Groundhog Boasts 3G/4G OSS Deal
CAMBRIDGE, Ma. -- A North American operator has selected and successfully deployed Groundhog Technologies’ CovMo geo-location on their LTE and 3G networks. This seamless inter-technology integration will allow the operator to efficiently monitor, optimize their networks, and ultimately reduce their overall churn. For this deployment, the operator was primarily concerned about network quality and roamer leakage.
The idea is to use CovMo to better understand their networks, and recognize which locations required immediate attention.
Historically the operator has lost substantial revenue streams to roamer leakage and churn. With CovMo geo-location, the operator can now see where the roaming boundaries are, and where their subscribers have switched to their competitor’s network. Once the identification of the boundaries are made, the operator can improve their own coverage issues at those locations; thereby reclaiming their lost subscribers and revenues.
Furthermore, the operator also has employed CovMo to identify indoor coverage holes that should be well within the operator’s network coverage areas. Since the majority of mobile users make their calls indoors, this is particularly important to the operator. With bad Quality of Service (QoS), subscribers that switch to competing operators are often hard and take substantial time to regain. With CovMo’s superior indoor resolution, the operator can quickly identify those trouble indoor areas and effectively combat churn. CovMo also allows the operator to combat churn proactively, by allowing the operator to know where the trouble areas are before the issues are even reported by the customers.
CovMo geo-locates network traffic and events to high resolution. Based on breakthrough research in Chaos Theory, CovMo applies a new way to pin-point network issues and various KPIs with high geographical resolution and accuracy, using big data available from the live network. CovMo is also the industry’s first geo location solution that can detect indoor-specific issues based on multi-dimensional modelling.