Improving customer churn prediction by data augmentation using pictorial stimulus-choice data

The purpose of this paper is to determine the added value of pictorial stimulus-choice data in customer churn prediction. Using Random Forests and 5 times 2 fold cross-validation, this study analyzes how much pictorial stimulus choice data and survey data increase the AUC of a churn model over and above administrative, operational and complaints data. The finding is that pictorial-stimulus choice data significantly increases AUC of models with administrative and operational data. The practical implication of this finding is that companies should start considering mining pictorial data from social media sites (e.g. Pinterest), in order to augment their internal customer database. This study is original in that it is the first that assesses the added value of pictorial stimulus-choice data in predictive models. This is important because more and more social media websites are focusing on pictures

Identifying new product ideas : waiting for the wisdom of the crowd or screening ideas in real time

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Evaluating multi-label classifiers and recommender systems in the financial service sector

status: publishe

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