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Data Mining in Market Segmentation: A Literature Review and Suggestions

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Proceedings of Fourth International Conference on Soft Computing for Problem Solving

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 335))

Abstract

The importance of data mining techniques for market segmentation is becoming indispensable in the field of marketing research. This is the first identified academic literature review of the available data mining techniques related to market segmentation. This research paper provides surveys of the available literature on data mining techniques in market segmentation. A categorization has been provided based on the available data mining techniques used in market segmentation. Eight online journal databases were used for searching, and finally, 103 articles were selected and categorized into 13 groups based on data mining techniques. The utility of data mining techniques and suggestions are also discussed. The findings of this study show that neural networks is the most used method, and kernel-based method is the most promising data mining techniques. Our research work provides a comprehensive understanding of past, present as well as future research trend on data mining techniques in market segmentation. We hope this paper provides reasonable insight and clear understating to both industry as well as academic researchers.

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References

  1. Abdi, K., Fathian, M., Safari, E.: A novel algorithm based on hybridization of artificial immune system and simulated annealing for clustering problem. Int. J. Adv. Manuf. Technol. 60(5–8), 723–732 (2012)

    Article  Google Scholar 

  2. Wildt, Albert R., McCann, John M.: Multicriterion market segmentation: a new model, implementation, and evaluation. J. Mark. Res. 17(3), 335–340 (1980)

    Article  Google Scholar 

  3. Babu, G.P., Murty, M.N.: A near-optimal initial seed value selection in K-means algorithm using a genetic algorithm. Pattern Recogn. Lett. 14(10), 763–769 (1993)

    Article  MATH  Google Scholar 

  4. Balakrishnan, P.V., Cooper, M.C., Jacob, V.S., Lewis, P.A.: Comparative performance of the FSCL neural net and K-means algorithm for market segmentation. Eur. J. Oper. Res. 93, 346–357 (1996)

    Article  MATH  Google Scholar 

  5. Bezdek, J.C., Pal, N.R.: Some new indexes of cluster validity. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 28(3), 301–315 (1998)

    Article  Google Scholar 

  6. Blatt, M., Wiseman, S., Domany, E.: Clustering data through an analogy to the potts model. Adv. Neural Inf. Process. Syst. 8, 416–422 (1996)

    Google Scholar 

  7. Bloom, J.Z.: Tourist market segmentation with linear and non-linear techniques. Tour. Manage. 25, 723–733 (2004)

    Article  Google Scholar 

  8. Boone, D.S., Roehm, M.: Retail segmentation using artificial neural networks. Int. J. Res. Mark. 19(3), 287–301 (2002)

    Article  Google Scholar 

  9. Boone, D.S., Roehm, M.: Evaluating the appropriateness of market segmentation solutions using artificial neural networks and the membership clustering criterion. Market. Lett. 13(4), 317–333 (2002)

    Article  Google Scholar 

  10. Brida, J.G., Disegna, M., Osti, L.: Segmenting visitors of cultural events by motivation: A sequential non-linear clustering analysis of Italian Christmas market visitors. Expert Syst. Appl. 39, 11349–11356 (2012)

    Article  Google Scholar 

  11. Brida, J.G., Disegna, M., Scuderi, R.: Visitors of two types of museums: a segmentation study. Expert Syst. Appl. 40(6), 2224–2232 (2012)

    Article  Google Scholar 

  12. Bursco, M.J., Cradit, J.D., Stahl, S.: A simulated annealing heuristic for a bicriterion partitioning problem in marketing segmentation. J. Mark. Res. 39(1), 99–109 (2002)

    Article  Google Scholar 

  13. Bursco, M.J., Cradit, J.D., Stahl, S.: Multicriterion cluster wise regression for joint segmentation settings: an application to customer value. J. Mark. Res. 40(2), 225–234 (2002)

    Article  Google Scholar 

  14. Chen, C.H., Khoo, C.L., Yan, W.: A strategy for acquiring customer requirement patterns using laddering technique and ART2 neural network. Adv. Eng. Inform. 16, 229–240 (2002)

    Article  Google Scholar 

  15. Chen, M.C., Wu, H.P.: An association-based clustering approach to order batching considering customer demand patterns. Omega 33(4), 333–343 (2005)

    Article  Google Scholar 

  16. Cheng, C.H., Chen, Y.S.: Classifying the segmentation of customer value via RFM model and RS theory. Expert Syst. Appl. 36, 4176–4184 (2009)

