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Combining visual customer segmentation and response modeling

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Abstract

Customer relationship management is a central part of Business Intelligence, and sales campaigns are often used for improving customer relationships. This paper uses advanced analytics to explore customer behavior during sales campaigns. We provide a visual, data-driven and efficient framework for customer-segmentation and campaign-response modeling. First, the customers are grouped by purchasing behavior characteristics using a self-organizing map. To this behavioral segmentation model, we link segment-migration patterns using feature plane representations. This enables visual monitoring of the customer base and tracking customer behavior before and during sales campaigns. In addition to the general segment-migration patterns, this method provides the capability to drill down into each segment to visually explore the dynamics. The framework is applied to a department store chain with more than 1 million customers.

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References

  1. Shah D, Rust RT, Parasuraman A, Staelin R, Day GS (2006) The path to customer centricity. J Serv Res 9(2):113–124

    Article  Google Scholar 

  2. Berry LL (2002) Relationship marketing of services perspectives from 1983 and 2000. J Relatsh Mark 1(1):59–77

    Google Scholar 

  3. Day GS (1997) Aligning the organization to the market. In: Lehmann DR, Jocz KE (eds) Reflections on the futures of marketing. MA: Marketing Science Institute, Cambridge, pp 67–93

    Google Scholar 

  4. Jayachandran S, Sharma S, Kaufman P, Raman P (2005) The role of relational information processes and technology use in customer relationship management. J Mark 69(4):177–192

    Article  Google Scholar 

  5. Kumar V, Venkatesan R, Reinartz W (2008) Performance implications of adopting a customer-focused sales campaign. J Mark 72(5):50–68

    Article  Google Scholar 

  6. Rygielski C, Wang JC, Yen DC (2002) Data mining techniques for customer relationship management. Technol Soc 24(4):483–502

    Article  Google Scholar 

  7. Armbrust M, Fox A, Griffith R, Joseph A, Katz R, Konwinski A, Lee G, Patterson D, Rabkin A, Stoica I, Zaharia M (2010) A view of cloud computing. Commun ACM 53(4):50–58

    Article  Google Scholar 

  8. 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 

  9. Holmbom AH, Eklund T, Back B (2011) Customer portfolio analysis using the SOM. Int J Bus Inf Syst 8(4):396–412

    Google Scholar 

  10. Mo J, Kiang MY, Zou P, Li Y (2010) A two-stage clustering approach for multi-region segmentation. Expert Syst Appl 37(10):7120–7131

    Article  Google Scholar 

  11. Yao Z, Holmbom A, Eklund T, Back B (2010) Combining unsupervised and supervised data mining techniques for conducting customer portfolio analysis. In: Proceedings of the 10th industrial conference on data mining, Springer, Germany, Berlin

  12. Dennis C, Marsland D, Cockett T (2001) Data mining for shopping centres—customer knowledge-management framework. J Knowl Manag 5(4):368–374

    Article  Google Scholar 

  13. 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 

  14. Kuo R, Ho L, Hu C (2002) Integration of self-organizing feature map and K-means algorithm for market segmentation. Comput Oper Res 29(11):1475–1493

    Article  MATH  Google Scholar 

  15. 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 

  16. Grieser L, Wilde KD (2010) Adaptive right-time technologies in customer relationship management. Bus Inf Syst Eng 2(1):41–44

    Article  Google Scholar 

  17. Ganti V, Gehrke J, Ramakrishnan R (1999) A framework for measuring changes in data characteristics. In: Proceedings of the eighteenth ACM SIGMOD-SIGACT-SIGART symposium on principles of database systems, ACM, Philadelphia, United States

  18. Chakrabarti S, Sarawagi S, Dom B (1998) Mining surprising patterns using temporal description length. In: Proceeding of the 24th international conference on very large data bases (VLDB ‘98), Morgan Kaufmann Publishers Inc., San Francisco, USA

  19. Yairi T, Kato Y, Hori K (2001) Fault detection by mining association rules from house-keeping data. In: Proceeding of the international symposium on artificial intelligence, robotics and automation in space, Montreal, Canada

