Skip to main content
SpringerLink
Log in

Mining the change of customer behavior in dynamic markets

  • Published:
Information Technology and Management Aims and scope Submit manuscript

Abstract

Identification of changes in customer behavior is a major challenge that must be tackled in order to survive in a rapidly changing business environment. For example, because information technology has advanced and data-storage costs have declined, for the purpose of serving customers, numerous enterprises have employed information systems and have directly logged customer behavior in databases. This trend has motivated the development of data mining applications. Fuzzy quantitative sequential pattern mining is a functional data mining technique that is used for discovering customer behavioral patterns over time and determining the quantities of goods or services they purchase. The example term used in shopping 〈[(Beer, Low)(Milk, High)] (Cola, Middle)〉 means that customers will first buy Beer and Milk in Low and High quantities, respectively, and then purchase Cola in Middle quantities on their next shopping trip, where Low, Middle, and High are predefined linguistic terms assigned by managers. A term such as this one provides managers with general and concise knowledge related to customer behavior and allows them to rapidly make decisions in response, especially in a competitive setting. However, literature searches indicate that no previous study has addressed the issue of changes in fuzzy quantitative sequential patterns. The aforementioned example pattern might have been available last year but might not be used this year, and it could have been substituted by 〈(Beer, Middle) {(Cola, Low)(Milk, Low)}〉. If this knowledge is not renewed, managers might develop inappropriate marketing plans for their products or services and use inventory strategies that are outdated with respect to time and quantities. To solve this problem, we propose a novel change-mining model that can be used for detecting changes in fuzzy quantitative sequential patterns. We conducted experiments in which we used real-world and synthetic datasets in order to evaluate the proposed model. When the pattern change was detected using the real-world dataset, the results showed that the model reveals 3 considerations that can help managers with their handling of products’ marketing and production. When we studied the model’s scalability by using the synthetic dataset, the results showed that even though all run times increased when parameter values were decreased, the model remained scalable.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Agrawal R, Srikant R (1994) Fast algorithms for mining association rules. In: Proceedings of 1994 international conference very large data bases, 1994, pp 487–499

  2. Agrawal R, Srikant R (1995) Mining sequential patterns. In: Proceedings of 1995 international conference data engineering, pp 3–14

  3. Cheung DW, Han J, Ng VT, Wong CY (1996) Maintenance of discovered association rules in large databases: an incremental updating technique. In: Proceedings of the 12th international conference on data engineering, pp 106–114

  4. Cheung KW, Kwok JT, Law MH, Tsui KC (2003) Mining customer product ratings for personalized marketing. Decis Support Syst 35(2):231–243

    Article  Google Scholar 

  5. Changchien SW, Lee CF, Hsu YJ (2004) On-line personalized sales promotion in electronic commerce. Expert Syst Appl 27(1):35–52

    Article  Google Scholar 

  6. Chen MC, Chiu AL, Chang HH (2005) Mining changes in customer behavior in retail marketing. Expert Syst Appl 28(4):773–781

    Article  Google Scholar 

  7. Chen YL, Huang CK (2005) Discovering fuzzy time-interval sequential patterns in sequence databases. IEEE Trans Syst Man Cybern Part B 35(5):959–972

    Article  Google Scholar 

  8. Chen YL, Huang CK (2006) A new approach for discovering fuzzy quantitative sequential patterns in sequence databases. Fuzzy Sets Syst 157(12):1641–1661

    Article  Google Scholar 

  9. Chen YL, Huang CK (2008) A novel knowledge discovering model for mining fuzzy multi-level sequential patterns in sequence databases. Data Knowl Eng 66(3):349–367

    Article  Google Scholar 

  10. Chen YL, Kuo MH, Wu SY, Tang K (2009) Discovering recency, frequency, and monetary (RFM) sequential patterns from customers’ purchasing data. Electron Commer Res Appl 8(5):241–251

    Article  Google Scholar 

  11. Cho YB, Cho YH, Kim SH (2005) Mining changes in customer buying behavior for collaborative recommendations. Expert Syst Appl 28(2):359–369

    Article  Google Scholar 

  12. Dong G, Li J (1998) Interestingness of discovered association rules in terms of neighborhood-based unexpectedness. In: Proceedings of 2nd Pacific-Asia conference on research and development in knowledge discovery and data mining, pp72–86

