Using data mining techniques for building customer profiles in hotel firms: RFM model example

It's important for hotel firms to build their customer profiles in order to learn about their customers and to find out the most valuable ones. Once they understand their preferences, companies can develop specific marketing and CRM (Customer Relationship Management) strategies for their customers. Thus, they can meet the demands of their customers, increase their loyalty and the satisfaction they gain from the services and make them to buy from the company again. The main purpose of this study is using data mining techniques to find the hidden customer information from the existing customer data and to build customer profiles. With the help of determining to which variables the hotel firms should pay more attention, they can develop different marketing strategies for different customer profiles and increase the efficiency of CRM. Research methods include one of the data mining techniques called RFM (recency, frequency and monetary) model which applied to the existing customer data of a five-star chain hotel in Antalya. To decide the number of data clusters self-organizing maps method and for cluster analysis the K-means method were used in the study. Based on the results obtained, the data mining techniques help identify eight different groups of customers including loyal customers, loyal summer season customers, collective buying customers, winter season customers, lost customers, high potential customers, new customers and winter season high potential customers and develop marketing strategies for the eight different customer profiles. Keywords: Data Mining, Customer Relationship Management, RFM Model, Antalya