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Business & Information Systems Engineering

, Volume 60, Issue 2, pp 151–166 | Cite as

Next-Purchase Prediction Using Projections of Discounted Purchasing Sequences

  • Katerina Shapoval
  • Thomas Setzer
Research Paper

Abstract

A primary task of customer relationship management (CRM) is the transformation of customer data into business value related to customer binding and development, for instance, by offering additional products that meet customers’ needs. A customer’s purchasing history (or sequence) is a promising feature to better anticipate customer needs, such as the next purchase intention. To operationalize this feature, sequences need to be aggregated before applying supervised prediction. That is because numerous sequences might exist with little support (number of observations) per unique sequence, discouraging inferences from past observations at the individual sequence level. In this paper the authors propose mechanisms to aggregate sequences to generalized purchasing types. The mechanisms group sequences according to their similarity but allow for giving higher weights to more recent purchases. The observed conversion rate per purchasing type can then be used to predict a customer’s probability of a next purchase and target the customers most prone to purchasing a particular product. The bias–variance trade-off when applying the models to target customers with respect to the lift criterion are discussed. The mechanisms are tested on empirical data in the realm of cross-selling campaigns. Results show that the expected bias–variance behavior well predicts the lift achieved with the mechanisms. Results also show a superior performance of the proposed methods compared to commonly used segmentation-based approaches, different similarity measures, and popular class predictors. While the authors tested the approaches for CRM campaigns, their parameterization can be adjusted to operationalize sequential features of high cardinality also in other domains or business functions.

Keywords

Customer relationship management Campaign management Feature generation Purchasing sequence Next purchase prediction 

