Applying a Decision-making Technique to Evaluate ‎the Key Factors Affecting Customer Churn Using a ‎Text-mining Approach: A Case Study in the Hotel ‎Industry

محورهای موضوعی : Computer Engineering

Leila Taherkhani ¹ , Amir Daneshvar ² , hossein Amoozad-khalili ³ , MohamadReza Sanaei ⁴

1 - Department of Information Technology, Science and Research Branch, Islamic Azad University, Tehran, Iran
2 - Department of Industrial Management, Science and Research Branch, Islamic Azad University, Tehran, Iran
3 - : Department of Industrial Engineering, Sari Branch, Islamic Azad University, Sari, Iran.
4 - Department of Information Technology Management, College of Management and Economics, Qazvin Branch, Islamic Azad University, Qazvin, Iran

تاریخ دریافت : 1402/05/11 تاریخ پذیرش : 1402/06/27 تاریخ انتشار : 1402/06/10

کلید واژه: Customer churn, Hotel Industry, Text Mining, Analytic Hierarchical Processing,

چکیده مقاله :

Customer churn is a critical challenge faced by the hotel industry, impacting revenue and profitability. Identifying and prioritizing the factors that contribute to customer churn is essential for hotel managers to devise effective retention strategies. The prioritization of these factors enables hotel managers to allocate resources efficiently towards implementing targeted retention initiatives. By understanding the factors that influence customer churn, hotels can proactively tailor their services and improve customer experiences to enhance loyalty and reduce churn rates. This study aims to determine and prioritize the key factors influencing customer churn in the hotel industry. The data is analyzed using advanced text mining techniques, including tokenizing, stemming, and eliminating stop words, to identify the significant factors affecting customer churn. The findings highlight factors such as room conditions, the beauty of the hotel, food quality, staff interactions, hotel hygiene, restrooms, and proximity to important centers. To prioritize these factors, we utilized the AHP technique in Expert Choice software. According to the findings, among these factors, staff interaction with 32%, room conditions with 22%, restrooms with 19%, food quality with 12%, proximity to important centers with 8%, hotel hygiene with 4%, and beauty of the hotel with 3%are respectively the most important factors affecting customer churn.

منابع و مأخذ:

• Buchanan, B., & Gillespie, J. (2021). Understanding customer churn in the telecommunications industry: A comprehensive review. Telecommunications Policy, 45(1), 102057.


• Ganesh, J., Arnold, M. J., & Reynolds, K. E. (2000). Understanding the customer base of service providers: An examination of the differences between switchers and stayers. Journal of Marketing, 64(3), 65-87.


• Homburg, C., Klarmann, M., & Schmitt, J. (2010). Brand awareness in business markets: When is it related to firm performance? International Journal of Research in Marketing, 27(3), 201-212.


• Keiningham, T. L., Aksoy, L., Cooil, B., & Andreassen, T. W. (2007). A longitudinal examination of customer satisfaction and share of wallet: Investigating the moderating effect of customer characteristics. Journal of Marketing, 71(1), 67-83.


• Verhoef, P. C., & Lemon, K. N. (2013). Successful customer value management: Key lessons and emerging trends. European Management Journal, 31(1), 1-15.


• Ali, B. J., Gardi, B., Jabbar Othman, B., Ali Ahmed, S., Burhan Ismael, N., Abdalla Hamza, P., ... & Anwar, G. (2021). Hotel service quality: The impact of service quality on customer satisfaction in hospitality. Ali, BJ, Gardi, B., Othman, BJ, Ahmed, SA, Ismael, NB, Hamza, PA, Aziz, HM, Sabir, BY, Anwar, G.(2021). Hotel Service Quality: The Impact of Service Quality on Customer Satisfaction in Hospitality. International Journal of Engineering, Business and Management, 5(3), 14-28.


• Lee, M., Cai, Y., DeFranco, A., & Lee, J. (2020). Exploring influential factors affecting guest satisfaction: Big data and business analytics in consumer-generated reviews. Journal of Hospitality and Tourism Technology, 11(1), 137-153.


• Padlee, S. F., Thaw, C. Y., & Zulkiffli, S. N. A. (2019). The relationship between service quality, customer satisfaction and behavioural intentions. Tourism and hospitality management, 25(1), 121-139.


• Rather, R. A., Tehseen, S., Itoo, M. H., & Parrey, S. H. (2019). Customer brand identification, affective commitment, customer satisfaction, and brand trust as antecedents of customer behavioral intention of loyalty: An empirical study in the hospitality sector. Journal of Global Scholars of Marketing Science, 29(2), 196-217.


