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The practical significance of AI for customer services in retail

Researchers of the Queensland University of Technology (QUT) as a part of an international research team propose an AI-based store layout design framework for retailers. This way store managers can take advantage of the latest advances in AI techniques, and its subfields in computer vision and deep learning to monitor and analyze shopping behaviors of their customers.

An efficient store design works to draw customers' attention to products they weren't planning to buy, increase browsing time, and make it easier to find related or alternative items grouped together. Comprehending customer emotion as they look for products could provide marketers and managers with a valuable tool for better understanding customer reactions to the merchandise they sell.

Along with recognizing emotions through facial cues and customer characterisation, layout managers could employ heat map analytics, human trajectory tracking and customer action recognition techniques to inform their decisions. All this can be assessed directly from the in-store video and can be useful for better understanding customer behavior in the stores without knowing any personal or customer-identifying information.

Professor Clinton Fookes said the team had proposed the Sense-Think-Act-Learn (STAL) framework for retailers to achieve all of the above: “Firstly, Sense is to collect raw data, say from video footage from a store’s CCTV cameras for processing and analysis. Store managers routinely do this with their own eyes; however, new approaches allow us to automate this aspect of sensing, and to perform this across the entire store.

Secondly, Think is to process the data collected through advanced AI, data analytics, and deep machine learning techniques, like how humans use their brains to process the incoming data.

Thirdly, Act is to use the knowledge and insights from the second phase to improve and optimize the supermarket layout. The process operates as a continuous learning cycle”.

According to Professor Fookes: “An advantage of this framework is that it allows retailers to evaluate store design predictions such as the traffic flow and behavior when customers enter a store, or the popularity of store displays placed in different areas of the store”.

The QuData team came to similar conclusions about the need for behavior analysis of game users, since constant monitoring of user engagement is an integral part of game development nowadays.

For the Game Processes Analysis, Qudata developed a comprehensive KPI tracking system from scratch. The system provides for generating a customizable set of reports for select products, allowing both to reflect the current project performance and forecast player behavior using segmentation, conversion analysis, entry funnel, A/B testing, shopping behavior analysis etc.

Read more information about game user behavior analysis by QuData here