×

Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting. (English) Zbl 1132.90349

Summary: Customer relationship management (CRM) enjoys increasing attention as a countermeasure to switching behaviour of customers. Because foregone profits of (partially) defected customers can be significant, an increase of the retention rate can be very profitable. In this paper we focus on the treatment of a company’s most behaviourally loyal customers in a non-contractual setting. We build a model in order to predict partial defection by behaviourally loyal clients using three classification techniques: Logistic regression, automatic relevance determination (ARD) Neural Networks and Random Forests. Focusing on partial attrition of high-frequency shoppers who exhibit a regular visit pattern may overcome the problem of unidentifiability of total defection in non-contractual settings. Classification accuracy (PCC) and area under the receiver operating characteristic curve (AUC) are used to evaluate classifier performance on a test/hold-out sample. Using real-life data from an FMCG retailer, we show that future partial defection can be successfully predicted, i.e. exceeding the benchmark hurdle of the null model. There are no significant differences in terms of performance among alternative classification techniques. Similar to direct-marketing applications we find that past behavioural variables, more specifically RFM variables (recency, frequency, and monetary value) are the best predictors of partial customer defection. This set of variables complements demographic variables confirming findings by other authors about its importance in predicting churn behaviour. Moreover, additional variables (listed in decreasing order of importance) such as the length of customer relationship, mode of payment, buying behaviour across categories, usage of promotions and brand purchase behaviour are shown to be moderately useful to incorporate in attrition models.

MSC:

90B99 Operations research and management science

Software:

SMOTE
PDF BibTeX XML Cite
Full Text: DOI

References:

