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Predicting Market Basket Additions as a Way to Enhance Customer Service Levels

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Book cover Exploring Service Science (IESS 2020)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 377))

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Abstract

It is imperative that online companies have a complete in-depth understanding of online behavior in order to provide a better service to their customers. This paper proposes a model for real-time basket addition in the e-grocery sector that includes predictors inferred from anonymous clickstream data, such as a Markov page view sequence discrimination value. This model aims at anticipating the addition and the non-addition of items to customers’ market basket, in order to enable marketers to act conveniently, for example recommending more appropriate items. Two classification techniques are used in the empirical study: logistic regression and random forests. A real sample of anonymous clickstream data taken from the servers of a European e-retailing company is explored. The empirical results reveal the high predictive power of the model proposed, based on the explanatory variables introduced, as well as the supremacy of random forests over logistic regression.

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References

  1. Juliá-Igual, J.F., Cervelló-Royo, R., Berné-Lafuente, I.: Market value analysis of a Chinese e-commerce holding group: a multicriteria approach. Serv. Bus. 11(3), 475–490 (2017)

    Article  Google Scholar 

  2. Cranshaw, J., Toch, E., Hong, J., Kittur, A., Sadeh, N.: Bridging the gap between physical location and online social networks. In: Proceedings of the 12th ACM International Conference on Ubiquitous Computing, Series Bridging the Gap Between Physical Location and Online Social Networks, pp. 119–128, Copenhagen (2010)

    Google Scholar 

  3. Fernández-Sabiote, E., Román, S.: The multichannel customer’s service experience: building satisfaction and trust. Serv. Bus. 10(2), 423–445 (2016)

    Article  Google Scholar 

  4. Reichheld, F.F., Schefter, P.: E-loyalty. Harv. Bus. Rev. 78(4), 105–113 (2000)

    Google Scholar 

  5. Gudigantala, N., Bicen, P., Eom, M.: An examination of antecedents of conversion rates of e-commerce retailers. Manag. Res. Rev. 39(1), 82–114 (2016)

    Article  Google Scholar 

  6. Internet Retailer: Top 500. Technical report, Vertical Web Media, Chicago (2016). https://www.internetretailer.com/top500/#!/

  7. Agudo-Peregrina, Á.F., Pascual-Miguel, F.J., Chaparro-Peláez, J.: It’s never the same: the role of homogeneity in online services. Serv. Bus. 8(3), 453–464 (2014)

    Article  Google Scholar 

  8. Perdikaki, O., Kesavan, S., Swaminathan, J.M.: Effect of traffic on sales and conversion rates of retail stores. Manufact. Serv. Oper. Manag. 14(1), 145–162 (2012)

    Article  Google Scholar 

  9. McCarty, J.A., Hastak, M.: Segmentation approaches in data-mining: a comparison of RFM, CHAID, and logistic regression. J. Bus. Res. 60(6), 656–662 (2007)

    Article  Google Scholar 

  10. Bucklin, R.E., Sismeiro, C.: Click here for Internet insight: advances in clickstream data analysis in marketing. J. Interact. Mark. 23(1), 35–48 (2009)

    Article  Google Scholar 

  11. Koeltringer, C., Dickinger, A.: Analyzing destination branding and image from online sources: a web content mining approach. J. Bus. Res. 68(9), 1836–1843 (2015)

    Article  Google Scholar 

  12. Wang, K.-Y., Ting, I.-H., Wu, H.-J.: Discovering interest groups for marketing in virtual communities: an integrated approach. J. Bus. Res. 66(9), 1360–1366 (2013)

    Article  Google Scholar 

  13. Cooley, R., Mobasher, B., Srivastava, J.: Data preparation for mining world wide web browsing patterns. Knowl. Inf. Syst. 1(1), 5–32 (2013)

    Article  Google Scholar 

  14. Liu, B.: Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-642-19460-3

    Book  MATH  Google Scholar 

  15. Laudon, K.C., Traver, G.C.: E-commerce. Pearson/Addison Wesley (2007)

    Google Scholar 

  16. Moe, W.W., Fader, P.S.: Dynamic conversion behavior at e-commerce sites. Manag. Sci. 50(3), 326–335 (2004)

    Article  Google Scholar 

  17. Montgomery, A.L., Li, S., Srinivasan, K., Liechty, J.C.: Modeling online browsing and path analysis using clickstream data. Mark. Sci. 23(4), 579–595 (2004)

