bottom line. In the changing market and economic conditions, the insurance industry has started considering customer-centric rather than a product-centric view to well serve the customers. Information Technology (IT)-driven data analytics now has the capability to discover knowledge hidden inside very large amount of data to help in making business decisions which are customized to customer needs. This paper is an attempt to create business rules from customer lead data which will help in identifying customer segments for better marketing campaign and to acquire new customers, and also to explore answers for specific business problems like low lead conversion ratio, important attributes influencing lead conversion and right customer profile to optimize lead conversion.

Suggested Citation

  • P H Anantha Desik & Samarendra Behera, 2012. "Acquiring Insurance Customer: The CHAID Way," The IUP Journal of Knowledge Management, IUP Publications, vol. 0(3), pages 7-13, July.
    • Handle: RePEc:icf:icfjkm:v:10:y:2012:i:3:p:7-13
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    Cited by:

    1. Raúl Ojeda Villareal & Humberto Valencia Herrera, 2013. "Modelos predictivos de riesgo en tarjeta de crédito con el algoritmo CHAID exhaustivo," Revista de Administración, Finanzas y Economía (Journal of Management, Finance and Economics), Tecnológico de Monterrey, Campus Ciudad de México, vol. 7(1), pages 15-37.
    2. Emer Owens & Barry Sheehan & Martin Mullins & Martin Cunneen & Juliane Ressel & German Castignani, 2022. "Explainable Artificial Intelligence (XAI) in Insurance," Risks, MDPI, vol. 10(12), pages 1-50, December.

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