• Mahajan, V., Misra, R., & Mahajan, R. (2017). Review on factors affecting customer churn in telecom sector. Int. J. Data Anal. Tech. Strateg., 9, 122-144. DOI:1504/IJDATS.2017.10006960


• Kandiero, A., & Makuwatsine, C. (2022). Exploring Determinants of Internet Service Provider Customer Switching Barriers Using an Exploratory Sequential Mixed Methods Research Design. Handbook of Research on Mixed Methods Research in Information Science.DOI:4018/978-1-7998-8844-4.ch015


• Aimee, R. M. (2019). A thorough literature review of customer satisfaction definition, factors affecting customer satisfaction and measuring customer satisfaction. International Journal of Advanced Research, 7(9), 828-843. DOI:21474/ijar01/9733


• De, S., & Prabu, P. (2022). Predicting customer churn: A systematic literature review. Journal of Discrete Mathematical Sciences and Cryptography, 25, 1965 - 1985. DOI:1080/09720529.2022.2133238


• Townsend, A., & Nilakanta, S. (2019). Customer churn: A study of factors affecting customer churn using machine learning (Doctoral dissertation, Iowa State University).


• Jacob, C., Sezgin, E., Sanchez-Vazquez, A., & Ivory, C. (2022). Sociotechnical factors affecting patients’ adoption of mobile health tools: systematic literature review and narrative synthesis. JMIR mHealth and uHealth, 10(5), e36284.


• Lee, S., Choi, W.G., & So, J. (2022). A Study on the Factors Affecting Customer Satisfaction in Delivery Applications: Focusing on Sentiment Analysis of Review Data. 2022 IEEE/ACIS 7th International Conference on Big Data, Cloud Computing, and Data Science (BCD), 34-38. DOI:1109/BCD54882.2022.9900519


• Witayati, S.N., Do, A.D., & Sudrajad, O.Y. (2023). Investigating Factors Influencing Customer Churn in the Online Bill Payment Services. International Journal of Current Science Research and Review.


• Keramati,A,seyedin ardebili,S.M.,sohrabi,B.,(2008), Analysis churn customers: Check the status of Iran's mobile operators with data mining techniques, Journal of Management Sciences in Iran,14,63-91.


• Tamadoni Jahromi, A. (2009). “churn customer prediction in telecommunication service providers”,final thesis of master degree,tarbiat modares university.


• Tavakoli, A., Mortazavi, S., Kahani, M. & Hosseini, Z. (2009). “Application of data mining models to predict customer churn in insurance”, Journal of Business Perspective,4(37),41-55.


• Sepehri, M.M., Norozi, A., Teymorpur, B. & Chubdar, S. (2010). “Customer churn ˓ reasons of banking services by combining data mining and survey methods”, Research in Management in Iran,14(15).


• Allahyari Soeini, R., & Vahidy Rodpysh, k. (2012). “Applying Data Mining to Insurance Customer Churn Management”, IACSIT Hong Kong Conferences.


• Madan, M., Dave, M., & Nijhawan, V. K. (2015). A Review on: Data mining for telecom customer churn management. International Journal of Advanced Research in Computer Science and Software Engineering, 5(9).


• Saleh, S., & Saha, S. (2023). Customer retention and churn prediction in the telecommunication industry: a case study on a Danish university. SN Applied Sciences, 5(7), 173.


• Li, J., Bai, X., Xu, Q., & Yang, D. (2023). Identification of Customer Churn Considering Difficult Case Mining. Systems, 11(7), 325.


• Troncoso, C. A. M. (2019). Predicting Customer Churn using Voice of the Customer. A Text Mining Approach. The University of Manchester (United Kingdom).


• Schatzmann, A., Heitz, C., & Münch, T. (2014). Churn prediction based on text mining and CRM data analysis. In 13th International Science-to-Business Marketing Conference:«Cross Organizational Value Creation», Winterthur, 2-4 June 2014 (pp. 296-310). Fachhochschule Münster.


• Vo, N. N., Liu, S., Li, X., & Xu, G. (2021). Leveraging unstructured call log data for customer churn prediction. Knowledge-Based Systems, 212, 106586.


• Adebiyi, S. O., Oyatoye, E. O., & Amole, B. B. (2016). Improved customer churn and retention decision management using operations research approach. EMAJ: Emerging Markets Journal, 6(2), 12-21.


• Rave, J. I. P., Álvarez, G. P. J., & Morales, J. C. C. (2022). Multi-criteria decision-making leveraged by text analytics and interviews with strategists. Journal of Marketing Analytics, 1-20.


• Jacob, R., & Subramoniam, S. (2021). Identifying the critical success factors of telecom switching barriers using the AHP. Global Business Review, 22(3), 767-779.


• Vaidya, O. S., & Kumar, S. (2006). Analytic hierarchy process: An overview of applications. European Journal of Operational Research, 169(1), 1-29.


• Lipovetsky, S., & Conklin, M. (2004). A practical guide to AHP for decision making. International Journal of Information Technology & Decision Making, 3(4), 501-516.