Improved customer churn prediction model using word order contextualized semantics on customers’ social opinion

Abstract

Through the hype in digital marketing and the continuous increase in volume and velocity of opinions about an organization’s brands, churn prediction now requires advanced analytics in opinion mining for effective customer behavioral management beyond keywords sentiment analysis (SA). Earlier, by analyzing customers’ opinions using SA models, the extracted positive-negative polarity is used to classify customers as churners or non-churner. In those methods, the impact of word order, context, and the inherent semantics of the clustered opinion set were oftentimes overlooked. However, with the consistent creation of new words with new meanings mapped to existing words on the web, the research extended the fuzzy support vector model (FSVM) to show that the dependency distance between the headword, its dependent, and tail word can be weighted by using information content derived from a corpus to generate four-classed social opinion categories as a strongly positive, positive, negative, and strong negative. These opinion classes formed the basis for the churn category as a premium customer, Inertia customer potential churner, and churner in customer behavioral management. In performance evaluation, aside from engendering quadrupled churn class against the existing churn binary pattern, better accuracy, precision, and recall values were obtained when compared with existing SA works in support vector machine and fuzzy support vector machine (FSVM), respectively.