Understanding Customer Preferences Using Image Classification – A Case Study

Today, companies have a large amount of data at their disposal. In addition to classic data in text or table form, the number of images also increases enormously. This is particularly the case if the customer contact exists via the Internet, e.g., social networks, blogs or forums. If these images can be evaluated, they lead to a better understanding of the customer. Improved recommendations can be made and customer satisfaction can be increased. This paper shows by means of support vector machines (SVM), convolutional neural networks (CNN) and cluster analyses how it is possible for companies to evaluate image data on their own and thus to understand and classify the customer. The data of travel platform users serve as a case study. Advantages and disadvantages of, as well as prerequisites for SVMs and CNNs are pointed out and segmentation of the users on the basis of their images is made

Towards Lifestyle Segmentation via Uploaded Images from Surveys and Social Networks

Today, people are more and more active in social networks and communicate via text massages, images or “likes”. Especially images are used to assist a person to provide their opinion. Images show the daily life or things that interest people (e.g. van House 2011). Thereby a huge amount of information is provided. The evaluation of images would enable a comprehensive classification of the consumer. Therefore the technologies of image classification like support vector machines (SVM) are needed. This study provides an approach to analyze images for market research. For this, we conducted a holiday survey. We asked 433 people about typical holiday activities and to upload their favorite holiday images. Overall 1,348 images have been uploaded. With the help of SVM, we could classify the images and evaluate particularly useful features. The study’s findings advance the possibilities of market research methods and provide numerous implications for researchers and practitioners