Comparison of Multivariate Statistical Analysis and Machine Learning Methods in Retailing: Research Framework Proposition
Keywords:
multivariate statistical analysis, RFM, machine learning, customer profitability, forecasting, knowledgeAbstract
The aim of this paper is comparison of multivariate statistical analysis and machine learning methods based on the model used for the measurement of current and forecasting of the future customer profitability. Modern customer profitability analysis shows that customer-company relationship is burdened, beside costs of product, with many other different costs generated by business activities. Such costs generated by logistics, post-sale support, customer administration, sale, marketing etc. are allocated in customer’s base in non-linear way. Allocation can vary significantly from customer to customer, making the reason why each different customer’s monetary unit of revenue does not participate in profit in the same way. The research model uses RFM model to define forecasting variables and neural network, multivariate regression analysis and binary logistic regression as forecasting methods. This paper shows the ways how proposed methods can be used in process of forecasting customer profitability giving comparison of their application in that field.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
References
Berger, P.D., Nasr, N.I. (1998), “Customer Lifetime Value: Marketing Models and Applications”, Journal of Inter- active Marketing, Vol. 12 No.1, pp. 17-30.
Campbell, D.,Frei, F. (2004),“The persistence of customer profitability: Empirical evidence and implications from a financial service firm”, Journal of Service Research, Vol. 7 No.2, pp. 107–123.
Cheng C.-H., Chen Y.-S. (2009), “Classifying the segmentation of customer value via RFM model and RS theory“, Expert Systems with Applications No.36 (2009), pp. 4176–4184.
Chiu, S., Tavella, D. (2008), “Data Mining and Market Intelligence for Optimal Marketing Returns”, First Edition, Elsevier.
Donkers, B., Verhoef, P.C., de Jong, M. (2007), “Modeling CLV: A test of competing models in the insurance industry”, Quantitative Marketing and Economics, Vol. 5 No.2, pp. 163–190.
Gupta, M., Foster, G.,Sjoblom, L. (1996),“Customer Profitability Analysis: Challenges and New Directions”, Cost Management Spring, pp. 5–17.
Howson, C. (2008), “Successful Business Intelligence: Secrets to Making BI a Killer App”, McGraw-Hill.
Hughes, A. M. (1994), “Strategic database marketing”, Chicago: Probus Publishing Company.
Krakhmal, V. (2006), “Customer profitability analysis in service industries”, BAA Annual Conference, Apr 2006, pp.11-13, Portsmouth, UK.
Mulhern, F. J. (1999), “Customer Profitability Analysis: Measurement, Concentration, and Research Directions”, Journal of Interactive Marketing, Vol.13 No.1, pp. 25-40.
Ngai, E.W.T (2005), “Customer relationship management research (1992-2002): An academic literature review and classification”, Marketing Intelligence, Planning 23, pp. 582-605.
Ngai, E.W.T, Xiu, L., Chau, D.C.K (2009), “Application of data mining techniques in customer relationship management: A literature review and classification”, Expert Systems with Application, Vol. 36 (2009), pp. 2592-2602.
Niraj, R., Gupta, M., Narasimhan, C. (2001),“Customer profitability in a supply chain”. Journal of Marketing, Vol. 65(July), pp.1–16.
Panian, Ž., Klepac, G. (2003), “Business Intelligence” (Poslovnainteligencija), Zagreb: Masmedia.
Panian, Ž. (2007),“Business Intelligence, case studies from Croatian practice” (Poslovna inteligencija, studije slučajeva iz hrvatske prakse), Zagreb: Narodne Novine.
Pourshahriar, H. (2012), “Correct vs. accurate prediction: A comparison between prediction power of artificial neural networks and logistic regression in psychological researches”, Procedia - Social and Behavioral Sciences, Vol. 32 (2012), pp. 97-103.
Reinartz,W. J., Kumar, V. (2003),“The impact of customer relationship characteristics on profitable lifetime duration”, Journal of Marketing, Vol.67, pp. 77–99.
Rust, R.T., Kumar, V., Venkatesan, R. (2011), “Will the frog change into a prince? Predicting future customer profitability”, International Journal of Research in Marketing, Vol. 28 (2011), pp. 281–294.
Sonnenburg, S., Ratsch, G., Henschel, S., Widmer, C., Behr, J., Zien, A., de Bona, F., Gehl, C., Binder, A., Franc, V. (2010), “The SHOGUN Machine Learning Toolbox”, Journal of Machine Learning Research, Vol.11, pp. 1799-1802.
vanRaaij, E., Vernooij, M., van Triest, S. (2003),“The implementation of customer profitability analysis: A case study”, Industrial Marketing Management, Vol. 32 No.7, pp. 573-583.
Vercellis, K. (2009), “Business Intelligence: Data Mining and Optimization for Decision Making”, John Wiley and Sons.
