[Case Study] Retail/AI Function Person Attribute Analysis
Introducing examples of understanding the behavior and attributes of non-purchasing customers that cannot be obtained 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, we can analyze the gender and age of visitors, allowing us to understand the differences between purchasing and non-purchasing customers. By cross-referencing the data of non-purchasing customers with POS data, it becomes possible to determine whether the targeted demographic truly matches the customer base. Furthermore, based on these results, we can optimize the display shelves to suit the customer demographics, as well as review products, in-store signage, and promotional content. 【Case Overview】 ■Challenge - High number of store visitors but no corresponding sales ■Results - Understanding the differences between purchasing and non-purchasing customers - Optimizing display shelves based on customer demographics - Ability to review products, in-store signage, and promotional content *For more details, please refer to the related links or feel free to contact us.
- Company:Idein 本社
- Price:Other