Enhanced Customer Lifetime Value Modeling: Integrating Conformity and Network Influence Through Sentiment Analysis

The growing use of online social networks has intensified the focus on customer lifetime value (CLV), particularly how social conformity and influence shape purchasing decisions. Traditional CLV models have primarily captured positive interactions within networks but have largely ignored negative influences and the degree of customer susceptibility to conformity. These oversights limit the effectiveness of existing models when applied to real-world datasets. This paper introduces a novel model that enhances the estimation of CLV by incorporating both positive and negative interactions through sentiment analysis. The model leverages the CASINO algorithm to integrate social influence and conformity extracted from interpersonal interactions, building upon the foundational customer lifetime network value model. We validate our model using both semi-simulated and real-world data, comparing its performance with traditional approaches and illustrating its improved capacity to reflect complex social dynamics in CLV calculations.