MetaM, a leading AICC company in South Korea (CEO Shin In-soo), announced on the 16th that it has designed the 2026 customer center operation strategy and detailed operation plan for KakaoStyle (CEO Seo Jeong-hoon) through a machine learning-based prediction model.

Based on years of accumulated contact center operation know-how and its own AICC technology, MetaM has derived a customized customer center operation strategy specialized for the fashion and distribution industries. This collaboration is significant in that it applies a machine learning-based call volume prediction model to reduce the uncertainty of counselor operations and establish a system that can proactively respond to external variables such as peak seasons or system overhauls.

Until now, call volume predictions relied on the experience and manual work of the person in charge. MetaM has introduced a machine learning-based prediction model to transform the system so that predictions can be made based on accurate data. The prediction model, which has learned various variable data, precisely calculated counseling demand by month, day, and time, and in tests using KakaoStyle's existing data, recorded an error rate of less than 6%, proving its accuracy. MetaM proposed appropriate staffing and system enhancement measures by reflecting counselor turnover rates, productivity, and differences by counseling type.

KakaoStyle reviewed the 2026 annual operation plan and peak season operation strategy based on MetaM's prediction results. The company examined the overall 2026 operation plan, including call response measures following system overhauls and peak season staffing plans, based on data derived through machine learning.

Jang Seung-woo, head of Partner Service Operations at KakaoStyle, stated, "Deploying staff just before the peak season can lead to decreased productivity," adding, "By accurately identifying counseling demand in advance through machine learning-based predictions and deploying staff at the right time, we can seek to improve customer center operation efficiency."

MetaM stated, "We do not simply provide AI models; we propose strategies optimized for the client's business environment based on nearly 20 years of accumulated contact center construction and operation know-how," adding, "This collaboration case is characterized by providing services that combine machine learning-based prediction models with counseling pattern analysis, staffing criteria, and operation simulations to be immediately applicable in the field."

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