lrfm

With the expansion of the Internet, various tools have been used to communicate with customers in organizations, and organizations use different E-CRM methods to create competitive advantages. Since customer loyalty is critical to achieving competitive advantage and profitability for organizations, one of the goals of organizations in using E-CRM is to maintain and increase customer loyalty. Therefore, considering the importance of the impact of various E-CRM services on customers’ loyalty, the purpose of this study is to investigate the impact of E-CRM on the loyalty of Bank Mellat customers using data mining techniques. The data required for this research were extracted from Bank Mellat databases. Data mining techniques include clustering with K-means algorithm and neural networks (using error-relay algorithm) and LRFM model through programming in MATLAB and Excel software were used to analyze the data. The results showed that with increasing use of E-CRM services, customers’ loyalty increases. The relationship between E-CRM, the components of LRFM model, and loyalty is a nonlinear and the change in loyalty as E-CRM changes is not a constant.  The increase in loyalty is a function of LRFM components, the amount of E-CRM and weights obtained in the neural network.

The aim of this study is to evaluate the tourist loyalty through data mining approach. The study has exemined 880 domestic tourists who have stayed in more than one night in four and five star hotels of Isfahan in spring and summer 2014 and 2015. SPSS and Clementine12 was used for data analysis.Also, Mixture Algorithm PSO-KM was applied for tourism clustering.The results showed that tourists can be classified in two categories. The first category have a high average in length of communication with tourism and travel recency and the cost and frequency of travel are less than average. Therefore, the customers are loyal and uncertain. The second category has a high average in travel recency and the length of communication with tourism, cost and frequency of travel is less than average. Therefore the customers are new and uncertain.