Comparison of Multivariate Statistical Analysis and Machine Learning Methods in Retailing: Research Framework Proposition

7 Pages Posted: 20 Nov 2018

See all articles by Ivica Ćorić

Ivica Ćorić

Hera Company, Bosnia and Herzegovina

Date Written: September 8, 2016

Abstract

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.

Keywords: multivariate statistical analysis, RFM, machine learning, customer profitability, forecasting, knowledge

JEL Classification: C45, C53

Suggested Citation

Ćorić, Ivica, Comparison of Multivariate Statistical Analysis and Machine Learning Methods in Retailing: Research Framework Proposition (September 8, 2016). 2016 ENTRENOVA Conference Proceedings. Available at SSRN: https://ssrn.com/abstract=3282274

Ivica Ćorić (Contact Author)

Hera Company, Bosnia and Herzegovina ( email )

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