Improving the accuracy of VOC data summarization and classification! An introduction to the background and utilization of generative AI training.
At Osaka Gas Co., Ltd., we introduced a case study on the implementation of generative AI training, focusing on the summarization and classification of texts using the "Visualization Engine" AI features. As we manage customer feedback (such as VOC and social media posts) from various channels in a centralized manner to enhance services and improve operations, we faced challenges in the rapid and accurate (not reliant on individual judgment) analysis of the increasing volume of feedback. By utilizing generative AI features, we realized the potential to streamline daily and monthly analysis tasks and report creation, making traditional processes simpler and more effective. [Case Overview] ■ Challenges - Rapid and accurate (not reliant on individual judgment) analysis of increasing feedback ■ Achievements - Streamlined daily and monthly analysis tasks and report creation, realizing the potential to advance traditional processes in a simpler and more effective manner - Improved the accuracy of summarization and classification of VOC data through prompt tuning *For more details, please refer to the related links or feel free to contact us.
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As a company that continues to create added value from all kinds of information, we have been providing cloud solutions centered around technologies such as 'text mining' and 'data mining' since our establishment in 2006. We visualize big data, such as customer voices, customer data/purchase data, and HR information, and have the power to provide insights.