| dc.description.abstract | The digital trajectory of the modern retail industry has developed multiple dimensions such as products, transactions, behaviors, and memberships. To maintain competitiveness, companies adopt customer-centric strategies, making customer relationship management increasingly important. In this context, customer churn phenomena and their causes deserve special attention. Markov Chain is a probabilistic model used to represent the likelihood of transitioning from one state to another. Therefore, the application of Markov Chain is suitable for describing the probability of customers transitioning from purchasing one category of goods to another, providing estimates of future outcomes.
This study proposes a model for predicting customer churn in retail businesses. It utilizes the similarity between a customer′s purchase sequence and the churn and non-churn sequences as predictive factors. A Markov Discrimination Model is used to differentiate the sequence of purchase events. In a case study, this research uses two years of data provided by a Taiwanese retail company. After data cleaning and organization, four variables are obtained: Recency (the customer′s most recent purchase), Frequency (the customer′s purchase frequency), Monetary (the customer′s purchase amount) and Likelihood (the likelihood of churn or non-churn). Next, using logistic regression classification techniques, focusing on new customers as the research subjects, RFM and RFML customer classification models are established. Finally, the Receiver Operating Characteristic (ROC) curve′s Area Under the Curve (AUC) and accuracy are used to evaluate the models′ ability to identify churners and classify correctly.
The research results show that the Markov customer churn prediction model has sufficient ability to identify customer churn and maintains stable accuracy rates in different situations. In Scenario 2, when the commonly used RFM classification model is combined with the Likelihood variable, both AUC and accuracy show significant improvements. This study establishes a customer churn prediction model to identify potentially churned customers and provide key customer characteristics, helping businesses understand customer value in advance and reduce customer churn. | en_US |