Distribution Mix Creation Using Data-Driven Market Segmentation

Abstract

The paper deals with the data-based analysis of customers' behaviour and the creation of applicable knowledge in revenue management and marketing strategies. The analysis is based on more than 55,000 transactions mined from the property management system, which reflects the customers' behaviour in detail. The natural and artificial market segments are created using the Two-Step Clustering Method and complemented by estimating the price demand elasticity coefficient using a log-log regression model. The results are put into the context of the distribution mix, where the RFM analysis is improved to reflect the service industry better, and the recency is replaced with the elasticity of the market segments. Twelve clusters were identified, and the elasticity ranged from -3.4 to 1.6 while reflecting the behavioural characteristics of the market segments. The study's uniqueness is in combining the clustering and econometric methods for improving the distribution mix of the selected company.

Keywords

• cluster analysis
• price demand elasticity
• hotel revenue management
• market segmentation
• data mining

References

1. Abbasimehr, H., & Shabani, M. (2021). A new framework for predicting customer behavior in terms of RFM by considering the temporal aspect based on time series techniques. Journal of Ambient Intelligence and Humanized Computing, 12(1), 515–531. https://doi.org/10.1007/s12652-020-02015-w
2. Alrawadieh, Z., Alrawadieh, Z., & Cetin, G. (2021). Digital transformation and revenue management: Evidence from the hotel industry. Tourism Economics, 27(2), 328–345. https://doi.org/10.1177/1354816620901928
3. Bachtiar, F. A. (2018). Customer Segmentation Using Two-Step Mining Method Based on RFM Model. 2018 International Conference on Sustainable Information Engineering and Technology (SIET), 10–15. https://doi.org/10.1109/SIET.2018.8693173
4. Bacila, M.-F., Radulescu, A., & Marar, I. L. (2012). RFM based segmentation: An analysis of a telecom company's customers. The Proceedings of the International Conference, Marketing - from Information to Decision, 5, 52–63.
5. Barna, M., & Semak, B. (2020). Main Trends of Marketing Innovations Development of International Tour Operating. Baltic Journal of Economic Studies, 6(5), Article 5. https://doi.org/10.30525/2256-0742/2020-6-5-33-41
6. Başkol, M. (2020). RFM ve Uyum Analizi Kullanılarak Müşteri Segmentasyonunun Belirlenmesi. Business & Management Studies: An International Journal, 8(4), Article 4. https://doi.org/10.15295/bmij.v8i4.1604
7. Borges Tiago, M. T. P. M., Couto, J. P. de A., Tiago, F. G. B., & Dias Faria, S. M. C. (2016). Baby boomers turning grey: European profiles. Tourism Management, 54, 13–22. https://doi.org/10.1016/j.tourman.2015.10.017
8. Bueno, I., Carrasco, R. A., Porcel, C., & Herrera-Viedma, E. (2022). Profiling clients in the tourism sector using fuzzy linguistic models based on 2-tuples. Procedia Computer Science, 199, 718–724. https://doi.org/10.1016/j.procs.2022.01.089

