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Predicting customer churn from valuable B2B customers in the logistics industry: a case study

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Abstract

This study uncovers the effect of the length, recency, frequency, monetary, and profit (LRFMP) customer value model in a logistics company to predict customer churn. This unique context has useful business implications compared to the main stream customer churn studies where individual customers (rather than business customers) are the main focus. Our results show the five LRFMP variables had a varying effect on customer churn. Specifically length, recency and monetary variables had a significant effect on churn, while the frequency variable only became a top predictor when the variability of the first three variables was limited. The profit variable had never become a significant predictor. Certain other behavioral variables (such as time between transactions) also had an effect on churn. The resulting set of predictors of churn expands the original LRFMP and RFM models with additional insights. Managerial implications were provided.

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References

  • Ahn JH, Han SP, Lee YS (2006) Customer churn analysis: churn determinants and mediation effects of partial defection in the Korean mobile telecommunications service industry. Telecommun Policy 30:552–568

    Article  Google Scholar 

  • Athanassopoulos AD (2000) Customer satisfaction cues to support market segmentation and explain switching behavior. J Bus Res 47(3):191–207

    Article  Google Scholar 

  • Buckinx W, Van den Poel D (2005) Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting. Eur J Oper Res 164(1):252–268

    Article  Google Scholar 

  • Burez J, Van den Poel D (2008) Separating financial from commercial customer churn: a modeling step towards resolving the conflict between the sales and credit department. Expert Syst Appl 35:497–514

    Article  Google Scholar 

  • Chang HC, Tsai HP (2011) Group RFM analysis as a novel framework to discover better customer consumption behavior. Expert Syst Appl 38(12):14499–14513

    Article  Google Scholar 

  • Chang EC, Huang SC, Wu HH (2010) Using K-means method and spectral clustering technique in an outfitter’s value analysis. Qual Quant 44(4):807–815

    Article  Google Scholar 

  • Chen MC, Chiu AL, Chang HH (2005) Mining changes in customer behavior in retail marketing. Expert Syst Appl 28(4):773–781

    Article  Google Scholar 

  • Chen Y, Fu C, Zhu H (2008) A data mining approach to customer segment based on customer value. In: International conference on fuzzy systems and knowledge discovery, pp 513–517

  • Cheng CH, Chen YS (2009) Classifying the segmentation of customer value via RFM model and RS theory. Expert Syst Appl 36(3, Part 1):4176–4184

    Article  Google Scholar 

  • Coussement K, Van den Bossche FAM, De Bock KW (2014) Data accuracy’s impact on segmentation performance: benchmarking RFM analysis, logistic regression, and decision trees. J Bus Res 67(1):2751–2758

    Article  Google Scholar 

  • De Bock KW, Van den Poel D (2012) Reconciling performance and interpretability in customer churn prediction using ensemble learning based on generalized additive models. Expert Syst Appl 39(8):6816–6826

    Article  Google Scholar 

  • Esper TL, Jensen TD, Turnipseed FL, Burton S (2003) The last mile: an examination of effects of online retail delivery strategies on consumers. J Bus Logist 24(2):177–203

    Article  Google Scholar 

  • Fornell C, Wernerfelt B (1987) Defensive marketing strategy by customer complaint management: a theoretical anlaysis. J Mark Res 24(4):337–346

    Article  Google Scholar 

  • Glady N, Baesens B, Croux C (2009) Modeling churn using customer lifetime value. Eur J Oper Res 197(1):402–411

    Article  Google Scholar 

  • Hosseini SMS, Maleki A, Gholamian MR (2010) Cluster analysis using data mining approach to develop CRM methodology to assess the customer loyalty. Expert Syst Appl 37(7):5259–5264

    Article  Google Scholar 

  • Huang BQ, Kechadi TM, Buckley B, Kiernan G, Keogh E, Rashid T (2010) A new feature set with new window techniques for customer churn prediction in land-line telecommunications. Expert Syst Appl 37(5):3657–3665