    Article  Google Scholar 

  18. Sismeiro, C., Bucklin, R.E.: Modeling purchase behavior at an e-commerce web site: a task-completion approach. J. Mark. Res. 41(3), 306–323 (2004)

    Article  Google Scholar 

  19. Verheijden, R.: Predicting purchasing behavior throughout the clickstream. Master thesis, Technische Universiteit, Eindhoven (2012)

    Google Scholar 

  20. Lim, M., Byun, H., Kim, J.: A web usage mining for modeling buying behavior at a web store using network analysis. Indian J. Sci. Technol. 8(25), 1–7 (2015)

    Article  Google Scholar 

  21. Ding, A.W., Li, S., Chatterjee, P.: Learning user real-time intent for optimal dynamic web page transformation. Inf. Syst. Res. 26(2) 339-359 (2015)

    Article  Google Scholar 

  22. Chang, H.-J., Hung, L.-P., Ho, C.-L.: An anticipation model of potential customers’ purchasing behavior based on clustering analysis and association rules analysis. Expert Syst. Appl. 32(3), 753–764 (2007)

    Article  Google Scholar 

  23. Wang, H.B.: An Efficient K-Means Algorithm in the Data Stream Model. CSRE Press, Athens (2005)

    Google Scholar 

  24. Cho, Y.H., Kim, J.K., Kim, S.H.: A personalized recommender system based on web usage mining and decision tree induction. Expert Syst. Appl. 23(3), 329–342 (2002)

    Article  Google Scholar 

  25. Aguiar, E., Nagrecha, S., Chawla, N.V.: Predicting online video engagement using clickstreams. In: 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA) 2015, vol. 36678, pp. 1–10 (2015)

    Google Scholar 

  26. Pachidi, S., Spruit, M., van de Weerd, I.: Understanding users’ behavior with software operation data mining. Comput. Hum. Behav. 30, 583–594 (2014)

    Article  Google Scholar 

  27. Markov, A.A.: Investigation of an important case of dependent trials. Izv. Acad. 1(6), 61–80 (1907)

    Google Scholar 

  28. Durbin, R., Eddy, S.R., Krogh, A., Mitchison, G.: Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids. Cambridge University Press, Cambridge (1998)

    Book  Google Scholar 

  29. Miguéis, V.L., den Poel, D.V., Camanho, A.S., Cunha, J.F.: Predicting partial customer churn using Markov for discrimination for modeling first purchase sequences. Adv. Data Anal. Classif. 6(4), 337–353 (2012)

    Article  MathSciNet  Google Scholar 

  30. Trigila, A., Iadanza, C., Esposito, C., Scarascia-Mugnozza, G.: Comparison of logistic regression and random forests techniques for shallow landslide susceptibility assessment in Giampilieri (NE Sicily, Italy). Geomorphology 249, 119–136 (2015)

    Article  Google Scholar 

  31. Thalji, N.M., et al.: Assessment of coronary artery disease risk in 5463 patients undergoing cardiac surgery: when is preoperative coronary angiography necessary? J. Thorac. Cardiovasc. Surg. 146(5), 1055–1064.e1 (2013)

    Article  Google Scholar 

  32. Hanley, J.A., McNeil, B.J.: The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143(1), 29–36 (1982)

    Article  Google Scholar 

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Authors and Affiliations

  1. INESC TEC and Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal

    Vera L. Migueis & Ricardo Teixeira

Authors
  1. Vera L. Migueis
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  2. Ricardo Teixeira
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Correspondence to Vera L. Migueis .

Editor information

Editors and Affiliations

  1. University of Porto, Porto, Portugal

    Henriqueta Nóvoa

  2. University Politehnica of Bucharest, Bucharest, Romania

    Monica Drăgoicea

  3. Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany

    Niklas Kühl

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Migueis, V.L., Teixeira, R. (2020). Predicting Market Basket Additions as a Way to Enhance Customer Service Levels. In: Nóvoa, H., Drăgoicea, M., Kühl, N. (eds) Exploring Service Science. IESS 2020. Lecture Notes in Business Information Processing, vol 377. Springer, Cham. https://doi.org/10.1007/978-3-030-38724-2_9

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  • DOI: https://doi.org/10.1007/978-3-030-38724-2_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-38723-5

  • Online ISBN: 978-3-030-38724-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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