9. Bult, J. R., & Wansbeek, T. (1995). Optimal Selection for Direct Mail. Marketing Science, 14(4), 378–394. https://doi.org/10.1287/mksc.14.4.378
10. Chalupa, S., & Petricek, M. (2022). Understanding customer's online booking intentions using hotel big data analysis. Journal of Vacation Marketing, 13567667221122107. https://doi.org/10.1177/13567667221122107
11. Choi, C., & Mattila, A. S. (2018). The Effects of Internal and External Reference Prices on Travelers' Price Evaluations. Journal of Travel Research, 57(8), 1068–1077. https://doi.org/10.1177/0047287517735910
12. Chung, H. C., Chung, N., & Kim, J. (2022). How do online hotel consumers perceive room rates?. Journal of Vacation Marketing, 28(3), 350–365. https://doi.org/10.1177/13567667211066326
13. Dursun, A., & Caber, M. (2016). Using data mining techniques for profiling profitable hotel customers: An application of RFM analysis. Tourism Management Perspectives, 18, 153–160. https://doi.org/10.1016/j.tmp.2016.03.001
14. Ernawati, E., Baharin, S. S. K., & Kasmin, F. (2021). A review of data mining methods in RFM-based customer segmentation. Journal of Physics: Conference Series, 1869(1), 012085. https://doi.org/10.1088/1742-6596/1869/1/012085
15. Guizzardi, A., Pons, F. M. E., & Ranieri, E. (2017). Advance booking and hotel price variability online: Any opportunity for business customers? International Journal of Hospitality Management, 64, 85–93. https://doi.org/10.1016/j.ijhm.2017.05.002
16. Guo, Y., & Peeta, S. (2020). Impacts of personalized accessibility information on residential location choice and travel behavior. Travel Behaviour and Society, 19, 99–111. https://doi.org/10.1016/j.tbs.2019.12.007
17. Helmold, M. (2020). Total Revenue Management (TRM)Total revenue management (TRM). In M. Helmold (Ed.), Total Revenue Management (TRM): Case Studies, Best Practices and Industry Insights (pp. 1–12). Springer International Publishing. https://doi.org/10.1007/978-3-030-46985-6_1
18. Hughes, A. M. (1994). Strategic Database Marketing: The Masterplan for Starting and Managing a Profitable, Customer-Based Marketing Program (First Edition). Probus Pub Co.
19. Ivanov, S. (2014). Hotel Revenue Management: From Theory to Practice. Zangador.
20. Ivanov, S., Del Chiappa, G., & Heyes, A. (2021). The research-practice gap in hotel revenue management: Insights from Italy. International Journal of Hospitality Management, 95, 102924. https://doi.org/10.1016/j.ijhm.2021.102924
21. Kaya, K., Yılmaz, Y., Yaslan, Y., Öğüdücü, Ş. G., & Çıngı, F. (2022). Demand forecasting model using hotel clustering findings for hospitality industry. Information Processing & Management, 59(1), 102816. https://doi.org/10.1016/j.ipm.2021.102816
22. Ketzenberg, M. E., Abbey, J. D., Heim, G. R., & Kumar, S. (2020). Assessing customer return behaviors through data analytics. Journal of Operations Management, 66(6), 622–645. https://doi.org/10.1002/joom.1086
23. Kimes, S. E. (1989). The Basics of Yield Management. Cornell Hotel and Restaurant Administration Quarterly, 30(3), 14–19. https://doi.org/10.1177/001088048903000309
24. Kimura, M. (2022). Customer segment transition through the customer loyalty program. Asia Pacific Journal of Marketing and Logistics, 34(3), 611–626. https://doi.org/10.1108/APJML-09-2020-0630
25. Maggon, M., & Chaudhry, H. (2018). Exploring Relationships Between Customer Satisfaction and Customer Attitude from Customer Relationship Management Viewpoint: An Empirical Study of Leisure Travellers. FIIB Business Review, 7(1), 57–65. https://doi.org/10.1177/2319714518766118
26. Martínez, R. G., Carrasco, R. A., Sanchez-Figueroa, C., & Gavilan, D. (2021). An RFM Model Customizable to Product Catalogues and Marketing Criteria Using Fuzzy Linguistic Models: Case Study of a Retail Business. Mathematics, 9(16), Article 16. https://doi.org/10.3390/math9161836
27. Masiero, L., Viglia, G., & Nieto-Garcia, M. (2020). Strategic consumer behavior in online hotel booking. Annals of Tourism Research, 83, 102947. https://doi.org/10.1016/j.annals.2020.102947
28. Miglautsch, J. R. (2000). Thoughts on RFM scoring. Journal of Database Marketing & Customer Strategy Management, 8(1), 67–72. https://doi.org/10.1057/palgrave.jdm.3240019
29. Monalisa, S. (2018). Analysis Outlier Data on RFM and LRFM Models to Determining Customer Loyalty with DBSCAN Algorithm. 2018 International Symposium on Advanced Intelligent Informatics (SAIN), 1–5. https://doi.org/10.1109/SAIN.2018.8673380
30. Monalisa, S., & Kurnia, F. (2019). Analysis of DBSCAN and K-means algorithm for evaluating outlier on RFM model of customer behaviour. TELKOMNIKA (Telecommunication Computing Electronics and Control), 17(1), Article 1. https://doi.org/10.12928/telkomnika.v17i1.9394