  13. Dong G, Li J (1999) Efficient mining of emerging patterns: discovering trends and differences. In: Proceedings of the fifth international conference on knowledge discovery and data mining, pp 43–52

  14. Dong L, Laurent A, Poncelet P (2011) WebUser: mining unexpected web usage. Int J Bus Intell Data Min 6(1):90–111

    Article  Google Scholar 

  15. El-Sayed M, Ruiz C, Rundensteiner EA (2004) FS-Miner: efficient and incremental mining of frequent sequence patterns in web logs. In: Proceedings of the 6th annual ACM international workshop on web information and data management, pp 128–135

  16. Fiot C, Laurent A, Teisseire M (2008) Fuzzy sequential pattern mining in incomplete databases. Mathw Soft Comput 15(1):41–59

    Google Scholar 

  17. Fiot C, Laurent A, Teisseire M (2007) From crispness to fuzziness: three algorithms for soft sequential pattern mining. IEEE Trans Fuzzy Syst 15(6):1263–1277

    Article  Google Scholar 

  18. Gusfield D (1997) Algorithms on strings, trees, and sequences: computer science and computational biology. Cambridge University Press, Cambridge

    Book  Google Scholar 

  19. Hong TP, Kuo CS, Chi SC (1999) Mining fuzzy sequential patterns from quantitative data. In: The 1999 IEEE international conference on systems, man, and cybernetics, pp 962–966

  20. Han J, Pei J, Mortazavi-Asl B, Chen Q, Dayal U, Hsu MC (2000) FreeSpan: frequent pattern-projected sequential pattern mining. In: Proceedings of 2000 international conference on knowledge discovery and data mining, pp 355–359

  21. Hui SC, Jha G (2000) Data mining for customer service support. Inf Manag 38(1):1–13

    Article  Google Scholar 

  22. Hong TP, Lin CW, Wu YL (2008) Incrementally fast updated frequent pattern trees. Expert Syst Appl 34(4):2424–2435

    Article  Google Scholar 

  23. Huang CL, Huang WL (2009) Handling sequential pattern decay: developing a two-stage collaborative recommender system. Electron Commer Res Appl 8(3):117–129

    Article  Google Scholar 

  24. Klir G, Yuan B (1995) Fuzzy sets and fuzzy logic: theory and applications. Prentice-Hall International Inc., London

    Google Scholar 

  25. Kim JK, Cho YH, Kim WJ, Kim JR, Suh JH (2002) A personalized recommendation procedure for internet shopping support. Electron Commer Res Appl 1(3):301–313

    Article  Google Scholar 

  26. Kim C, Lim JH, Ng R, Shim K (2004) SQUIRE: Sequential pattern mining with quantities. In: Proceedings of the 20th international conference on data engineering, pp 827–827

  27. Lanquillon C (1999) Information filtering in changing domains. In: Proceedings of the international joint conference on artificial intelligence, pp 41–48

  28. Liu B, Hsu W, Han HS, Xia Y (2000) Mining changes for real-life applications. In: Second international conference on data warehousing and knowledge discovery, pp. 337–346

  29. Lin QY, Chen YL, Chen JS, Chen YC (2003) Mining inter-organizational retailing knowledge for an alliance formed by competitive firms. Inf Manag 40(5):431–442

    Article  Google Scholar 

  30. Lin MY, Lee SY (2004) Incremental update on sequential patterns in large databases by implicit merging and efficient counting. Inf Syst 29(5):385–404

    Article  Google Scholar 

  31. Lee CH, Lin CR, Chen MS (2005) Sliding window filtering: an efficient method for incremental mining on a time-variant database. Inf Syst 30(3):227–244

    Article  Google Scholar 

  32. Liu B, Cao SG, He W (2011) Distributed data mining for e-business. Inf Technol Manag 12(2):67–79

    Article  Google Scholar 

  33. Masseglia F, Poncelet P, Teisseire M (2003) Incremental mining of sequential patterns in large databases. Data Knowl Eng 46(1):97–121

