Tele Comm. Customer Data Analysis using Multi-Layer Clustering Model

Abstract

Customer segmentation provides an efficient way to get insights into customer characteristics and behavioral preferences. Two-layer clustering model for mobile telecommunication client analysis enhances customer relationship management and focuses on a dynamically dynamical marketplace. With the increase of huge data and also the evolution of data mining technology, the mass storage of internal enterprise data will be analyzed. Effectively for hidden customer worth. The promotion of promoting activities and client relationship support is supported an extensive precision-marketing model that evolved to focus on the client base and acquire in-depth understanding to suit that base’s desires. a progressively important issue is a way to integrate marketing resources and properly distribute and match individual client interests and preferences with the foremost effective marketing activities, additionally as mine knowledge to work out those products or services most attractive to customers. Data clustering and clustering algorithms allow us to cluster extremely consistent individuals and assign dissimilar people to the acceptable segments. Trade and world have several samples of clustering analysis getting used to ascertain cluster characteristics: customer grouping analysis could be a well-liked application. By analyzing client attributes, behaviors, and preferences, we will verify the high homogeneity of individual clusters, and also the high degree of unsimilarity among individuals, given the appropriate segments.