Sequentially Clustered Social Opinion for Improved Customer Management in Churn prediction

Abstract

As business competition continues to increase in different sectors of the economy, chum prediction models as a social behaviour have continued to impact the classification of customers for enhanced decision support and recommendations through text analytics. While it is generally believed that user’s opinion can be gradually influenced by friends on the social media, the prospect of a churning customer increases proportionately when the friend churns. However, oftentimes, during media exchange on the social network, customers are treated distinctly for positive, negative and neutral opinion classifications. By this, the network structure, sequential conversion and its influence on the user community content is neglected for churn prediction. Thus, by developing an opinion-based churn prediction model through sequential content clustering, this research explores user’s media conversation to understand the intrinsic mechanisms of sequentially formulated opinion in a social network community. Consequently, the computational similarities and sentiment between text were extracted to build an opinion driven churn prediction community for enhanced targeted customer acquisition and retention support. The essence is to harness the power of the social networking as replica for mouth-to-mouth conversation on products brands in Natural Language Processing. The research obtained 95%, 87%, and 94% in Recall, Precision, and Accuracy respectively when compared with opinion mining from ordinary data clustering.