You Look like You’ll Buy It! Purchase Intent Prediction Based on Facially Detected Emotions in Social Media Campaigns for Food Products

Abstract

Understanding the online behavior and purchase intent of online consumers in social media can bring significant benefits to the ecommerce business and consumer research community. Despite the tight links between consumer emotions and purchase decisions, previous studies focused primarily on predicting purchase intent through web analytics and sales historical data. Here, the use of facially expressed emotions is suggested to infer the purchase intent of online consumers while watching social media video campaigns for food products (yogurt and nut butters). A FaceReader OnlineTM multi-stage experiment was set, collecting data from 154 valid sessions of 74 participants. A set of different classification models was deployed, and the performance evaluation metrics were compared. The models included Neural Networks (NNs), Logistic Regression (LR), Decision Trees (DTs), Random Forest (RF,) and Support Vector Machine (SVM). The NNs proved highly accurate (90–91%) in predicting the consumers’ intention to buy or try the product, while RF showed promising results (75%). The expressions of sadness and surprise indicated the highest levels of relative importance in RF and DTs correspondingly. Despite the low activation scores in arousal, micro expressions of emotions proved to be sufficient input in predicting purchase intent based on instances of facially decoded emotions.