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 showed a higher prediction performance with an 80% accuracy rate. The most important variables were RFM-based, followed by hotel sector-specific variables such as market, season, accompaniers and booker. Some managerial strategies were proposed to retain future churners, clustered as “hesitant,” “economy,” “alternative seeker,” and “opportunity chaser” customer groups.

Research limitations/implications

This study contributes to the theoretical understanding of customer behavior in the hospitality industry and provides valuable insight for hotel practitioners by demonstrating the methods that facilitate the identification of potential churners and their characteristics.

Originality/value

Most customer retention studies in hospitality either concentrate on the antecedents of retention or customers’ revisit intentions using traditional methods. Taking a unique place within the literature, this study conducts churn prediction analysis for repeat hotel customers by opening a new area for inquiry in hospitality studies.

中文翻译：

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使用机器学习方法预测未来的流失者：对酒店回头客的分析

目的

本研究旨在通过计算回头客未来入住同一酒店品牌的流失概率来预测度假酒店的客户流失率。

设计/方法论/途径

基于新近度、频率、货币 (RFM) 范式，使用随机森林和逻辑回归监督机器学习算法来预测客户流失行为。具有优越性能的模型用于检测潜在的流失者并生成优先级矩阵。

发现

随机森林算法表现出较高的预测性能，准确率达到80%。最重要的变量是基于 RFM 的，其次是酒店行业特定的变量，例如市场、季节、陪伴者和预订者。提出了一些管理策略来留住未来的流失者，这些客户群分为“犹豫型”、“经济型”、“另类寻求者”和“机会追逐者”客户群。

研究局限性/影响

这项研究有助于从理论上理解酒店行业的客户行为，并通过展示有助于识别潜在流失者及其特征的方法，为酒店从业者提供宝贵的见解。

原创性/价值

酒店业的大多数客户保留研究要么集中在保留的前因上，要么集中在使用传统方法的客户重访意图上。这项研究在文献中占有独特的地位，通过开辟酒店研究的新探究领域，对酒店回头客进行流失预测分析。