A combination and correlation of the network-related and the customer-related viewpoints are done on very limited scale. The enormous number of data sources and the information from them, the difficulty in comparing the information, and the complexity of the analyses require a tremendous effort. ­Moreover, separate analyses have always been adequate in the past for the operation and planning of the mobile network. Network operation thereby uses measurements of the network ­elements. Determination of the long-term sales potential is ­based on ­customer-related data which are independent of network elements and do not allow information about the spatial distribution of the traffic sources.

Furthermore, a forecast of the spatial traffic distribution is ­required even for long-term network expansion planning. In particular, the estimation of market potential typically depends on data such as turnover, customer segments, service utilization, demographics, purchasing power, household size, ­penetration. These data are assigned to the billing address and provide an exact projection of market potential. However, the spatial ­distribution of the utilization per service cannot be assessed with the help of these data because customers typically use the services while on the go and not at the location of their billing address.

On the other hand measurements in the network provide information on the spatial distribution of the service demands and the corresponding growth, but do not allow conclusions about the billing address and thus about the customers.

If both of these data sources are correlated, a comprehensive view about the spatial service distribution and their ­development per individual customer groups can be obtained.

Correlation of all data sources can result in optimization of service and technology strategy

The common consideration of all data sources therefore is a ­powerful tool to achieve further optimization of the service and technology strategy which goes far beyond pure ­technical ­monitoring and quality improvement in the network and ­analysis of the service utilization. If it is successful to correlate all of these data and to link them temporally and spatially, network operators acquire an efficient tool which enables them to better align the network evolution to the customer demand and to offer new and innovative services. For example, by means of so-called “location-based services”, location based  information can be transmitted to a customer that is related to his needs and to his current position. In such cases, the mobile ­provides important orientation aid and gives directions, if needed, to the nearest restaurant or enables contact to social networks in the ­vicinity. Furthermore, the precise knowledge about the ­temporal and spatial usage of services helps to develop customer segment specific tariff plans which are restricted in terms of time and location.and to utilize the existing network capacity more efficiently.