LAUSR

Abstract Companies can improve their customer relationships and business performance via analytical applications such as estimation of customer lifetime value (CLV) and profitability, customer profiling and classification, customer retention and churn analyses. Customer Relationship Management (CRM) tools can now have access to relationship and interaction data of the customers, besides the traditional data sets such as billing information. While there has been a sharp increase in mining social and interaction data, integration of this information with the current data analytical models is limited. In this paper, we develop a new model for estimating the customer lifetime value in airline industry that integrates customers' social network and flight information. We first adopt a regression model for airline customers that can be used to estimate their CLVs. We then present a methodology to enhance this base model with customers' social network information to incorporate indirect contributions the customers make. We compare the performances of both models to show that our proposed method may improve the accuracy and reliability of models that make use of only flight related factors. We provide examples to potential customer analyses using our models for use by airline CRM applications.