Depth camera based customer behaviour analysis for retail: Master's thesis

In 2000s traditional shop-based retailing has had to adapt to competition created by internet-based e-commerce. As a distinction from traditional retail, e-commerce can gather unprecedented amount of information about its customers and their behaviour. To enable behaviour-based analysis in traditional retailing, the customers need to be tracked reliably through the store. One such tracking technology is depth camera people tracking system developed at VTT, Technical Research Centre of Finland Ltd. This study aims to use the aforementioned people tracking system's data to enable e-commerce style behavioural analysis in physical retail locations. This study is done following the design science research paradigm to construct a real-life artefact. The artefact designed and implemented is based on accumulated knowledge from a systematic literature review, application domain analysis and iterative software engineering practices. Systematic literature review is used to understand what kind of performance evaluation is done in retail. These metrics are then analysed in regards to people tracking technologies to propose a conceptual framework for customer tracking in retail. From this the artefact is designed, implemented and evaluated. Evaluation is done by combination of requirement validation, field experiments and three distinct real-life field studies. Literature review found that retailing uses traditionally easily available performance metrics such as sales and profit. It was also clear that movement data, apart from traffic calculation, has been unavailable for retail and thus is not often used as quantifiable performance metric. As a result this study presents one novel way to use customer movement as a store performance metric. The artefact constructed quantifies, visualises and analyses customer tracking data with the provided depth camera system, which is a new approach to people tracking domain. The evaluation with real-life cases concludes that the artefact can indeed find and classify interesting behavioural patterns from customer tracking data.