Researchers at the Queensland University of Technology (QUT) as part of an international research team offer a AI AI layout design framework For retailers. In this way, store managers can take advantage of the latest advances in AI techniques and its sub-domains in computer vision and in-depth learning to monitor and analyze their customers' purchase behavior.
An effective store design works to attract customers' attention to the products they did not plan to buy, increase navigation time and facilitate the search for related or alternative items. Understanding customer emotions when they are looking for products could provide marketing specialists and managers a precious tool to better understand customer reactions to the goods they sell.
In addition to recognizing emotions through facial signs and customer characterization, disposition managers could use the analysis of thermal cards, monitoring human trajectory and customer action recognition techniques to clarify their decisions. All of this can be evaluated directly from store video and can be useful to better understand customer behavior in stores without knowing personal information or customer identification.
Professor Clinton Fookes said that the team had proposed the framework of Sens-Think-Act-Learn (Stal) so that the retailers realize all of the above: “First, Sense is to collect raw data, for example from video sequences from the video surveillance cameras of a store for processing and analysis. Store managers regularly do this with their own eyes; However, the new approaches allow us to automate this aspect of detection and to perform them throughout the store.
Secondly, Think is to process the data collected via advanced AI, analysis of deep automatic learning techniques and techniques, such as the way humans use their brains to process incoming data.
Thirdly, Act is to use the knowledge and ideas of the second phase to improve and optimize the layout of supermarkets. The process works as a continuous learning cycle ”.
According to Professor Fookes: “An advantage of this framework is that it allows retailers to assess store design forecasts such as traffic flow and behavior when customers enter a store or the popularity of store screens placed in different areas of the store”.
The Qudata team has taken similar conclusions on the need for analysis of the behavior of game users, because constant monitoring of user engagement is an integral part of the development of games today.
For the analysis of game processes, Qudata has developed a complete KPI monitoring system from zero. The system provides for the generation of a customizable report of reports for certain products, allowing both to reflect the current project performance and the behavior of the expected players using segmentation, conversion analysis, entry funnel, A / B tests, purchasing behavior analysis, etc.
Read more information on analyzing the behavior of users of the game by Qudata here