  20. Keogh E, Lonardi S, Chiu BY (2002) Finding surprising patterns in a time series database in linear time and space. In: Proceeding of the eighth ACM SIGKDD international conference on knowledge discovery and data mining, ACM, Edmonton, Canada

  21. Liao TW (2005) Clustering of time series data—a survey. Pattern Recogn 38(11):1857–1874

    Article  MATH  Google Scholar 

  22. D’Urso P, De Giovanni L (2008) Temporal self-organizing maps for telecommunications market segmentation. Neurocomputing 71(13):2880–2892

    Article  Google Scholar 

  23. Denny, Squire DM (2005) Visualization of cluster changes by comparing self-organizing maps. In: Proceedings of the 9th Pacific-Asia conference on knowledge discovery and data mining (PAKDD), Springer, Hanoi, Vietnam

  24. Denny, Williams GJ, Christen P (2008) ReDSOM: relative density visualization of temporal changes in cluster structures using self-organizing maps. In: Proceedings of the eighth IEEE international conference on data mining (ICDM), IEEE, Pisa, Italy

  25. Denny, Williams GJ, Christen P (2010) Visualizing temporal cluster changes using relative density self-organizing maps. Knowl Inf Syst 25(2):281–302

    Article  Google Scholar 

  26. Yao Z, Sarlin P, Eklund T, Back B (2012) Temporal customer segmentation using the self-organizing time map. In: Proceeding of the 16th international conference on information visualisation (IV), IEEE, Montpellier, France

  27. Sarlin P (2013) Self-organizing time map: an abstraction of temporal multivariate patterns. Neurocomputing 99(1):496–508

    Article  Google Scholar 

  28. Lingras P, Hogo M, Snorek M, West C (2005) Temporal analysis of clusters of supermarket customers: conventional versus interval set approach. Inf Sci 172(1–2):215–240

    Article  Google Scholar 

  29. Ha SH, Bae SM, Park SC (2002) Customer’s time-variant purchase behavior and corresponding marketing strategies: an online retailer’s case. Comput Ind Eng 43(4):801–820

    Article  Google Scholar 

  30. Denny, Christen P, Williams GJ (2012) Analysis of cluster migrations using self-organizing maps. In: Cao L, Huang J, Bailey J, Koh Y, Luo J (eds) New frontiers in applied data mining, vol 7104. Springer, Berlin, pp 171–182

    Chapter  Google Scholar 

  31. Basu AK, Basu A, Batra R (1995) Modeling the response pattern to direct marketing campaigns. J Mark Res 32(2):204–212

    Article  Google Scholar 

  32. Piatetsky-Shapiro G, Masand B (1999) Estimating campaign benefits and modeling lift. In: Proceeding of the fifth ACM SIGKDD international conference on knowledge discovery and data mining, ACM, San Diego, United States

  33. Lessmann S, Voß S (2010) Customer-centric decision support. Bus Inf Syst Eng 2(2):79–93

    Article  Google Scholar 

  34. Sarlin P, Yao Z, Eklund T (2012) A framework for state transitions on the self-organizing map: some temporal financial applications. Intell Syst Account Financ Manag 19(3):189–203

    Article  Google Scholar 

  35. Ferreira de Oliveira MC, Levkowitz H (2003) From visual data exploration to visual data mining: a survey. IEEE Trans Visual Comput Graphics 9(3):378–394

    Article  Google Scholar 

  36. Vesanto J (1999) SOM-based data visualization methods. Intell Data Anal 3(2):111–126

    Article  MATH  Google Scholar 

  37. Sarlin P (1999) Data and dimension reduction for visual financial performance analysis. TUCS Technical Reports. TUCS

  38. Lee SC, Suh YH, Kim JK, Lee KJ (2004) A cross-national market segmentation of online game industry using SOM. Expert Syst Appl 27(4):559–570

    Article  Google Scholar 

  39. Vellido A, Lisboa P, Meehan K (1999) Segmentation of the on-line shopping market using neural networks. Expert Syst Appl 17(4):303–314

    Article  Google Scholar 

  40. Vesanto J, Alhoniemi E (2000) Clustering of the self-organizing map. IEEE Trans Neural Networks 11(3):586–600

    Article  Google Scholar 

  41. Li H (2005) Data visualization of asymmetric data using Sammon mapping and applications of self-organizing maps. University of Maryland, (College Park, Md.)

