| dc.description.abstract | Marketing strategies have always been one of the top priorities for companies due to the
effect on their revenue. In order to build a suitable marketing strategy, businesses need to
have a clear understanding of their customers, and that proper customer segmentation is
essential. Customer segmentation is one of the powerful tools for gaining an edge in a
competitive environment. This thesis aims to make sufficient use of transaction data for
identifying different types of customers by providing a new two-phase methodology for
segmenting customers based on the RFM model. Phase 1 implements fuzzy clustering
algorithms to segment the customers, where phase 2 focuses on ranking customers using
weighted interval-valued dual hesitant fuzzy sets. This method classifies customers so that
customers in each group have similar characteristics, and then assesses the importance of
each customer group. A numerical application was provided to solve a real-life problem and
thus the utilization of the proposed approach is proven. The results obtained from the model
can be used to develop object-oriented marketing strategies or to develop customer
relationship management campaigns. | en_US |
