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

Konferenzbeitrag

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: In: Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Rovinj, Croatia, 8-9 September 2016 ; Year: 2016 ; Pages: 76-82 ; Zagreb: IRENET - Society for Advancing Innovation and Research in Economy

Classification and Topics

• Classification: Wirtschaft ; Neural Networks and Related Topics ; Forecasting Models; Simulation Methods
• Subject: multivariate statistical analysis ; RFM ; machine learning ; customer profitability ; forecasting ; knowledge

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Ćorić, Ivica
• Event: Veröffentlichung
• (who): IRENET - Society for Advancing Innovation and Research in Economy
• (where): Zagreb
• (when): 2016

Further information

• Handle: http://hdl.handle.net/10419/183702
• Last update: 10.03.2025, 11:44 AM CET