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[Case Study] Real Estate / AI Function Person Count and Attribute Analysis

[Case Study] Real Estate / AI Function Person Count and Attribute Analysis

We will introduce a case where the event attendance situation at commercial facilities can be understood in real time.

We will introduce a case study that addresses the challenge of wanting to understand the total number of visitors to a large shopping mall and the attendance situation of events. By using the AI camera from "Actcast," it becomes possible to reduce labor costs compared to manual headcount methods. Additionally, counting errors decrease, and the reliability of the numbers increases. Furthermore, by obtaining attribute data, it becomes possible to gain a more detailed understanding of the visitor demographics, allowing for more effective planning and execution of various measures that can lead to increased sales. [Case Overview] ■ Challenge - Want to understand the number of visitors and the attendance situation of events ■ Results - Real-time understanding of event attendance - Reduction in labor costs - Decrease in counting errors, increasing the reliability of the numbers [Expected Effects] ■ By tracking the number of visitors by hour, the effectiveness of events can be measured ■ Analysis results can also be used as foundational information for tenant pricing during tenant leasing *For more details, please refer to the related links or feel free to contact us.

  • DMP
  • IoT

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[Case Study] Retail/AI Function Person Attribute Analysis

[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.

  • DMP
  • IoT

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