In order to measure the customer loyalty the customer value is an essential metric. However, if an organization provides value to their customers by satisfying them with the service then the customer loyalty increases (Ekman et al., 2017). The major strength of customer value is retention of customer. By keeping the value of the customers an organization will be able to retain their customers for long time, which increases customer loyalty. On the other hand, it is difficult to understand the changing customer perception, which is associated with the customer value. This is the major drawback of this metric. RFM stands for recency, frequency and monetary. It highlights the behavior based customer segmentation process. This is the main strength of RFM. RFM combines the different customer attributes, which is beneficial to enhance the customer loyalty. RFM analysis helps to identify different customer behavior and the trend of current market. However, if the RFM model is not applied properly due lack of resources it will hamper the customer loyalty. Share of wallet highlights the customer’s spending on multiple outlets. If the customers purchase more from an organization then they can increase their customer loyalty. To increase the customer spending it is important to satisfy the customers via service. On the other hand, increasing customer share may maximize the competition in the market.
Customer behavior is always changing over the time. Price sensitivity and decrease in the customer engagement are two leading indicators of customer defection (Wang & Ng, 2017). Often high price or changes in price affects the choice or purchasing behavior of the customers. Therefore, if the customers use the company site less and less then it highlights the poor customer engagement in an organization.
Ekman, P., Jonas, J., Maglio, P., Reynolds, D., & Röndell, J. (2017, May). Customer Value Through Resource Integration: The Role of the Institutional Solution Space: An Abstract. In Academy of Marketing Science Annual Conference (pp. 495-496). Springer, Cham.
Wang, Y., & Ng, V. (2017, May). Anomaly Detection with Attribute Conflict Identification in Bank Customer Data. In Smart Computing (SMARTCOMP), 2017 IEEE International Conference on (pp. 1-6). IEEE.
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