Modeling and predicting customer purchase behavior in the grocery retail industry

Date

2017

Author

Peker, Serhat

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In today’s business, grocery retail industry companies operate in highly competitive environment. Such an intense competition have compelled companies to develop close and long-term relationships with their customers by implementing more targeted marketing strategies and personalized services. To implement such customized services, modelling and predicting customer purchase behaviors are essential. Accordingly, this thesis mainly aims to model and predict the customers’ purchasing behavior in the grocery retail industry using machine learning techniques on past customer purchase logs. To this end, customer segmentation, product segmentation, prediction of customers’ individual purchase behaviors, and shopping list prediction are studied and a novel evaluation metric and an approach for determining recommendation list size are proposed. This thesis may serve as a valuable reference for academics and researchers who are willing to investigate customer purchase behavior and identify hidden patterns in their transactional data, and also promises substantial benefits to marketers and decision makers of grocery retailing industry in developing customized services and marketing activities.

Subject Keywords

Electronic commerce., Consumer profiling., Machine learning., Artificial intelligence.

URI

http://etd.lib.metu.edu.tr/upload/12621196/index.pdf
https://hdl.handle.net/11511/26517

Collections

Graduate School of Informatics, Thesis

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Citation Formats

S. Peker, “Modeling and predicting customer purchase behavior in the grocery retail industry,” Ph.D. - Doctoral Program, Middle East Technical University, 2017.