1 week Ago By Kim Ji-won
On April 10, KT announced its initiative to minimize customer inconvenience through proactive quality innovation utilizing AI for its mobile network subscribers. The AI analyzes customer usage patterns and quality information to predict anomalies and take action accordingly. Initially, AI powered by deep learning and machine learning algorithms, including Convolutional Neural Networks (CNN) related to image processing, analyzes data associated with mobile service quality, such as response time and signal strength.
Based on this analysis, it proactively predicts any anomalies, relaying this information to customer service, who will then verify if customers experience any actual service interruptions. If any issues arise, specialized engineers are dispatched to inspect and, if necessary, replace relay equipment. A notable feature of this process is that it enables the identification of minor inconveniences or signal degradation due to unusual weather conditions—even before customers notice them—which facilitates quality improvement measures.
Customers can expect enhancements in perceived service quality as a result. By implementing quality checks before customers officially raise complaints, KT has managed to reduce the potential number of grievances by roughly 60%. To achieve this proactive quality innovation, KT utilizes AI models to analyze data generated daily from 13 million mobile customers, excluding personal information.
In the future, the company plans to continuously enhance its AI models using Microsoft's AX infrastructure. In the second half of the year, KT also intends to expand the scope of AI-driven proactive quality innovation to include wired customers, such as those using internet and IPTV services. O Tae-kyun, head of KT's Network Operation Innovation Division, stated, “Through AI-driven proactive quality innovation, we aim to reduce customer inconveniences and enhance satisfaction.
We will do our utmost to innovate customer experiences.”.