    Article  Google Scholar 

  17. Chung, K.Y., Oh, S.Y., Kim, S.S., Han, S.Y.: Three representative market segmentation methodologies for hotel guest room customers. Tour. Manag. 25(4), 429–441 (2004)

    Article  Google Scholar 

  18. Croft, M.J.: Market Segmentation: A Step-by-Step Guide to Profitable New Business. Routledge, London, New York (1994)

    Google Scholar 

  19. Desarbo, W.S., Ramaswamy, V.: CRISP: customer response based iterative segmentation procedures for response modeling in direct marketing. J. Direct Market. 8, 7–20 (1994)

    Article  Google Scholar 

  20. Desarbo, W.S., Ramaswamy, V., Cohen, S.H.: market segmentation with choice-based conjoint analysis. Market. Lett. 6(2), 137–147 (1995)

    Article  Google Scholar 

  21. DeSarbo, W.S., Grisaffe, D.: Combinatorial optimization approaches to constrained market segmentation: an application to industrial market segmentation. Market. Lett. 9, 115–134 (1998)

    Article  Google Scholar 

  22. Desarbo, W.S., Grewal, R., Scott, C.J.: A cluster wise bilinear multidimensional scaling methodology for simultaneous segmentation and positioning analyses. J. Mark. Res. 45, 280–292 (2008)

    Article  Google Scholar 

  23. Frank, R.E., Massy, W.F., Wind, Y.: Market segmentation. Prentice Hall, Englewood Cliffs (1972)

    Google Scholar 

  24. Huang, J.H., Tzeng, G.H., Ong, C.S.: Marketing segmentation using support vector clustering. Expert Syst. Appl. 32, 313–317 (2007)

    Article  Google Scholar 

  25. Hammond, K., Ehrenberg, A.S.C., Goodhardt, G.J.: Market segmentation for competitive brands. Eur. J. Mark. 30(12), 39–49 (1996)

    Article  Google Scholar 

  26. Hruschka, H., Natter, M.: Comparing performance of feedforward neural nets and K-means for cluster-based market segmentation. Eur. J. Oper. Res. 114, 346–353 (1999)

    Article  MATH  Google Scholar 

  27. Hong, C.W.: Using the Taguchi method for effective market segmentation. Expert Syst. Appl. 39, 5451–5459 (2012)

    Article  Google Scholar 

  28. Kamakura, W.A., Russell, G.J.: A probabilistic choice model for market segmentation and elasticity structure. J. Market. Res. 26, 379–390 (1989)

    Google Scholar 

  29. Kauko, T., Hooimeijer, P., Hakfoort, J.: Capturing housing market segmentation: an alternative approach based on neural network modeling. Hous. Stud. 17(6), 875–894 (2002)

    Article  Google Scholar 

  30. Kim, Y., Street, W.N.: An intelligent system for customer targeting: a data mining approach. Decis. Support Syst. 37(2), 215–228 (2004)

    Article  Google Scholar 

  31. Kotler, P., Gordon, M.: Principles of Market. Prentice Hall, Canada (1983)

    Google Scholar 

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

    Article  MATH  Google Scholar 

  33. Kuo, R.J., Chang, K., Chien, S.Y.: Integration of self-organizing feature maps and genetic algorithm- based clustering method for market segmentation. J. Organ. Comput. Electron. Commer. 14(1), 43–60 (2004)

    Google Scholar 

  34. Li, Z., Wang, W., Yang, C., Ragland, D.R.: Bicycle commuting market analysis using attitudinal market segmentation approach. Transport. Res. Part A: Policy Pract. 47, 56–68 (2013)

    Google Scholar 

  35. Maroosi, A., Amiri, B.: A new clustering algorithm based on hybrid global optimization based on a dynamical systems approach algorithm. Expert Syst. Appl. 37(8), 5645–5652 (2010)

    Article  Google Scholar 

  36. Maulik, U., Bandyopadhyay, S.: Genetic algorithm-based clustering technique. Pattern Recogn. 33(9), 1455–1465 (2000)

    Article  Google Scholar 

  37. Maulik, U., Bandyopadhyay, S.: Performance evaluation of some clustering algorithms and validity indices. IEEE Trans. Patt. Anal. Mach. Intell. 24(12), 1650–1654 (2002)

    Article  Google Scholar 

  38. Miguéis, V.L., Camanho, A.S., Falcão e Cunha, J.: Customer data mining for lifestyle segmentation. Expert Syst. Appl. 39, 9359–9366 (2012)

    Article  Google Scholar 

  39. Mostafa, M.M.: Clustering the ecological footprint of nations using Kohonen’s self-organizing maps. Expert Syst. Appl. 37, 2747–2755 (2010)