[1] Anderson, J.A., Logistic discrimination, (), 169-191 · Zbl 0505.62046
[2] Anderson, E.; Weitz, B., Determinants of continuity in convential industrial channel dyads, Marketing science, 8, 4, 310-323, (1989)
[3] Anderson, E.W.; Fornell, C.; Lehmann, D.R., Customer satisfaction, market share, and profitability: findings from Sweden, Journal of marketing, 58, 3, 53-66, (1994)
[4] Athanassopoulos, A.D., Customer satisfaction cues to support market segmentation and explain switching behaviour, Journal of business research, 47, 3, 191-207, (2000)
[5] Baesens, B.; Verstraeten, G.; Van den Poel, D.; Egmont-Petersen, M.; Van Kenhove, P.; Vanthienen, J., Bayesian network classifiers for identifying the slope of the customer lifecycle of long-life customers, European journal of operational research, 156, 2, 242-257, (2004) · Zbl 1056.90019
[6] Baesens, B.; Viaene, S.; Van den Poel, D.; Vanthienen, J.; Dedene, G., Bayesian neural network learning for repeat purchase modelling in direct marketing, European journal of operational research, 138, 1, 191-211, (2002) · Zbl 1008.90515
[7] Bawa, K.; Shoemaker, R.W., The coupon-prone customer: some findings based on purchase behaviour across product classes, Journal of marketing, 51, 4, 99-110, (1987)
[8] Bhattacharya, C.B., When customers are members: customer retention in paid membership contexts, Journal of the Academy of marketing science, 26, 1, 31-45, (1998)
[9] Blattberg, R.C.; Getz, G.; Thomas, J.S., Customer equity: building and managing relationships as valuable assets, (2000), Harvard Business School Press, 228 pp
[10] Bloemer, J.M.M.; Brijs, T.; Vanhoof, K.; Swinnen, G., Comparing complete and partial classification for identifying customers at risk, International journal of research in marketing, 20, 2, 117-216, (2003)
[11] Bolton, R.N., A dynamic model of the duration of the Customer’s relationship with a continuous service provider: the role of satisfaction, Marketing science, 17, 1, 45-65, (1998)
[12] Breiman, L., Bagging predictors, Machine learning, 24, 2, 123-140, (1996) · Zbl 0858.68080
[13] Breiman, L., Random forests, Machine learning, 45, 1, 5-32, (2001) · Zbl 1007.68152
[14] Buckinx, W., Moons, E., Van den Poel, D., Wets, G., 2004. Customer-adapted coupon targeting using feature selection. Expert Systems with Applications, in press
[15] Carroll, P., Revisiting customer retention, Journal of retail banking, 15, 1, 7-13, (1993)
[16] Chawla, N.V.; Bowyer, K.W.; Hall, L.O.; Kegelmeyer, W.P., Smote: synthetic minority over-sampling technique, Journal of artificial intelligence research, 16, 321-357, (2002) · Zbl 0994.68128
[17] Colgate, M.; Stewart, K.; Kinsella, R., Customer defection: A study of the student market in Ireland, International journal of bank marketing, 14, 3, 23-29, (1996)
[18] Corstjens, M.; Lal, R., Building store loyalty through store brands, Journal of marketing research, 37, 3, 281-291, (2000)
[19] Dietterich, T., An experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting and randomization, Machine learning, 40, 2, 139-157, (2000)
[20] Duda, R.O.; Hart, P.E.; Stork, D.G., Pattern classification, (2001), Wiley NY
[21] Dudoit, S.; Fridlyand, J.; Speed, T.P., Comparison of discrimination methods for the classification of tumors using gene expression data, Journal of the American statistical association, 97, 457, 77-87, (2002) · Zbl 1073.62576
[22] Dwyer, R.F., Customer lifetime valuation to support marketing decision making, Journal of direct marketing, 11, 4, 6-13, (1997)
[23] Ganesh, J.; Arnold, M.J.; Reynolds, K., Understanding the customer base of service providers: an examination of the differences between switchers and stayers, Journal of marketing, 64, 3, 65-87, (2000)
[24] Hoekstra, J.C.; Huizingh, E.K.R.E., The lifetime value concept in customer-based marketing, Journal of market focused management, 3, 257-274, (1999)
[25] Jones, M.A.; Mothersbaugh, D.L.; Beatty, S.E., Switching barriers and repurchase intentions in services, Journal of retailing, 76, 2, 259-274, (2000)
[26] Keaveney, S.M.; Parthasarathy, M., Customer switching behaviour in online services: an exploratory study of the role of selected attitudinal, behavioural, and demographic factors, Journal of the Academy of marketing science, 29, 4, 374-390, (2001)
[27] Kim, S.Y.; Staelin, R., Manufacturer allowances and retailer pass-through rates in a competitive environment, Marketing science, 18, 1, 59-76, (1999)
[28] Lemon, K.N.; White, T.B.; Winer, R.S., Dynamic customer relationship management: incorporating future considerations into the service retention decision, Journal of marketing, 66, 1, 1-14, (2002)
[29] Lindgreen, A.; Pels, J., Buyer-seller exchange situations: four empirical cases, Journal of relationship marketing, 1, 3, 69-93, (2002)
[30] MacKay, D.J.C., The evidence framework applied to classification networks, Neural computation, 4, 5, 720-736, (1992)
[31] Mittal, B.; Lassar, W.M., Why do customers switch? the dynamics of satisfaction versus loyalty, Journal of services marketing, 12, 3, 177-194, (1998)
[32] Mittal, V.; Kamakura, W.A., Satisfaction, repurchase intent, and repurchase behaviour: investigating the moderating effect of customer characteristics, Journal of marketing research, 38, 1, 131-142, (2001)