31. Nurjannah, Darmanto, Solimun, Budi Astuti, A., Achmad Rinaldo Fernandes, A., Amaliana, L., Yanti, I., & Isaskar, R. (2019). Two Step Cluster Analysis for Tourist Segmentation Coastal Object for Green Marketing Strategy. IOP Conference Series: Earth and Environmental Science, 239, 012019. https://doi.org/10.1088/1755-1315/239/1/012019
32. Petricek, M., Chalupa, S., & Chadt, K. (2020). Identification of Consumer Behavior Based on Price Elasticity: A Case Study of the Prague Market of Accommodation Services. Sustainability, 12(22), Article 22. https://doi.org/10.3390/su12229452
33. Petricek, M., Chalupa, S., & Melas, D. (2021). Model of Price Optimization as a Part of Hotel Revenue Management—Stochastic Approach. Mathematics, 9(13), Article 13. https://doi.org/10.3390/math9131552
34. Sarvari, P. A., Ustundag, A., & Takci, H. (2016). Performance evaluation of different customer segmentation approaches based on RFM and demographics analysis. Kybernetes, 45(7), 1129–1157. https://doi.org/10.1108/K-07-2015-0180
35. Schlich, R., Schönfelder, S., Hanson, S., & Axhausen, K. W. (2004). Structures of Leisure Travel: Temporal and Spatial Variability. Transport Reviews, 24(2), 219–237. https://doi.org/10.1080/0144164032000138742
36. Shihab, S. H., Afroge, S., & Mishu, S. Z. (2019). RFM Based Market Segmentation Approach Using Advanced K-means and Agglomerative Clustering: A Comparative Study. 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE), 1–4. https://doi.org/10.1109/ECACE.2019.8679376
37. Sota, S., Chaudhry, H., & Srivastava, M. K. (2020). Customer relationship management research in hospitality industry: A review and classification. Journal of Hospitality Marketing & Management, 29(1), 39–64. https://doi.org/10.1080/19368623.2019.1595255
38. Spoor, J. M. (2023). Improving customer segmentation via classification of key accounts as outliers. Journal of Marketing Analytics, 11(4), 747–760. https://doi.org/10.1057/s41270-022-00185-4
39. Talón-Ballestero, P., González-Serrano, L., Soguero-Ruiz, C., Muñoz-Romero, S., & Rojo-Álvarez, J. L. (2018). Using big data from Customer Relationship Management information systems to determine the client profile in the hotel sector. Tourism Management, 68, 187–197. https://doi.org/10.1016/j.tourman.2018.03.017
40. Taşabat, S. E., Özçay, T., Sertbaş, S., & Akca, E. (2023). A New RFM Model Approach: RFMS. Springer Books, 143–172.
41. Tavakoli, M., Molavi, M., Masoumi, V., Mobini, M., Etemad, S., & Rahmani, R. (2018). Customer Segmentation and Strategy Development Based on User Behavior Analysis, RFM Model and Data Mining Techniques: A Case Study. 2018 IEEE 15th International Conference on E-Business Engineering (ICEBE), 119–126. https://doi.org/10.1109/ICEBE.2018.00027
42. Unger, O., Uriely, N., & Fuchs, G. (2016). The business travel experience. Annals of Tourism Research, 61, 142–156. https://doi.org/10.1016/j.annals.2016.10.003
43. Vives, A., & Jacob, M. (2020). Dynamic pricing for online hotel demand: The case of resort hotels in Majorca. Journal of Vacation Marketing, 26(2), 268–283. https://doi.org/10.1177/1356766719867377
44. Vives, A., & Jacob, M. (2023). Sources of price elasticity of demand variability among Spanish resort hotels: A managerial insight. Journal of Hospitality and Tourism Technology, ahead-of-print(ahead-of-print). https://doi.org/10.1108/JHTT-11-2020-0298
45. Vives, A., Jacob, M., & Payeras, M. (2018). Revenue management and price optimization techniques in the hotel sector: A critical literature review. Tourism Economics, 24(6), 720–752. https://doi.org/10.1177/1354816618777590
46. Wan, X., Li, D., Chen, J., & Lei, Y. (2020). Managing customer returns strategy with the option of selling returned products. International Journal of Production Economics, 230, 107794. https://doi.org/10.1016/j.ijpe.2020.107794
47. Wu, Z., Zang, C., Wu, C.-H., Deng, Z., Shao, X., & Liu, W. (2021). Improving Customer Value Index and Consumption Forecasts Using a Weighted RFM Model and Machine Learning Algorithms. Journal of Global Information Management (JGIM), 30(3), 1–23. https://doi.org/10.4018/JGIM.20220701.oa1
48. Xian, Z., Keikhosrokiani, P., XinYing, C., & Li, Z. (2022). An RFM Model Using K-Means Clustering to Improve Customer Segmentation and Product Recommendation. In Handbook of Research on Consumer Behavior Change and Data Analytics in the Socio-Digital Era (pp. 124–145). IGI Global. https://doi.org/10.4018/978-1-6684-4168-8.ch006
49. Yang, F. X., & Lau, V. M. C. (2015). "LuXurY" hotel loyalty – a comparison of Chinese Gen X and Y tourists to Macau. International Journal of Contemporary Hospitality Management, 27(7), 1685–1706. https://doi.org/10.1108/IJCHM-06-2014-0275
50. Zheng, C., & Forgacs, G. (2017). The emerging trend of hotel total revenue management. Journal of Revenue and Pricing Management, 16(3), 238–245. https://doi.org/10.1057/s41272-016-0057-x