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Factor based prediction model for customer behavior analysis

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Abstract

Information Technology is nearing ubiquity stage in modern workplaces. The domain and applications of information technology is expanded abundantly. Any organization that wishes to improve their prospect in the market would definitely keep track their buyers’ perspective and emerging trends. In order to understand their aspirants, the companies are applying enormous technical ideas, tools and methodologies. Analysing more data and facts lead to better decision making. This is a strong perception of business intelligence experts. This work deals with a gradual transformation from instinct-driven approach to progressively data-driven approach. Understanding the expectations of the customers and improving their sales in particular to online trading. Therefore any business firm today have to access to unlimited amount of data. This include sales demographics, economic trends, competitive data and consumer behaviour, efficiency measures and financial calculations and more. Business Intelligence has a leading contribution in this venture. The empirical data are systematically gathered in order to analyse or test hypotheses and consequently make new observations and experiments that leads to gain new insights. The factor based principle component analysis method is used to select the important customer buying factors to analyze their behavior.

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References

  • Agrawal R, Srikant R (1993) Fast algorithms for mining association rules inlarge databases. In: Bocca JB, Jarke M, Zaniolo C (eds) Proceedings of the 20th international conference on very large data bases (VLDB ‘94), Morgan Kaufmann Publishers Inc., San Francisco, CA, pp 487–499

  • Ahmed SR (2004) Applications of data mining in retail business. In: Proceedings of International conference on information technology: coding and computing, ITCC 2004, vol 2, pp 455–459

  • Böttcher M, Spott M, Nauck D, Kruse R (2009) Mining changing customer segments in dynamic markets. Expert Syst Appl 36(1):155–164

    Article  Google Scholar 

  • Buckinx W, Verstraeten G, Van den Poel D (2007) Predicting customer loyalty using the internal transactional database. Expert Syst Appl 32(1):125–134

    Article  Google Scholar 

  • Chang HJ, Hung LP, Ho CL (2007) An anticipation model of potential customers’ purchasing behavior based on clustering analysis and association rules analysis. Expert Syst Appl 32(3):753–764

    Article  Google Scholar 

  • Doran P (2007) Information architecture for Irish grocery retailers using business intelligence tools. Dissertations

  • Einav L, Levin JD (2013) The data revolution and economic analysis (No. w19035).National Bureau of Economic Research

  • Fang CH, Lee HJ (2009) Food-related lifestyle segments in Taiwan: application of the food-related lifestyle instrument. Am J Appl Sci 6(12):2036–2042

    Article  Google Scholar 

  • Ha S (2007) Applying knowledge engineering techniques to customer analysis in the service industry. Adv Eng Inf 21(3):293–301

    Article  MathSciNet  Google Scholar 

  • Hsieh NC (2004) An integrated data mining and behavioral scoring model for analyzing bank customers. Expert Syst Appl 27(4):623–633

    Article  Google Scholar 

  • Liao S-H, Chen CM, Wu CH (2008) Mining customer knowledge for product line and brand extension in retailing. Expert Syst Appl 34(3):1763–1776

    Article  Google Scholar 

  • Lohse GL, Spiller P (1998) Electronic shopping. Commun ACM 41:81–87

    Article  Google Scholar 

  • Negi R (2009) Determining customer satisfaction through perceived service quality: a study of Ethiopian mobile users. Int J Mob Market 4(1):31–38

    MathSciNet  Google Scholar 

  • Romdhane LB, Fadhel N, Ayeb B (2010) An efficient approach for building customer profiles from business data. Expert Syst Appl 37(2):1573–1585

    Article  Google Scholar 

  • Russell GJ, Petersen A (2000) Analysis of cross category dependence in market basket selection. J Retail 76(3):367–392

    Article  Google Scholar 

  • Sheu JJ, Chang YW, Chu KT (2008) Applying decision tree data mining for online group buying consumers’ behaviour. Int J Electron Cust Relationsh Manag 2(2):140–157

    Article  Google Scholar 

  • Venkatesan M, Thangavelu A (2015) A Delaunay diagram-based Min–Max CP-Tree algorithm for spatial data analysis. WIREs Data Min Knowl Discov 5:142–154

    Article  Google Scholar 

  • Venkatesan M, Thangavelu A, Prabhavathy P (2013) An improved bayesian classification data mining method for early warning landslide susceptibility model using GIS. In: Proceedings of seventh international conference on bio-inspired computing: theories and applications (BIC-TA 2012), advances in intelligent systems and computing, Springer

  • Venkatesan M, Thangavelu A, Prabhavathy P (2015) A novel cp-tree based co-located classifier for big data analysis, international. J Commun Netw Distrib Syst 15:191–211

    Google Scholar 

  • Wang HF, Hong WK (2006) Managing customer profitability in a competitive market by continuous data mining. Ind Mark Manag 35(6):715–723

    Article  Google Scholar 

  • Yu CC, Wang CS (2008) A hybrid mining approach for optimizing returns policies in e-retailing. Expert Syst Appl 35(4):1575–1582

    Article  MathSciNet  Google Scholar 

Download references

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Authors and Affiliations

  1. Department of Computer Technology, Dr.SNS Rajalakshmi College of Arts and Science, Coimbatore, India

    D. Kalaivani

  2. Research and PG Department of Computer Science, Government Arts College, Coimbatore, India

    P. Sumathi

Authors
  1. D. Kalaivani
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  2. P. Sumathi
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Correspondence to D. Kalaivani.

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Kalaivani, D., Sumathi, P. Factor based prediction model for customer behavior analysis. Int J Syst Assur Eng Manag 10, 519–524 (2019). https://doi.org/10.1007/s13198-018-0739-4

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  • DOI: https://doi.org/10.1007/s13198-018-0739-4

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