[33] Mizerski, R.W., An attribution explanation of the disproportionate influence of unfavorable information, Journal of consumer research, 9, 3, 301-310, (1982)
[34] Morgan, R.M.; Hunt, S.D., The commitment-trust theory of relationship marketing, Journal of marketing, 58, 3, 20-38, (1994)
[35] Morrison, D.G., On the interpretation of discriminant analysis, Journal of marketing research, 6, 156-163, (1969)
[36] Morwitz, V.G.; Johnson, E.; Schmittlein, D., Does measuring intent change behaviour, Journal of consumer research, 20, 1, 46-61, (1993)
[37] Mozer, M.C.; Wolniewicz, R.; Grimes, D.B.; Johnson, E.; Kaushansky, H., Predicting subscriber dissatisfaction and improving retention in the wireless telecommunications industry, IEEE transactions on neural networks, 11, 3, 690-696, (2000)
[38] Nabney, I.T., NETLAB algorithms for pattern recognition, (2001), Springer Verlag London · Zbl 1011.68116
[39] Nielsen, A.C., 2001. Major study to track store switching, Retail World, August
[40] Niraj, R.; Gupta, M.; Narasimhan, C., Customer profitability in a supply chain, Journal of marketing, 65, 3, 1-16, (2001)
[41] O’Brien, L.; Jones, C., Do rewards really create loyalty?, Harvard business review, 73, 3, 75-83, (1995)
[42] Peelen, E.; Ekelmans, C.F.W.; Vijn, P., Direct marketing for establishing the relationships between buyers and sellers, Journal of direct marketing, 3, 1, 7-14, (1989)
[43] Penny, W.D.; Roberts, S.J., Bayesian neural networks for classification: how useful is the evidence framework, Neural networks, 12, 6, 877-892, (1999)
[44] Peterson, R.A., Relationship marketing and the consumer, Journal of the Academy of marketing science, 23, 4, 278-281, (1995)
[45] Popkowski, L.P.T.L.; Sinha, A.; Timmermans, H.J.P., Consumer store choice dynamics: an analysis of the competitive market structure for grocery stores, Journal of retailing, 76, 3, 323-345, (2000)
[46] Reichheld, F.F., Learning from customer defections, Harvard business review, 74, 2, 56-69, (1996)
[47] Reichheld, F.F.; Sasser, W.E., Zero defections: quality comes to services, Harvard business review, 68, 5, 105-111, (1990)
[48] Reinartz, W.J.; Kumar, V., On the profitability of long-life customers in a noncontractual setting: an empirical investigation and implications for marketing, Journal of marketing, 64, 4, 17-35, (2000)
[49] Reinartz, W.J.; Kumar, V., The mismanagement of customer loyalty, Harvard business review, 80, 7, 86-94, (2002)
[50] Rust, R.T.; Zahorik, A.J., Customer satisfaction, customer retention, and market share, Journal of retailing, 69, 2, 193-215, (1993)
[51] Schmittlein, D.C.; Peterson, R.A., Customer base analysis: an industrial purchase process application, Marketing science, 13, 1, 41-67, (1994)
[52] Sheth, J.N.; Parvatiyar, A., Relationship in consumer markets: antecedents and consequences, Journal of the Academy of marketing science, 23, 4, 255-271, (1995)
[53] Sonnenberg, F., Marketing to win, (1990), Harvard Business School Press
[54] Stum, D.; Thiry, A., Building customer loyalty, Training and development journal, 45, 4, 34-36, (1991)
[55] Vakratsas, D., Household cost effects on purchase timing decisions: do demographics matter?, Journal of consumer marketing, 15, 1, 6-22, (1998)
[56] Van den Poel, D., Predicting mail-order repeat buying: which variables matter?, Tijdschrift voor economie en management, 48, 3, 371-403, (2003)
[57] Van den Poel, D.; Larivi√®re, B., Customer attrition analysis for financial services using proportional hazard models, European journal of operational research, 157, 1, 196-217, (2004) · Zbl 1106.91318
[58] Verhoef, P.C.; Franses, P.H.; Hoekstra, J.C., The effect of relational constructs on customer referrals and number of services purchased from a multiservice provider: does age of relationship matter?, Journal of the Academy of marketing science, 30, 3, 202-216, (2002)
[59] Verstraeten, G.; Van den Poel, D.; Prinzie, A.; Van Kenhove, P., Detecting sequential patterns for cross-selling fast moving consumer goods, (), 521-530
[60] Viaene, S.; Baesens, B.; Van Gestel, T.; Suykens, J.A.K.; Van den Poel, D.; Vanthienen, J.; De Moor, B.; Dedene, G., Knowledge discovery in a direct marketing case using least squares support vector machines, International journal of intelligent systems, 16, 9, 1023-1036, (2002) · Zbl 0995.90054
[61] Weerahandi, S.; Moitra, S., Using survey data to predict adoption and switching for services, Journal of marketing research, 32, 1, 85-96, (1995)
[62] Wu, C.; Chen, H.L., Counting your customers: compounding Customer’s in-store decisions, interpurchase time and repurchasing behaviour, European journal of operational research, 127, 1, 109-119, (2000) · Zbl 0979.90082
[63] Zhang, Q.; Varshney, P.K.; Wesel, R.D., Optimal bi-level quantization of i.i sensor observations for binary hypothesis testing, IEEE transactions on information theory, 48, 7, 2105-2111, (2002) · Zbl 1061.94521
[64] Zeithaml, V.A.; Berry, L.L.; Parasuraman, A., The behavioural consequences of service quality, Journal of marketing, 60, 2, 31-46, (1996)
[65] Ziliani, C., Retail micro-marketing: strategic advance or gimmick?, The international review of retail, distribution and consumer research, 10, 4, 355-368, (2000)
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.