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[Case Study] Retail/AI Function Person Attribute Analysis

Introducing examples of understanding the behaviors and attributes of non-purchasing customers that cannot be captured through POS data.

We would like to introduce a case study that addresses the issue of having a high number of store visitors without corresponding sales. By implementing "Actcast" and installing AI cameras in the store to analyze the gender and age of visitors, it becomes possible to understand the differences between purchasing and non-purchasing customers. By cross-referencing the data of non-purchasing customers with POS data, it is possible to determine whether the targeted audience truly matches the customer demographic. Furthermore, based on these results, you can optimize the display shelves to fit the customer demographic, as well as review products, in-store displays, and promotional content. [Case Overview] ■ Challenge - High number of store visitors, but not translating into sales ■ Results - Understanding the differences between purchasing and non-purchasing customers - Optimizing display shelves based on the results to match the customer demographic - Ability to review products, in-store displays, and promotional content *For more details, please refer to the related links or feel free to contact us.