Currently, network operators perform traditional network optimization. The goal thereof is to provide the highest possible service quality with the operator's available resources. In some cases, if subscribers complain about a low level of service quality, network optimization systems are capable of providing actions in a reactive manner.
Proactive optimization on a subscriber level is, however, generally not a capability of these traditional approaches. Among the reasons for this is a lack of analysis tools for service quality data on a subscriber level—if such data is at all acquired and long-term storage thereof is provided so as to have a sufficient data pool for long-term service quality analysis. In some cases, even though network operators acquire service quality data, they do not exploit them for network optimization but rather sell them to third parties that use the data for other purposes (e.g., marketing purposes).
Moreover, network operators do not know the actual service quality level demands of their subscribers. This is, however, essential for being able to meet those demands. As another issue, only low level network parameters are tuned in current network optimization processes. It is often unsure if this low level parameter tuning is efficient as regards a subscriber's perception of service quality.
In sum, for high customer satisfaction, it is crucial to achieve a good user experience. Current network optimization approaches do not guarantee that the actual subscriber experience attained is good compared to the subscriber experience possible with the available network resources.