Predicting partial customer churn using Markov for discrimination for modeling first purchase sequences

Advances in Data Analysis and Classification

Volumn 6, Issue 4, 2012, Pages 337-353

  Miguéis, Vera L ^a   Van den Poel, Dirk ^b   Camanho, Ana S ^a   Falcão e Cunha, João ^a  

^a UNIVERSITY OF PORTO   (Portugal)

^b GHENT UNIVERSITY   (Belgium)

Author keywords

Churn analysis; Classification; Customer relationship management; Logistic regression; Random forests; Retailing

Indexed keywords

CLASSIFICATION (OF INFORMATION); CUSTOMER SATISFACTION; DATA WAREHOUSES; DECISION TREES; INDUSTRY; LOGISTICS; REGRESSION ANALYSIS;

CHURN ANALYSIS; CUSTOMER RELATIONSHIP MANAGEMENT; LOGISTIC REGRESSIONS; RANDOM FORESTS; RETAILING;

SALES;

EID: 84870472711     PISSN: 18625347     EISSN: 18625355     Source Type: Journal    
DOI: 10.1007/s11634-012-0121-3     Document Type: Article

References (52)

1. Agresti A (1996) An introduction to categorical data analysis. Wiley-Interscience, New York.
2. Bejou D, Ennew CT, Palmer A (1998) Trust, ethics and relationship satisfaction. Int J Bank Market 16: 170-175.
3. Bikhchandani S, Hirshleifer D, Welch I (1992) A theory of fads, fashion, custom, and cultural change as informational cascades. J Political Econ 100(5): 992-1026.
4. Bikhchandani S, Hirshleifer D, Welch I (1998) Learning from the behavior of others: conformity, fads, and informational cascades. J Econ Perspect 12(3): 151-170.
5. Breiman L (1996) Bagging predictors. Mach Learn 24(2): 123-140.
6. Breiman L (2001) Random forests. Mach Learn 45(1): 5-32.
7. 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 Operat Res 164(1): 252-268.
8. Bult JR, Wansbeek T (1995) Optimal selection for direct mail. Market Sci 14(4): 378-394.
9. Burez J, van den Poel D (2009) Handling class imbalance in customer churn prediction. Expert Syst Appl 36: 4626-4636.
10. Coussement K, van den Poel D (2008) Churn prediction in subscription services: an application of support vector machines while comparing two parameter-selection techniques. Expert Syst Appl 34(1): 313-327.
11. DeLong ER, DeLong DM, Clarke-Pearson DL (1988) Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44(3): 837-845.
12. Dhar R, Novemsky N (2002) The effects of goal fulfillment on risk preferences in sequential choice. Adv Consumer Res 29: 6-7.
13. Dick AS, Basu K (1994) Customer loyalty: toward an integrated conceptual framework. J Acad Market Sci 22: 99-113.
14. Dudoit S, Fridlyand J, Speed TP (2002) Comparison of discrimination methods for the classification of tumors using gene expression data. J Am Stat Assoc 97(457): 77-87.
15. Durbin R, Eddy SR, Krogh A, Mitchison G (1998) Biological sequence analysis: probabilistic models of proteins and nucleic acids. Cambridge University Press, Cambridge.
16. Dwyer FR (1989) Customer lifetime valuation to support marketing decision making. J Direct Market 3(4): 8-15.
17. EFMI, CBL (2005) Consumenten trends 2005. Technical report, Rotterdam/Leidschendam.
18. Grover R, Vriens M (2006) The handbook of marketing research: uses, misuses, and future advances. Sage Publications, California.
19. Gujarati D (2002) Basic econometrics. McGraw-Hill/Irwin, New York.
20. Hanley JA, McNeil BJ (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143(1): 29-36.
21. Hung S, Yen DC, Wang H (2006) Applying data mining to telecom churn management. Expert Syst Appl 31(3): 515-524.
22. 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.
23. Kalbfleisch J, Prentice RL (1980) The statistical analysis of failure time data. Wiley, New York.
24. Kamakura W, Ramaswami S, Srivastava R (1991) Applying latent trait analysis in the evaluation of prospects for cross-selling of financial services. Int J Res Market 8(4): 329-349.
25. Kotler P (1999) Marketing management: analysis, planning, implementation, and control. Prentice Hall, New Jersey.
26. Kumar DA, Ravi V (2008) Predicting credit card customer churn in banks using data mining. Int J Data Anal Tech Strateg 1(1): 4-28.
