Using machine learning methods to predict future churners: an analysis of repeat hotel customers
Aslıhan Dursun-Cengizci,
Meltem Caber
Abstract:Purpose
This study aims to predict customer churn in resort hotels by calculating the churn probability of repeat customers for future stays in the same hotel brand.
Design/methodology/approach
Based on the recency, frequency, monetary (RFM) paradigm, random forest and logistic regression supervised machine learning algorithms were used to predict churn behavior. The model with superior performance was used to detect potential churners and generate a priority matrix.
Findings
The random forest algorithm sh… Show more
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