[Case Study of Visualization Engine Implementation] Book Off Corporation Co., Ltd.

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last updated：May 29, 2025

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Improving customer experience based on customer feedback, both in-store and online. Reducing inquiries through improvements in the store environment and enhancements to the online site’s guidance.

We would like to introduce the case study of the implementation of the "Visualization Engine" by Book Off Corporation, which operates approximately 800 reuse shops nationwide. With over 20,000 VOCs collected in peak months, the tool was essential for both improving the efficiency of analysis and enhancing its quality. The decision to implement this system was based on its rich variety of analysis types, high flexibility for cross-analysis, and the clarity of trend changes. It has made it easier to identify issues, difficulties, and customer needs, leading to improvement measures. [Challenges and Background] - Monthly reports were conducted amidst the operation of the customer center. Due to a lack of resources, it took about a week each month just for aggregation and reporting. - Manual aggregation was the limit, preventing the allocation of time to the analyses and proposals that should have been prioritized, resulting in a decrease in the quality of analysis and the number of proposals. - As the number of customers using both in-store and online services increased, there was a demand for integrated data analysis to achieve seamless customer understanding. *For more details, please refer to the related links or feel free to contact us.*

Related Link - https://www.mieruka-engine.com/case/bookoff/

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【Results】 ■ The report creation time, which used to take a week, can now be completed in three days. ■ The email open rate for reports increased from about 16% to 29% over the course of a year. ■ It has become easier to identify issues, difficulties, and needs, both in-store and online, leading to improvement measures. ■ After detecting an increase in inquiries regarding the shipping service and analyzing the reasons, we changed the explanatory text as an improvement measure, resulting in inquiries decreasing from 60 to 1 the following month and to 0 the month after that. ■ Through analysis based on review ratings, we demonstrated with evidence that measures for recommendations, neutrality, and criticism are actually different, and utilized this for prioritizing measures. *For more details, please refer to the related links or feel free to contact us.