Investigating the post-complaint period by means of survival analysis
Firms increasingly view each contact with their customers as an opportunity that needs to be managed. The primary purpose of this article is to gain a better understanding of the customers’ post-complaint period. Specific focus is placed on the impact of effective complaint handling on actual customer behavior throughout the time, whereas previous research has mainly focused on time-invariant or intentional measures. Survival analysis techniques are used to investigate the longitudinal behavior of complainants after their problem recovery. The proportionality assumption is tested for each explanatory variable under investigation. In addition, the impact for each variable is estimated by means of survival forests. Survival forests enable us to explore the evolution over time of the effects of the covariates under investigation. As such, the impact of each explanatory variable is allowed to change when the experiment evolves over time, in contrast to “proportional” models that restrict these estimates to be stationary. Our research is performed in the context of a financial services provider and analyzes the post-complaint periods of 2,326 customers. Our findings indicate that (i) it is interesting to consider complainants since they represent a typical and rather active customer segment, (ii) furthermore, it is beneficiary to invest in complaint handling, since these investments are likely to influence customers’ future behavior and (iii) survival forests are a helpful tool to investigate the impact of complaint handling on future customer behavior, since its components provide evidence of changing effects over time.
|Date of creation:||Mar 2005|
|Date of revision:|
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- Kumar, Dhananjay & Westberg, Ulf, 1997. "Maintenance scheduling under age replacement policy using proportional hazards model and TTT-plotting," European Journal of Operational Research, Elsevier, vol. 99(3), pages 507-515, June.
- Maxham, James III, 2001. "Service recovery's influence on consumer satisfaction, positive word-of-mouth, and purchase intentions," Journal of Business Research, Elsevier, vol. 54(1), pages 11-24, October.
- Van den Poel, Dirk & Lariviere, Bart, 2004.
"Customer attrition analysis for financial services using proportional hazard models,"
European Journal of Operational Research,
Elsevier, vol. 157(1), pages 196-217, August.
- D. Van Den Poel & B. Larivière, 2003. "Customer Attrition Analysis For Financial Services Using Proportional Hazard Models," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 03/164, Ghent University, Faculty of Economics and Business Administration.
- B. Larivière & D. Van Den Poel, 2004. "Investigating the role of product features in preventing customer churn, by using survival analysis and choice modeling: The case of financial services," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 04/223, Ghent University, Faculty of Economics and Business Administration.
- Martin R. Young & Wayne S. DeSarbo & Vicki G. Morwitz, 1998. "The Stochastic Modeling of Purchase Intentions and Behavior," Management Science, INFORMS, vol. 44(2), pages 188-202, February.
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