# When is the best time to reactivate your inactive customers?

- 591 Downloads
- 3 Citations

## Abstract

In a noncontractual setting, it is impossible to tell whether a specific customer is still active or he/she has already defected. A popular approach to identify active customers is to calculate the probability of being active based on the Pareto/negative binomial distribution (NBD) model. Building on the Pareto/NBD, we assume that a firm can take direct marketing actions to reactivate an “inactive” customer with a certain cost. So, the firm has to determine the optimal cutoff threshold of the probability of being active to identify inactive customers to trigger reactivations. We propose a continuous time dynamic model, which aims at maximizing customer lifetime value and finding the optimal time to reactivate inactive customers. We develop a Markov chain Monte Carlo algorithm to obtain the model parameters at individual level. The empirical study shows that selecting optimal threshold for reactivation can be a profitable strategy to influence the lifetime value of customers.

## Keywords

CRM Pareto/NBD Customer lifetime value Churn analysis## Notes

### Acknowledgments

The author acknowledges the ongoing support of the National Natural Science Foundation of China under grant nos. 70871057, 71171100, and 71273121.

## References

- Chandar, M., A. Laha, Krishna, P. (2006). Modeling churn behavior of bank customers using predictive data mining techniques. National Conference on Soft Computing Techniques For Engineering Applications, March 24–26, Rourkela, India.Google Scholar
- Fader, P. S., Hardie, B. G. S., & Lee, K. L. (2005a). Counting your customers the easy way: an alternative to the Pareto/NBD model.
*Marketing Science, 24*(2), 275–84.CrossRefGoogle Scholar - Fader, P. S., Hardie, B. G. S., & Lee, K. L. (2005b). RFM and CLV: using iso-value curves for customer base analysis.
*Journal of Marketing Research, 42*(4), 415–30.CrossRefGoogle Scholar - Gelman, A., & Rubin, D. B. (1992). Inference from iterative simulation using multiple sequences.
*Statistical Science 7*(4), 457–511.Google Scholar - Glady, N., Baesens, B., & Croux, C. (2006). Modeling churn using customer lifetime value.
*European Journal of Operational Research, 197*(1), 402–411.CrossRefGoogle Scholar - Glady, N., Baesens, B., & Croux, C. (2009). A modified Pareto/NBD approach for predicting customer lifetime value.
*Expert Systems with Applications, 36*(2), 2062–2071.CrossRefGoogle Scholar - Ma, S. H. J., & Büschken. (2011). Counting your customers from an “always a share” perspective.
*Marketing letters, 22*(3), 243–257.CrossRefGoogle Scholar - Ma, S.H., Liu, J.L. (2007). The MCMC approach for solving the Pareto/NBD model and possible extensions. Proc. Third Internat. Conf. Natural Computation (ICNC) Vol. 2. IEEE, Washington, DC, 505–512.Google Scholar
- Montoya, R., Netzer, O., & Jedidi, K. (2010). Dynamic allocation of pharmaceutical detailing and sampling for long term profitability.
*Marketing Science, 29*(5), 909–924.CrossRefGoogle Scholar - Reinartz, W., & Kumar, V. (2000). On the profitability of long-life customers in a non-contractual setting: an empirical investigation and implications for marketing.
*Journal of Marketing, 64*(4), 17–35.CrossRefGoogle Scholar - Reinartz, W., & Kumar, V. (2003). The impact of customer relationship characteristics on profitable lifetime duration.
*Journal of Marketing, 67*(1), 77–99.CrossRefGoogle Scholar - Rossi, P. E., & Allenby, G. M. (2003). Bayesian statistics and marketing.
*Marketing Science, 22*, 304–328.CrossRefGoogle Scholar - Sharma, S. (1996).
*Applied multivariate techniques*. New York: Wiley.Google Scholar - Schmittlein, D. C., Morrison, D. G., & Colombo, R. (1987). Counting your customers: who are they and what will they do next?
*Management Science, 33*(1), 1–24.CrossRefGoogle Scholar - Schmittlein, D. C., & Peterson, R. A. (1994). Customer base analysis: an industrial purchase process application.
*Marketing Science, 13*(1), 41–67.CrossRefGoogle Scholar - Schweidel, D. A., Knox, G. (2013). Incorporating direct marketing activity into latent attrition models.
*Marketing Science, 32*(3), 471–487.Google Scholar - Wübben, M., & Wangenheim, F. (2008). Instant customer base analysis: managerial heuristics often ‘get it right’.
*Journal of Marketing, 72*(May), 82–93.CrossRefGoogle Scholar