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Edge analysis device - List of Manufacturers, Suppliers, Companies and Products

Edge analysis device Product List

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[Case Study] Customer Service/AI Function Conversation Analysis (Speaker Separation)

[Case Study] Customer Service/AI Function Conversation Analysis (Speaker Separation)

By obtaining and textifying conversation data with customers, it becomes possible to understand the situation even without being on-site.

We will introduce a case that addresses issues such as not being able to grasp the customer service situation and content in stores, and staff being exhausted from customer harassment. By implementing "Actcast" and capturing the content of conversations as text data, we can enhance the quality of customer service and allow staff to serve customers with peace of mind. Additionally, by placing AI microphones in the store and processing the audio data to separate it into customer and staff sides, we can further convert this into text data on the server side. Based on this conversation data, we can improve employee training and implement measures against customer harassment. [Case Overview] ■ Issues - Unable to grasp the customer service situation and content in stores - Staff exhausted from customer harassment ■ Results - Ability to understand the situation even when not on-site - Improvement of employee training and implementation of measures against customer harassment based on conversation data *For more details, please refer to the related links or feel free to contact us.

  • Other measurement, recording and measuring instruments

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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 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.

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