    Article  Google Scholar 

  40. Murthy, C.A., Chowdhury, N.: In search of optimal clusters using genetic algorithms. Pattern Recogn. Lett. 17(8), 825–832 (1996)

    Article  Google Scholar 

  41. Natter, M.: Conditional market segmentation by neural networks: a Monte-Carlo study. J. Retail. Consum. Serv. 6(4), 237–248 (1999)

    Article  Google Scholar 

  42. Ngai, E.W., Xiu, L., Chau, D.C.K.: Application of data mining techniques in customer relationship management: A literature review and classification. Expert Syst. Appl. 36(2), 2592–2602 (2009)

    Article  Google Scholar 

  43. Park, D.H., Kim, H.K., Choi, I.Y., Kim, J.K.: A literature review and classification of recommender systems research. Expert Syst. Appl. 39(11), 10059–10072 (2012)

    Article  Google Scholar 

  44. Punj, G., Stewart, D.W.: Cluster analysis in marketing research: review and suggestions for application. J. Market. Res. 20, 134–148 (1983)

    Google Scholar 

  45. Smith, W.R.: Product differentiation and market segmentation as an alternative marketing strategy. J. Market. 21, 3–8 (1956)

    Article  Google Scholar 

  46. Vellido, A., Lisboa, P.J.G., Vaughan, J.: Neural networks in business: a survey of applications (1992–1998). Expert Syst. Appl. 17(1), 51–70 (1999)

    Article  Google Scholar 

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

    Article  Google Scholar 

  48. Vishwanath, A., Chen, H.: Technology clusters: using multidimensional scaling to evaluate and structure technology clusters. J. Am. Soc. Inform. Sci. Technol. 57(11), 1451–1460 (2006)

    Article  Google Scholar 

  49. Vriens, M., Wedel, M., Wilms, T.: Metric conjoint segmentation methods: a Monte Carlo comparison. J. Mark. Res. 33, 73–85 (1996)

    Article  Google Scholar 

  50. Wang, C.H.: Apply robust segmentation to the service industry using kernel induced fuzzy clustering techniques. Expert Syst. Appl. 37, 8395–8400 (2010)

    Article  Google Scholar 

  51. Wedel, M., Kamakura, W.A.: Market segmentation: conceptual and methodological foundations. Kluwer Academic, Boston (2000)

    Book  Google Scholar 

  52. Wedel, M., Kistemaker, C.: Consumer benefit segmentation using cluster wise linear regression. Int. J. Res. Mark. 6, 241–258 (1989)

    Article  Google Scholar 

  53. Wei, J.T., Lin, S.Y., Weng, C.C., Wu, H.H.: A case study of applying LRFM model in market segmentation of a children’s dental clinic. Expert Syst. Appl. 39(5), 5529–5533 (2012)

    Article  Google Scholar 

  54. Wu, W.W.: Segmenting and mining the ERP users’ perceived benefits using the rough set approach. Expert Syst. Appl. 38, 6940–6948 (2011)

    Article  Google Scholar 

  55. Zhang, Y., Jiao, J., Ma, Y.: Market segmentation for product family positioning based on fuzzy clustering. J. Eng. Des. 18(3), 227–241 (2007)

    Article  Google Scholar 

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Authors and Affiliations

  1. Indian Institute of Technology Kharagpur, Kharagpur, India

    Saibal Dutta, Sujoy Bhattacharya & Kalyan Kumar Guin

Authors
  1. Saibal Dutta
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  2. Sujoy Bhattacharya
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  3. Kalyan Kumar Guin
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Correspondence to Saibal Dutta .

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Editors and Affiliations

  1. Mathematics, National Institute of Technology Silchar, Silchar, Assam, India

    Kedar Nath Das

  2. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, India

    Kusum Deep

  3. Department of Paper Technology, Indian Institute of Technology Roorkee, Roorkee, India

    Millie Pant

  4. Department of Mathematics, South Asian University, New Delhi, India

    Jagdish Chand Bansal

  5. Department of Computer Science, Liverpool Hope University, Liverpool, United Kingdom

    Atulya Nagar

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Dutta, S., Bhattacharya, S., Guin, K.K. (2015). Data Mining in Market Segmentation: A Literature Review and Suggestions. In: Das, K., Deep, K., Pant, M., Bansal, J., Nagar, A. (eds) Proceedings of Fourth International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 335. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2217-0_8

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  • DOI: https://doi.org/10.1007/978-81-322-2217-0_8

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