    Article  Google Scholar 

  • Huang B, Kechadi MT, Buckley B (2012) Customer churn prediction in telecommunications. Expert Syst Appl 39(1):1414–1425

    Article  Google Scholar 

  • Hughes AM (1994) Strategic database marketing. Probus Publishing Company, Chicago

    Google Scholar 

  • Hughes AM (2005) Strategic database marketing. McGraw-Hill, New York City

    Google Scholar 

  • Hung SY, Yen DC, Wang HY (2006) Applying data mining to telecom churn management. Expert Syst Appl 31(3):515–524

    Article  Google Scholar 

  • Hwang H, Jung T, Suh E (2004) An LTV model and customer segmentation based on customer value: a case study on the wireless telecommunication industry. Expert Syst Appl 26(2):181–188

    Article  Google Scholar 

  • Kisioglu P, Topcu YI (2011) Applying Bayesian belief network approach to customer churn analysis: a case study on the telecom industry of Turkey. Expert Syst Appl 38(6):7151–7157

    Article  Google Scholar 

  • Kumar D, Ravi V (2008) Predicting credit card customer churn in banks using data mining. Int J Data Anal Tech Strateg 1(1):4–28

    Article  Google Scholar 

  • Lee J, Lee J, Feick L (2006) Incorporating word-of-mouth effects in estimating customer lifetime value. Database Mark Cust Strateg Manag 14(1):29–39

    Article  Google Scholar 

  • Li DC, Dai WL, Tseng WT (2011a) A two-stage clustering method to analyze customer characteristics to build discriminative customer management: a case of textile manufacturing business. Expert Syst Appl 38(6):7186–7191

    Article  Google Scholar 

  • Li Y, Deng Z, Qian Q, Xu R (2011b) Churn forecast based on two-step classification in security industry. Intell Inf Manag 3(4):160–165

    Google Scholar 

  • Liang YH (2010) Integration of data mining technologies to analyze customer value for the automotive maintenance industry. Expert Syst Appl 37(12):7489–7496

    Article  Google Scholar 

  • Linoff GS, Berry MJ (2011) Data mining techniques: for marketing, sales, and customer relationship management. Wiley, Hoboken

    Google Scholar 

  • Liu CL, Lyons AC (2011) An analysis of third-party logistics performance and service provision. Transp Res Part E Logist Transp Rev 47(4):547–570

    Article  Google Scholar 

  • Liu DR, Shih YY (2005) Hybrid approaches to product recommendation based on customer lifetime value and purchase preferences. J Syst Softw 77(2):181–191

    Article  Google Scholar 

  • McCarty JA, Hastak M (2007) Segmentation approaches in data-mining: a comparison of RFM, CHAID, and logistic regression. J Bus Res 60(6):656–662

    Article  Google Scholar 

  • Miglautsch JR (2000) Thoughts on RFM scoring. J Database Mark 8(1):67–72

    Article  Google Scholar 

  • Miguéis VL, Van den Poel D, Camanho AS, e Cunha JF (2012) Modeling partial customer churn: on the value of first product-category purchase sequences. Expert Syst Appl 39(12):11250–11256

    Article  Google Scholar 

  • Neslin S, Gupta S, Kamakura W, Lu J, Mason C (2006) Defection detection: improving predictive accuracy of customer churn models. J Mark Res 43(2):204–211

    Article  Google Scholar 

  • Nie G, Rowe W, Zhang L, Tian Y, Shi Y (2011) Credit card churn forecasting by logistic regression and decision tree. Expert Syst Appl 38(12):15273–15285

    Article  Google Scholar 

  • Payne A, Frow P (2005) A strategic framework for customer relationship management. J Mark 69(4):167–176

    Article  Google Scholar 

  • Reinartz WJ, Kumar V (2003) The impact of customer relationship characteristics on profitable lifetime duration. J Mark 67(1):77–99

    Article  Google Scholar 

  • Reinartz W, Krafft M, Hoyer WD (2004) The customer relationship management process: its measurement and impact on performance. J Mark Res 41(3):293–305

