Unusual customer response identification and visualization based on text mining and anomaly detection

The Vehicle Dependability Study (VDS) is a survey study on customer satisfaction for vehicles that have been sold for three years. VDS data analytics plays an important role in the vehicle development process because it can contribute to enhancing the brand image and sales of an automobile company by properly reflecting customer requirements retrieved from the analysis results when developing the vehicle's next model. Conventional approaches to analyzing the voice of customers (VOC) data, such as VDS, have focused on finding the mainstream of customer responses, many of which are already known to the enterprise. However, detecting and visualizing notable opinions from a large amount of VOC data are important in responding to customer complaints. In this study, we propose a framework for identifying unusual but significant customer responses and frequently used words therein based on distributed document representation, local outlier factor, and TF–IDF methods. We also propose a procedure that can provide useful information to vehicle engineers by visualizing the main results of the framework. This unusual customer response detection and visualization framework can accelerate the efficiency and effectiveness of many VOC data analytics.