References

  1. Back B, Holmbom A, Eklund T (2011) Customer portfolio analysis using the SOM. Int J Bus Inf Syst 8(4):396–412Google Scholar
  2. Baumann A, Lessmann S, Coussement K, De Bock KW (2015) Maximize what matters: predicting customer churn with decision-centric ensemble selection. In: ECIS 2015 completed research papers. http://aisel.aisnet.org/ecis2015_cr/15/. Accessed 25 June 2017
  3. Bicego M, Murino V, Figueiredo MA (2003) Similarity-based clustering of sequences using hidden Markov models. Machine learning and data mining in pattern recognition. Springer, Heidelberg, pp 86–95CrossRefGoogle Scholar
  4. Bose I, Chen X (2009) Quantitative models for direct marketing: a review from systems perspective. Eur J Oper Res 195(1):1–16CrossRefGoogle Scholar
  5. Brown RG (2004) Smoothing, forecasting and prediction of discrete time series. Courier Dover Publications, Mineola, NYGoogle Scholar
  6. Chan CCH (2008) Intelligent value-based customer segmentation method for campaign management: a case study of automobile retailer. Expert Syst Appl 34(4):2754–2762CrossRefGoogle Scholar
  7. Cho YB, Cho YH, Kim SH (2005) Mining changes in customer buying behavior for collaborative recommendations. Expert Syst Appl 28(2):359–369CrossRefGoogle Scholar
  8. Daoud RA, Amine A, Bouikhalene B, Lbibb R (2015) Combining RFM model and clustering techniques for customer value analysis of a company selling online. In: Computer systems and applications (AICCSA), 2015 IEEE/ACS 12th international conference, IEEE, pp 1–6Google Scholar
  9. Domingos P (2000) A unified bias-variance decomposition. In: Proceedings of 17th international conference on machine learning. Morgan Kaufmann, Stanford, CA, pp 231–238Google Scholar
  10. Dunlavy DM, Kolda TG, Acar E (2011) Temporal link prediction using matrix and tensor factorizations. ACM Trans Knowl Discov Data TKDD 5(2):10Google Scholar
  11. Han SH, Lu SX, Leung SC (2012) Segmentation of telecom customers based on customer value by decision tree model. Expert Syst Appl 39(4):3964–3973CrossRefGoogle Scholar
  12. Hsu MW, Lessmann S, Sung MC, Ma T, Johnson JE (2016) Bridging the divide in financial market forecasting: machine learners vs. financial economists. Expert Syst Appl 61:215–234CrossRefGoogle Scholar
  13. James G, Witten D, Hastie T, Tibshirani R (2013) An introduction to statistical learning, vol 6. Springer, HeidelbergCrossRefGoogle Scholar
  14. Joh CH, Timmermans HJ, Popkowski-Leszczyc PT (2003) Identifying purchase-history sensitive shopper segments using scanner panel data and sequence alignment methods. J Retail Consum Serv 10(3):135–144CrossRefGoogle Scholar
  15. Kaski S, Nikkilä J, Kohonen T (1998) Methods for interpreting a self-organized map in data analysis. In: In Proc. 6th European Symposium on Artificial Neural Networks (ESANN98). D-Facto, Brugfes, CiteseerGoogle Scholar
  16. Khajvand M, Tarokh MJ (2011) Estimating customer future value of different customer segments based on adapted RFM model in retail banking context. Proced Comput Sci 3:1327–1332CrossRefGoogle Scholar
  17. Kohonen T (2001) Self-organizing maps. Springer, HeidelbergCrossRefGoogle Scholar
  18. Kruskal JB (1983) An overview of sequence comparison: time warps, string edits, and macromolecules. SIAM Rev 25(2):201–237CrossRefGoogle Scholar
  19. Levenshtein VI (1966) Binary codes capable of correcting deletions, insertions and reversals. Cybern Control Theory 10:845–848Google Scholar
  20. Li S, Sun B, Wilcox RT (2005) Cross-selling sequentially ordered products: an application to consumer banking services. J Mark Res 42(2):233–239CrossRefGoogle Scholar
  21. MacQueen J et al (1967) Some methods for classification and analysis of multivariate observations, vol 1. In: Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, California, pp 281–297Google Scholar
  22. Miguéis V, Van den Poel D, Camanho A, Cunha J (2012) Predicting partial customer churn using Markov for discrimination for modeling first purchase sequences. Adv Data Anal Classif 6(4):337–353CrossRefGoogle Scholar
  23. Moeyersoms J, Martens D (2015) Including high-cardinality attributes in predictive models: A case study in churn prediction in the energy sector. Decis Support Syst 72:72–81CrossRefGoogle Scholar
  24. Moon S, Russell GJ (2008) Predicting product purchase from inferred customer similarity: an autologistic model approach. Manag Sci 54(1):71–82CrossRefGoogle Scholar
  25. Mooney CH, Roddick JF (2013) Sequential pattern mining—approaches and algorithms. ACM Comput Surv 45(2):19:1–19:39CrossRefGoogle Scholar
  26. Netzer O, Lattin JM, Srinivasan V (2008) A hidden Markov model of customer relationship dynamics. Mark Sci 27(2):185–204CrossRefGoogle Scholar
  27. 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):2592–2602CrossRefGoogle Scholar
  28. Park DH, Kim HK, Choi IY, Kim JK (2012) A literature review and classification of recommender systems research. Expert Syst Appl 39(11):10,059–10,072CrossRefGoogle Scholar
  29. Piatetsky-Shapiro G, Masand B (1999) Estimating campaign benefits and modeling lift. In: Proceedings of the fifth ACM SIGKDD international conference on knowledge discovery and data mining, ACM, New York, KDD ’99, pp 185–193. doi: 10.1145/312129.312225
  30. 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):28–45CrossRefGoogle Scholar
  31. Sahoo N, Singh PV, Mukhopadhyay T (2012) A hidden Markov model for collaborative filtering. MIS Q 36(4):1329–1356Google Scholar
  32. Schweidel DA, Bradlow ET, Fader PS (2011) Portfolio dynamics for customers of a multiservice provider. Manag Sci 57(3):471–486CrossRefGoogle Scholar
  33. Shirley KE, Small DS, Lynch KG, Maisto SA, Oslin DW (2010) Hidden Markov models for alcoholism treatment trial data. Ann Appl Stat 4:366–395CrossRefGoogle Scholar
  34. Steinmann S, Silberer G (2010) Clustering customer contact sequences—results of a customer survey in retailing. European Retail Research. Gabler, Wiesbaden, pp 97–120CrossRefGoogle Scholar
  35. Van den Poel D, Buckinx W (2005) Predicting online-purchasing behaviour. Eur J Oper Res 166(2):557–575CrossRefGoogle Scholar
  36. Wong KW, Zhou S, Yang Q, Yeung JMS (2005) Mining customer value: from association rules to direct marketing. Data Min Knowl Discov 11(1):57–79CrossRefGoogle Scholar

Copyright information

© Springer Fachmedien Wiesbaden GmbH 2017

Authors and Affiliations

  1. 1.Karlsruhe Institute of Technology (KIT), Institute of Information Systems and Marketing (IISM)Information and Market Engineering (IM)KarlsruheGermany

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