End to End Intelligent Optimization


With the continuous evolution of network and the continuous development of business, the optimization of wireless network becomes more and more complex. This solution takes user experience as the core, end-to-end business data in many fields as the basis, expert experience and machine learning as the means, and four major functions of customer complaint handling, regional perception optimization, user perception optimization and business perception optimization as the starting point, so as to effectively assist operators in daily wireless network optimization.

Highlights
● Take user experience as the core: this solution starts with finding poor user experience and ends with optimizing and improving user experience to form a closed loop of finding, analyzing and solving problems.
● End to end multi domain integration: this solution relies on the business data of wireless network, bearer network, core network, Internet and other fields, and associates the data of different fields and levels in time, users, business and other dimensions, so as to analyze and solve problems more accurately and efficiently.
● Intelligent algorithm: take expert experience, machine learning and other intelligent algorithms as the core means, combined with end-to-end multi field data, to improve the analysis of wireless network problems to a new level.

Business Value
● Improve the efficiency of complaint handling. This solution provides the customer complaint handling function, completes the analysis of complaint work order by "one key out result", and supports the docking with operator work order system in online or offline ways.
● Save the cost of network optimization. Automatic analysis of all areas in the network, intelligent root searching for poor quality areas, and efficient improvement of network efficiency. Replace human road test with machine behavior to reduce the investment of human and material resources in operator network.
● Optimize user business experience. Through the all-round analysis of user perception, we can make precise efforts to effectively improve the reputation of operators.

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