Using Eye-Tracking Data of Advertisement Viewing Behavior to Predict Customer Churn

The purpose of this paper is to assess the feasibility of predicting customer churn using eye-tracking data. The eye- movements of 175 respondents were tracked when they were looking at advertisements of three mobile operators. These data are combined with data that indicate whether or not a customer has churned in the one year period following the collection of the eye tracking data. For the analysis we used Random Forest and leave-one-out cross validation. In addition, at each fold we used variable selection for Random Forest. An AUC of 0.598 was obtained. On the eve of the commoditization of eye-tracking hardware this is an especially valuable insight. The findings denote that the upcoming integration of eye-tracking in cell phones can create a viable data source for predictive Customer Relationship Management. The contribution of this paper is that it is the first to use eye-tracking data in a predictive customer intelligence context.