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Mouse Movement Pattern Based Analysis of Customer Behavior (CBA-MMP) Using Cloud Data Analytics

Abstract

In the vast field of E-Commerce and E-Business, the appropriate way of customer analysis is very much important to make a business more successful. In such modes of online business, the behavior of customers is analyzed through various processes of data analytics to effectively satisfy them will improved services. The behaviors of the customers can be well analyzed through their mouse movement patterns in a very exact manner. With that concern, this paper contributes to developing a model customer behavior analysis based on the mouse movement pattern. This helps in deriving or mining information and aids to predict customer activities in the E-Market. Typically, for the behavioral analysis, one of the effective data mining algorithms called decision tree algorithm and a classification technique called Multi-layer Neural Network techniques have been incorporated. Through these techniques, customer behaviors are exactly analyzed and determined. For experimentation and evaluation, some benchmark datasets are used and the results show that the proposed model produces better analysis than existing works.

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References

  1. 1.

    Quinlan, J. R. (1993). C4.5: Programs for machine learning. Burlington: Morgan Kaufmann.

  2. 2.

    Quinlan, J. R. (1996). Improved use of continuous attributes in C4.5. arXiv preprint cs/9603103.

  3. 3.

    Wei, D., & Wei, J. (2014). A MapReduce implementation of C4.5 decision tree algorithm. International Journal of Database Theory and Application,7(1), 49–60.

  4. 4.

    Thompson, S., & Teo, H. (2006). To buy or not to buy online: Adopters and non-adopters of online shopping in Singapore. Behaviour & Information Technology,25(6), 497–509.

  5. 5.

    Aikaterini, C. (2013). Online and mobile customer behaviour: A critical evaluation of grounded theory studies. Behaviour & Information Technology,32(7), 655–667.

  6. 6.

    Watson, H. J., Goodhue, D. L., & Wixom, B. H. (2002). The benefits of data warehousing: Why some organizations realize exceptional payoffs. Information Management,39, 491–502.

  7. 7.

    Al-Zaidy, R., Fung, B. C. M., Youssef, A. M., & Fortin, F. (2012). Mining criminal networks from unstructured text documents. Digital Investigation,8, 147–160.

  8. 8.

    Sam, S., Liping, Z., & Lawrence, C. (2016). GOMA: Supporting big data analytics with a goal-oriented approach. In 2016 IEEE international congress on big data (pp. 149–156).

  9. 9.

    Liyan, J., Qing, Z., & Lang, T. (2013). Retail pricing for stochastic demand with unknown parameters: An online machine learning approach. In Fifty-first annual Allerton conference (pp. 1353–1358). Allerton House.

  10. 10.

    Premchaiswadi, W., & Romsaiyud, W. (2012). Extracting weblog of Siam university for learning user behavior on MapReduce. In Proceedings of 4th international conference on intelligent and advanced systems (ICIAS) and a conference of world engineering, science and technology congress.

  11. 11.

    Senecal, S., Kalczynski, P. J., & Nantel, J. (2015). Consumers decision making process and their online shopping behaviour: A clickstream analysis. Journal of Business Research,58, 1599–1608.

  12. 12.

    Hu, J., & Zhong, N. (2008). Web farming with clickstream. International Journal of Information Technology & Decision Making,7(2), 291–308.

  13. 13.

    Ting, I. H., Kimble, C., & Kudenko, D. (2009). Finding unexpected navigation behaviour in clickstream data for website design improvement. Journal of Web Engineering,8(1), 71–92.

  14. 14.

    Detlor, B. (2009). The corporate portal as information structure: Towards a framework for portal design. International Journal of Information Technology,20, 91–101.

  15. 15.

    Domingues, M. A., Soares, C., & Jorge, A. M. (2013). Using statistics, visualization and data mining for monitoring the quality of meta-data in web portals. Information Systems and E-Business Management,11(4), 569–595.

  16. 16.

    Vicari, D., & Alfo, M. (2014). Model based clustering of customer choice data. Computational Statistics & Data Analysis,71(Special Issue), 3–13.

  17. 17.

    Hu, X. H., & Cercone, N. A. (2004). Data warehouse/online analytic processing framework for web usage mining and business intelligence reporting. International Journal Of Intelligent Systems,19(7), 585–606.

  18. 18.

    Wang, P., Zhang, X. Y., Xu, L. B., Xia, G. P., & Osaki, H. (2002). Applications of data mining to electronic commerce: A survey. In Proceedings of the sixth China–Japan international conference on industrial management (pp. 397–400).

  19. 19.

    Radwa, A., Soumya, B., Neveen, I. G., & Aboul, E. H. (2012). Towards retail market recommendations using termite colony optimization. In 2012 22nd international conference on computer theory and applications (ICCTA), 13–15 October 2012, Alexandria, Egypt (pp. 128–131).

  20. 20.

    Iftikhar, A. B., Payal, B., Sumit, S., Dipayan, M., Ashish, R., & Aravind, S. (2015). Similarity learning for product recommendation and scoring using multi-channel data. In 2015 IEEE 15th international conference on data mining workshops (pp. 1143–1152).

  21. 21.

    Venu Gopalachari, M., & Sammulal, P. (2014) Personalized collaborative filtering recommender system using domain knowledge. In 2014 international conference on computer and communications technologies (ICCCT) (pp. 1–6).

  22. 22.

    Gökhan, S., & Hale, D. (2015). Analysis and prediction of E-Customers’ behavior by mining clickstream data. In 2015 IEEE international conference on big data (big data) (Vol. 29, pp. 1466–1472).

  23. 23.

    Parinaz, N., & Sasan, H. A. (2016). An extension of multinomial choice model for customer purchase behavior analysis. In 2016, artificial intelligence and robotics (IRANOPEN) (pp. 61–66).

  24. 24.

    Minghua, H. (2008). Customer segmentation model based on retail consumer behavior analysis. In International symposium on intelligent information technology application workshops (pp. 914–917).

  25. 25.

    Rana, A. A., Elemam, S. M., Shereen, M., & Nermeen, M. (2015). Performance study of classification algorithms for consumer online shopping attitudes and behavior using data mining. In 2015 fifth international conference on communication systems and network technologies (CSNT) (pp. 1344–1349).

  26. 26.

    Tallapragada, S., Rao, A., & Kanapala, S. (2017). EMOMETRIC: An IOT integrated big data analytic system for real time retail customer’s emotion tracking and analysis. International Journal of Computational Intelligence Research,13(5), 673–695.

  27. 27.

    Silahtaroglu, G. (2015). Predicting gender of online customer using artificial neural networks. In Proceedings of international conference on management and information technology, New York, 28th August (pp. 45–50).

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Correspondence to J. Raj Kannan.

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Raj Kannan, J., Sabitha, R., Karthik, S. et al. Mouse Movement Pattern Based Analysis of Customer Behavior (CBA-MMP) Using Cloud Data Analytics. Wireless Pers Commun (2020). https://doi.org/10.1007/s11277-020-07055-1

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Keywords

  • Data analytics
  • Customer behavior
  • Mouse movement pattern
  • Classification
  • Decision tree