Existing mobile networks are so complex they could be treated as unique solutions created for each individual operator. If we take into account specific users' behaviour, inherited solutions through time and different views on future network and technology development, mobile operators are confronting not just the general problems known in mobile networks but also to some very specific issues.
In order to be able to address these operator specific issues technically, economically and in a systematic way, mobile operators have to deploy state of the art unique solutions to optimise their network performance and investments. To be efficient, solutions have to be customised to the network issues but they shouldn't be expensive to the operators and must prove effectiveness over prolonged periods of time (solution durability).
Cardinality’s goal is to be known for designing optimised solutions that provide mobile operators with the best cost/benefit ratio.
Problems are not customised to the solution - solutions are uniquely customised to the problem. This provides maximum benefits to the operator's network thus providing optimal performance to end users. Indirectly, it leads to improved customer experience and churn reduction.
Multiple use cases/application implemented in the Analytics Engine does not penalise the customer. Usually consulting service or network optimisation service systems charged per event/occasion. With Cardinality the solution can perform unlimited use cases/applications without additional charge, making the solution very cost effective.
New data driven challenges for the operator do not require a change of platform or a new platform. Bring the data in to your existing data cluster, analyse the problem and develop the solution that will use the network data and implement a software code that will have a solution mathematically described.
The operator pays only for solutions (use cases) for the issues and tasks that they want to address. There are no bulk solution packages with use cases that the operator will never use but got as there was a need for some of them or even for just one solution. In this way the operator will achieve the best cost/benefit ratio.
Automated solutions based on ML/AI and other classes of algorithms guarantee that implemented solutions will be flexible to be updated and up to date if solutions are to be deployed periodically. This will provide the operator with maximum performance with optimised costs.