    Article  Google Scholar 

  • Renko S, Ficko D (2010) New logistics technologies in improving customer value in retailing service. J Retail Consum Serv 17(3):216–223

    Article  Google Scholar 

  • Saradhi VV, Palshikar GK (2011) Employee churn prediction. Expert Syst Appl 38(3):1999–2006

    Article  Google Scholar 

  • Slater SF, Narver JC (2000) Intelligence generation and superior customer value. J Acad Mark Sci 28(1):120–127

    Article  Google Scholar 

  • Tan PN, Steinbach M, Kumar V (2006) Introduction to data mining. Addison Wesley, Boston

    Google Scholar 

  • Tsai CF, Chen MY (2010) Variable selection by association rules for customer churn prediction of multimedia on demand. Expert Syst Appl 37(3):2006–2015

    Article  Google Scholar 

  • Tsai CF, Lu YH (2009) Customer churn prediction by hybrid neural networks. Expert Syst Appl 36(10):12547–12553

    Article  Google Scholar 

  • Van den Poel D, Larivière B (2004) Customer attrition analysis for financial services using proportional hazard models. Eur J Oper Res 157(1):196–217

    Article  Google Scholar 

  • Verbeke W, Dejaeger K, Martens D, Hur J, Baesens B (2012) New insights into churn prediction in the telecommunication sector: a profit driven data mining approach. Eur J Oper Res 218(1):211–229

    Article  Google Scholar 

  • Waters D (2003) Logistics: an introduction to supply chain management. Palgrave Macmillan, New York City

    Google Scholar 

  • Wei CP, Chiu IT (2002) Turning telecommunications call details to churn prediction: a data mining approach. Expert Syst Appl 23(2):103–112

    Article  Google Scholar 

  • Wei JT, Lin SY, Weng CC, Wu HH (2012) A case study of applying LRFM model in market segmentation of a children’s dental clinic. Expert Syst Appl 39(5):5529–5533

    Article  Google Scholar 

  • Xu M, Walton J (2005) Gaining customer knowledge through analytical CRM. Ind Manag Data Syst 105(7):955–971

    Article  Google Scholar 

  • Yeh IC, Yang KJ, Ting TM (2009) Knowledge discovery on RFM model using Bernoulli sequence. Expert Syst Appl 36(3, Part 1):5866–5871

    Article  Google Scholar 

  • Yildiz H, Ravi R, Fairey W (2010) Integrated optimization of customer and supplier logistics at Robert Bosch LLC. Eur J Oper Res 207(1):456–464

    Article  Google Scholar 

  • Yu X, Guo S, Guo J, Huang X (2011) An extended support vector machine forecasting framework for customer churn in e-commerce. Expert Syst Appl 38(3):1425–1430

    Article  Google Scholar 

  • Zhang JQ, Dixit AA, Friedmann RR (2010) Customer loyalty and lifetime value: an empirical investigation of consumer packaged goods. J Mark Theory Pract 18(2):127–140

    Article  Google Scholar 

Download references

Acknowledgments

This research was supported by the National Science Council of the Republic of China under the Grant NSC 102-2410-H-194-104-MY2.

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Authors and Affiliations

  1. Department of Business Information Systems, Western Michigan University, 3344 Schneider Hall, Kalamazoo, MI, 49008-5412, USA

    Kuanchin Chen

  2. Department of Information Management, National Chung Cheng University, Chiayi, 62102, Taiwan, ROC

    Ya-Han Hu & Yi-Cheng Hsieh

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  1. Kuanchin Chen
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Correspondence to Ya-Han Hu.

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Chen, K., Hu, YH. & Hsieh, YC. Predicting customer churn from valuable B2B customers in the logistics industry: a case study. Inf Syst E-Bus Manage 13, 475–494 (2015). https://doi.org/10.1007/s10257-014-0264-1

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  • DOI: https://doi.org/10.1007/s10257-014-0264-1

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