  42. Ward JH Jr (1963) Hierarchical grouping to optimize an objective function. J Am Stat Assoc 58(301):236–244

    Article  Google Scholar 

  43. Theodoridis S, Koutroumbas K (2008) Pattern recognition. Elsevier Science, Amsterdam

    Google Scholar 

  44. Rand WM (1971) Objective criteria for the evaluation of clustering methods. J Am Stat Assoc 66(336):846–850

    Article  Google Scholar 

  45. Hubert L, Schultz J (1976) Quadratic assignment as a general data-analysis strategy. Br J Math Stat Psychol 29(2):190–241

    Article  MATH  MathSciNet  Google Scholar 

  46. Tibshirani R, Walther G, Hastie T (2001) Estimating the number of clusters in a data set via the gap statistic. J R Stat Soc Ser B (Stat Methodol) 63(2):411–423

    Article  MATH  MathSciNet  Google Scholar 

  47. Dunn JC (1973) A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. J Cybern 3(3):32–57

    Article  MATH  MathSciNet  Google Scholar 

  48. Rousseeuw PJ, Kaufman L (1990) Finding groups in data: an introduction to cluster analysis. Wiley-Interscience, Hoboken

    Google Scholar 

  49. Kohonen T (1988) The ‘neural’ phonetic typewriter. Computer 21(3):11–22. doi:10.1109/2.28

    Article  Google Scholar 

  50. Eklund T, Back B, Vanharanta H, Visa A (2003) Using the self-organizing map as a visualization tool in financial benchmarking. Inf Vis 2(3):171–181

    Article  Google Scholar 

  51. Sarlin P, Marghescu D (2011) Visual predictions of currency crises using self-organizing maps. Intell Syst Account Financ Manag 18(1):15–38

    Article  Google Scholar 

  52. Afolabi MO, Olude O (2007) Predicting stock prices using a hybrid Kohonen self organizing map (SOM). In: Proceedings of the 40th annual Hawaii international conference on system sciences, IEEE, Big Island, Hawaii, USA

  53. Hsu S-H, Hsieh JP-A, Chih T-C, Hsu K-C (2009) A two-stage architecture for stock price forecasting by integrating self-organizing map and support vector regression. Expert Syst Appl 36(4):7947–7951

    Article  Google Scholar 

  54. Sulkava M, Hollmén J (2003) Finding profiles of forest nutrition by clustering of the self-organizing map. In: Proceedings of the workshop on self-organizing maps, Springer, Kitakyushu, Japan

  55. Luyssaert S, Sulkava M, Raitio H, Hollmén J (2004) Evaluation of forest nutrition based on large-scale foliar surveys: are nutrition profiles the way of the future? J Environ Monit 6(2):160–167

    Article  Google Scholar 

  56. SOMine (2009), Viscovery SOMine user manual, Austria, Vienna

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

    Article  Google Scholar 

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Acknowledgments

The financial support of the Academy of Finland (Grant Nos. 127656 and 127592) and the Foundation of Economic Education is gratefully acknowledged.

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Authors and Affiliations

  1. Department of Information Technologies, Åbo Akademi University, Joukahaisenkatu 3–5, 20520, Turku, Finland

    Zhiyuan Yao, Peter Sarlin, Tomas Eklund & Barbro Back

Authors
  1. Zhiyuan Yao
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  2. Peter Sarlin
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  3. Tomas Eklund
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  4. Barbro Back
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Corresponding author

Correspondence to Zhiyuan Yao.

Appendix

Appendix

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Table 3 The summary statistics of the demographic variables
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Yao, Z., Sarlin, P., Eklund, T. et al. Combining visual customer segmentation and response modeling. Neural Comput & Applic 25, 123–134 (2014). https://doi.org/10.1007/s00521-013-1454-3

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