    Article  Google Scholar 

  34. Mahdavi I, Cho N, Shirazi B, Sahebjamnia N (2008) Designing evolving user profile in e-CRM with dynamic clustering of Web documents. Data Knowl Eng 65(2):355–372

    Article  Google Scholar 

  35. Matthews SG, Gongora MA, Hopgood AA, Ahmadi S (2012) Temporal fuzzy association rule mining with 2-tuple linguistic representation. In: Proceedings of the 2012 IEEE international conference on fuzzy systems (FUZZ-IEEE 2012), pp 1–8

  36. Ng V, Chan S, Lau D, Ying CM (2007) Incremental mining for temporal association rules for crime pattern discoveries. In: Proceedings of the eighteenth conference on Australasian database, pp. 123–132

  37. Padmanabhan B, Tuzhilin A (1999) Unexpectedness as a measure of interestingness in knowledge discovery. Decis Support Syst 27(3):303–318

    Article  Google Scholar 

  38. Pei J, Han J, Mortazavi-Asl B, Zhu H (2000) Mining access patterns efficiently from web logs. In: Proceedings of 2000 Pacific-Asia conference on knowledge discovery and data mining, pp 396–407

  39. Pei J, Han J, Mortazavi-Asl B, Wang J, Pinto H, Chen Q, Dayal U, Hsu M-C (2004) Mining sequential patterns by pattern-growth: the PrefixSpan approach. IEEE Trans Knowl Data Eng 16(11):1424–1440

    Article  Google Scholar 

  40. Rabatel J, Bringay S, Poncelet P (2010) Contextual sequential pattern mining. In: 2010 IEEE international conference on data mining workshops, pp 981–988

  41. Srikant R, Agrawal R (1996) Mining sequential patterns: generalizations and performance improvements. In: Proceedings of the fifth international conference on extending database technology, pp 3–17

  42. Shaw MJ, Subramaniam C, Tan GW, Welge ME (2001) Knowledge management and data mining for marketing. Decis Support Syst 31(1):127–137

    Article  Google Scholar 

  43. Song HS, Kim JK, Kim SH (2001) Mining the change of customer behavior in an Internet shopping mall. Expert Syst Appl 21(3):157–168

    Article  Google Scholar 

  44. Subramanyam RBV, Goswami A (2005) A fuzzy data mining algorithm for incremental mining of quantitative sequential patterns. Int J Uncertain Fuzziness Knowl Based Syst 13(6):633–652

    Article  Google Scholar 

  45. Tsai CY, Shieh YC (2009) A change detection method for sequential patterns. Decis Support Syst 46(2):501–511

    Article  Google Scholar 

  46. Watson HJ, Frolick MN (1993) Determining information requirement for an EIS. MIS Q 17(3):255–269

    Article  Google Scholar 

  47. Wang K, Yang Q, Yeung JMS (2005) Mining customer value: from association rules to direct marketing. Data Min Knowl Disc 11(1):57–79

    Article  Google Scholar 

  48. Yang TC, Lai H (2006) Comparison of product bundling strategies on different online shopping behaviors. Electron Commer Res Appl 5(4):295–304

    Article  Google Scholar 

  49. Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to thank the Editors-in-Chief, Dr. Raymond A. Patterson and Dr. Erik Rolland, and the anonymous referees for their helps and valuable comments to improve this paper. The first author also appreciates Krannert School of Management, Purdue University, providing the research resources to support the revision of this paper during his visiting period. This research was supported by the National Science Council of the Republic of China under the grant NSC 99-2410-H-194-063-MY2.

Author information

Authors and Affiliations

  1. Department of Business Administration, National Chung Cheng University, 168, University Rd., Min-Hsiung, Chia-Yi, Taiwan, Republic of China

    Cheng-Kui Huang & Ting-Yi Chang

  2. Department of Agricultural Economics, Center for Global Trade Analysis, Purdue University, West Lafayette, IN, USA

    Badri G. Narayanan

Authors
  1. Cheng-Kui Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ting-Yi Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Badri G. Narayanan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Cheng-Kui Huang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Huang, CK., Chang, TY. & Narayanan, B.G. Mining the change of customer behavior in dynamic markets. Inf Technol Manag 16, 117–138 (2015). https://doi.org/10.1007/s10799-014-0197-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10799-014-0197-x

Keywords

Search

Navigation