نيلوفر ناظريان

As online social networks facilitate increased communication between customers globally, customer lifetime value has received increasing attention. Thus, social influence, as well as social conformity, significantly impact customer purchase decisions. Previous advances in customer lifetime value models regarding network effects have predominantly utilized positive interactions. The latest approaches for eva‎luating customer value in a network context present only one refined model accounting for potential negative influences between individuals. This model is unable to leverage real-world datasets to properly implement it, as the authors state it is challenging to estimate. Therefore, established models overlook two key factors: negative interactions between individuals and conformity, meaning the extent to which a customer is inclined to be influenced. This paper proposes an enhanced model for determining customer lifetime value from a social network perspective, accounting for positive and negative customer interactions using sentiment analysis techniques. Combining conformity and social influence, extracted from interpersonal interactions via the CASINO algorithm, with the basic customer lifetime network value model, we introduce the Enhanced Customer Lifetime Network Value. Using semi-simulated data and real-world datasets, we demonstrate the application of our enhanced model for determining customer lifetime network value, comparing it to the previous basic model, and drawing conclusions from the available data.

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Niloofar Nazerian

Dr. Babak Amiri