Customer Value Hierarchy Based Customer Demand Analysis in Personalized Service Recommender System

Recommender systems are powerful tools for promoting marketing in the mobile industry. An effective recommender system can help boost the mobile service provider’s marketing by finding potential customers and recommend customers to engage additional services that are not originally engaged. Based on the background of the mobile industry, this paper proposes the framework and process of a mobile service recommendation. The customer value hierarchy-based customer demand analysis is used. Firstly, a contour model of customer value hierarchy is obtained by investigation and specific interview; secondly, the significant attributes of customer value layers are screened out; then a customer demand discrimination model is built where the customer demand objective layer is the output of the model and the customer demand attribute layer is its input. A well-formed model can dynamically identify the customer demand objectives from their engagement history record; finally, a personalized product recommendation is made. This model is used for the analysis of mobile customer samples. The results of customer demand discrimination reflect its outcome with the correct percentile exceeding 80%. Compared to existing recommendation systems, the system can identify potential customers’ demands of unsought services/products. Furthermore, it is accurate and high in intelligence level.