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Mining for trigger events with survival analysis

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Abstract

This paper discusses a new application of data mining, quantifying the importance of responding to trigger events with reactive contacts. Trigger events happen during a customer’s lifecycle and indicate some change in the relationship with the company. If detected early, the company can respond to the problem and retain the customer; otherwise the customer may switch to another company. It is usually easy to identify many potential trigger events. What is needed is a way of prioritizing which events demand interventions. We conceptualize the trigger event problem and show how survival analysis can be used to quantify the importance of addressing various trigger events. The method is illustrated on four real data sets from different industries and countries.

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References

  • Abe N, Verma N, Apte C, Schroko R (2004) Cross channel optimized marketing by reinforcement learning. In: KDD ’04: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, Seattle, WA, USA, ACM Press, 767–772

  • Adomavicius G and Tuzhilin A (2005). Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans Knowl Data Eng 17(6): 734–749

    Article  Google Scholar 

  • Agrawal R, Mannila H, Srikant R, Toivonen H, Inkeri Verkamo A (1996) Fast discovery of assocation rules. In: Fayyad P-S, Smyth, Urthurusamy (eds) Advances in knowledge discovery and data mining, AAAI Press, 307–328

  • Allison P (1995) Survival analysis using the SAS system, SAS Institute

  • Allison P (1996). Fixed effects partial likelihood for repeated events. Sociol Method Res 25(2): 207–222

    Article  Google Scholar 

  • Chamberlain G (1985). Heterogeneity, omitted variable bias and duration dependence. In: Heckman, JJ and Singer, B (eds) Longitudinal analysis of labor market data., pp 3–38. Cambridge University Press, New York

    Google Scholar 

  • Fader P, Hardie B and Lee KL (2005). RFM and CLV: using iso-value curves for customer base analysis. J Market Res XLII: 415–430

    Article  Google Scholar 

  • KDD-1998 Competition. http://www.kdnuggets.com/meetings/kdd98/kdd-cup-98.html

  • Jones TO, Sasser WE (1995) Why satisfied customers defect. Harvard Business Review November–December, 88–99

  • Mannila H (2002) Association rules. In: Klösgen W, Zytkow J (eds) Handbook of data mining and knowledge discovery. Oxford, 344–348

  • Pfeifer P and Bang H (2005). Non-parametric estimation of mean customer lifetime value. J Interact Market 19(4): 48–66

    Article  Google Scholar 

  • Rosset S, Neumann E, Eick U and Vatnik N (2003). Customer lifetime value models for decision support. Data Mining Knowledge Discovery 7: 321–339

    Article  MathSciNet  Google Scholar 

  • Shepard D (1999). The new direct marketing. McGraw Hill, New York

    Google Scholar 

  • Therneau T, Grambsch P (2000) Modeling survival data. Springer

  • Zadrozny B, Elkan C (2001) Learning and making decisions when costs and probabilities are both unknown. In: Proc KDD-2001, 204–213

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Correspondence to Edward C. Malthouse.

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Responsible editor: Eamonn Keogh.

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Malthouse, E.C. Mining for trigger events with survival analysis. Data Min Knowl Disc 15, 383–402 (2007). https://doi.org/10.1007/s10618-007-0074-x

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  • DOI: https://doi.org/10.1007/s10618-007-0074-x

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