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Using eye-tracking data of advertisement viewing behavior to predict customer churn
Book Contribution - Book Chapter Conference Contribution
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.
Book: IEEE International Conference on Data Mining
Pages: 201 - 205