Fujian MCC used artificial intelligence to identify potential dissatisfied users and quickly find and fix the root cause. The operator partnered with Nokia and used TM Forum’s Business Process Framework and Autonomous Networks Framework to guide the design and implementation of the strategy.
scores with core competitors
from 120 minutes to 10 minutes
and repeat visits by 90%
faster
sites across four zones in India
sites across four zones in India
sites across four zones in India
sites across four zones in India
Although Fujian MCC (FJMCC), a subsidiary of China Mobile, invested in a high-quality network and was spending significant resources on gathering regular feedback by telephone and door-to-door surveys, customer satisfaction for its home broadband was lagging behind that of competitors and was below China Mobile group’s average score.
The operator, which has 8 million broadband subscribers, needed to understand and close the gap.
It faced several challenges, including product diversity that increased operational complexity, a lack of coordinated optimization of operational mechanisms and processes, and multiple operations support systems that were difficult to unify.
Using TM Forum’s Business Process framework as a guide, FJMCC worked with Nokia to develop a transformation strategy.
This included a customer-oriented product operation system, a unified operations platform, and a new operations and maintenance model focused on customer perception, end-to-end quality management and optimized processes and standards.
The AI-driven Broadband User Perception Improvement solution, which is based on China Mobile’s big data and network AI platform, uses AI to correlate and analyze a wide range of network data, as well as trouble tickets and customer satisfaction survey data.
The fully automated solution identifies potential issues and causes of user dissatisfaction, analyzes the root cause, applies intelligent decision-making, and runs closed-loop evaluation for continuous improvement.
The system has been calculated to identify excessively low customer experiences with an accuracy of almost 86%.
New indicators based on service level agreements were added to avoid the issue of traditional network indicators being good but the actual customer experience being poor.
The solution has improved operational efficiency and reduced network operations and maintenance costs significantly. traditional network indicators being good but the actual customer experience being poor.
TM Forum’s Autonomous Networks Framework helped define the overall solution roadmap and functional architecture. Because the solution was designed using TM Forum’s Business Process Framework and is based on decoupled systems, open architecture, and open APIs, it was quickly deployed and integrated and is able to use multiple mature AI models.
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