A Study on a Predictive Model of Customer Defection in a Hotel Reservation Website

Han, Shaoyong

doi:10.1051/matecconf/201822801009

Cited by 2 publications

(1 citation statement)

References 12 publications

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“…The results showed that the random forest algorithm can better solve the classification problem of CCP, and the accuracy of the prediction model reached 94%. In the research of Han [10], the customer churn of hotel reservation websites has been investigated in China. In this research, logistic regression and random forest algorithms were used to identify the characteristics that affect customer churn.…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of the Customer Churn Prediction Project in the Hotel Industry Based on Text Mining and the Random Forest Algorithm

Taherkhani,

Daneshvar,

Amoozad Khalili

et al. 2023

Advances in Civil Engineering

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The ability of hotels to differentiate themselves from competitors and continue to operate profitably depends on their ability to retain their customers by building long-term and permanent customer relationships. Technological developments in recent years have made it possible for companies to predict their customers’ behavior by accessing their opinions faster and preventing them from churning. Managing customer churn prediction projects has become an important issue today, especially in the hotel industry. Therefore, this research seeks to analyze projects that predict the churn of hotel customers to provide a model to help hotel managers in this field. In this research, an approach based on text mining on customers’ comments in the Persian language is presented, which uses the random forest algorithm for classification that was considered the most effective method to solve this problem. In this model, to increase the efficiency of the proposed method in compare with existing works, the gravitational search algorithm was used to select the useful features, and the differential evolution algorithm was used to adjust the parameters of the classification method. The dataset of this research is the collected data from the customer database on social networks and hotels’ websites, especially the hotels on Kish Island in Iran. The results of this research showed that after the implementation of the preprocessing operations, the method of adjusting the parameters and removing the unimportant features, the model’s accuracy increased significantly. The precision, recall, F1, and accuracy criteria were 0.77, 0.76, 0.76, and 0.77, respectively.

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Section: Literature Reviewmentioning

confidence: 99%

Analysis of the Customer Churn Prediction Project in the Hotel Industry Based on Text Mining and the Random Forest Algorithm

Taherkhani,

Daneshvar,

Amoozad Khalili

et al. 2023

Advances in Civil Engineering

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Using machine learning methods to predict future churners: an analysis of repeat hotel customers

Dursun-Cengizci,

Caber

2024

IJCHM

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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.

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A Study on a Predictive Model of Customer Defection in a Hotel Reservation Website

Cited by 2 publications

References 12 publications

Analysis of the Customer Churn Prediction Project in the Hotel Industry Based on Text Mining and the Random Forest Algorithm

Analysis of the Customer Churn Prediction Project in the Hotel Industry Based on Text Mining and the Random Forest Algorithm

Using machine learning methods to predict future churners: an analysis of repeat hotel customers

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