Marketing strategies have been one of the top considerations in commercial companies due to the effect on business sales lead to the revenue. In order to build an effective marketing strategy, companies need understand of customer’s preferences and needs. With the digital marketing, many companies are able to have customers around the world. Managing and analyzing the customer data in large scale and transform it to useful information is necessary for supporting to business under uncertain environment. This study aims to provide a new two-phase framework for segmenting customers based on the RFM model. Phase 1 implements fuzzy C mean clustering algorithms to segment the customers. Phase 2 focuses on ranking customer segment using weighted interval-valued dual hesitant fuzzy sets which allow to integrated opinions of groups decision makers even different knowledge and points of view when giving judgments. A numerical example in e-commerce is provided to illustrate the proposed method for solving practical problems. The results obtained from the model can be used to develop object-oriented marketing strategies or to develop customer relationship management campaigns.