An analytics model for TelecoVAS customers’ basket clustering using ensemble learning approach

Abstract

Value-Added Services at a Mobile Telecommunication company provide customers with a variety of services. Value-added services generate significant revenue annually for telecommunication companies. Providing solutions that can provide customers of a telecommunication company with relevant and engaging services has become a major challenge in this field. Numerous methods have been proposed so far to analyze customer basket and provide related services. Although these methods have many applications, they still face difficulties in improving the accuracy of bids. This paper combines the X-Means algorithm, the ensemble learning system, and the N-List structure to analyze the customer portfolio of a mobile telecommunication company and provide value-added services. The X-Means algorithm is used to determine the optimal number of clusters and clustering of customers in a mobile telecommunication company. The ensemble learning algorithm is also used to assign categories to new Elder customers, and finally to the N-List structure for customer basket analysis. By simulating the proposed method and comparing it with other methods including KNN, SVM, and deep neural networks, the accuracy improved to about 7%.