27. Larivire B, van den Poel D (2004) Investigating the role of product features in preventing customer churn, by using survival analysis and choice modeling: the case of financial services. Expert Syst Appl 27(2): 277-285.
28. Larivire B, van den Poel D (2005) Predicting customer retention and profitability by using random forests and regression forests techniques. Expert Syst Appl 29(2): 472-484.
29. Martin C, Clark M, Peck H, Payne A (1995) Relationship marketing for competitive advantage: winning and keeping customers. Butterworth-Heinemann, Oxford.
30. Mavri M, Ioannou G (2008) Customer switching behaviour in greek banking services using survival analysis. Manag Finance 34: 186-197.
31. Mcculloch W, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5(133): 115.
32. Miguéis VL, Camanho AS, Cunha JF (2011) Mining customer loyalty card programs: The improvement of service levels enabled by innovative segmentation and promotions design. Lect Notes Bus Inf Process 82: 83-97.
33. Migueis VL, van den Poel D, Camanho AS, Falcao e Cunha J (2012) Modeling partial customer churn: on the value of first product-category purchase sequences. Expert Syst Appl 39(12): 11250-11256.
34. Morik K, Kpcke H (2004) Analysing customer churn in insurance data: a case study. In: Boulicaut JF, Esposito F, Giannotti F, Pedreschi D (eds) Knowledge discovery in databases, lecture notes in computer science, vol 3202. Springer, Italy, pp 325-336.
35. Morrison DG (1969) On the interpretation of discriminant analysis. J Market Res 6(2): 156-163.
36. Murthy SK (1997) Automatic construction of decision trees from data: a multi-disciplinary survey. Data Min Knowl Discov 2: 345-389.
37. Nakahara T, Yada K (2012) Analyzing consumers' shopping behavior using RFID data and pattern mining. Adv Data Anal Class Special Issue Data Anal Class Market. doi: 10. 1007/s11634-012-0117-z.
38. Neter J, Kutner M, Wasserman W, Nachtsheim C (1996) Applied linear statistical models, 4th edn. McGraw-Hill/Irwin, New York.
39. Ngai E, Xiu L, Chau D (2009) Application of data mining techniques in customer relationship management: a literature review and classification. Expert Syst Appl 36(2, Part 2): 2592-2602.
40. Novemsky N, Dhar R (2005) Goal fulfillment and goal targets in sequential choice. J Consumer Res 32(3): 396-404.
41. Paruelo J, Tomasel F (1997) Prediction of functional characteristics of ecosystems: a comparison of artificial neural networks and regression models. Ecol Modell 98(2-3): 173-186.
42. Peterson RA (1995) Relationship marketing and the consumer. J Acad Market Sci 23: 278-281.
43. Prinzie A, van den Poel D (2006a) Incorporating sequential information into traditional classification models by using an element/position-sensitive SAM. Decis Support Syst 42(2): 508-526.
44. Prinzie A, van den Poel D (2006b) Investigating purchasing-sequence patterns for financial services using markov, MTD and MTDg models. Eur J Operat Res 170(3): 710-734.
45. Prinzie A, van den Poel D (2007) Predicting home-appliance acquisition sequences: Markov/Markov for discrimination and survival analysis for modeling sequential information in NPTB models. Decis Support Syst 44(1): 28-45.
46. Quinlan JR (1992) C4. 5: programs for machine learning. Morgan Kaufmann Publishers Inc, San Francisco.
47. Reichheld FF, Sasser WE Jr (1990) Zero defections: quality comes to services. Harvard Bus Rev 68(5): 105-111.
48. Saren MJ, Tzokas NX (1998) Some dangerous axioms of relationship marketing. J Strateg Market 6(3): 187-196.
49. Strandvik T, Liljander V (1994) Relationship strength in bank services. Theory, methods and applications, relationship marketing. Atlanta, GA, pp 356-359.
50. Verbeke W, Martens D, Mues C, Baesens B (2011) Building comprehensible customer churn prediction models with advanced rule induction techniques. Expert Syst Appl 38(3): 2354-2364.
51. Wei C, Chiu I (2002) Turning telecommunications call details to churn prediction: a data mining approach. Expert Syst Appl 23(2): 103-112.
52. Zeithaml V, Berry L, Parasuraman A (1996) The behavioral consequences of service quality. J Market 60(2): 31-46.