By correlating the responses of surveyed subscribers with key quality indicators (KQI) observed and analyzed by CellMining’s Subscriber Network Analytics – such as low-quality VoLTE calls, slow video streaming, or frequent dropped connections when traveling – CellMining’s embedded machine learning model can actively predict the detractors from the entire subscriber base for which it has analyzed KQIs.
CellMining said this breakthrough gives the marketing team the power to identify both detractors for retention campaigns and promoters who can be nurtured. It also directly provides the network team with insights for automating network configuration changes, performance optimization, and enhanced prioritization for network tasks, based on subscriber data including NPS metrics.
“The impact of network quality on the brand NPS score is a major concern for network operators today, and a technology to predict detractors will be a game changer for them,” said Greg (Giora) Snipper, CEO of CellMining.
“CellMining’s Virtual Network NPS gives operators the power both to automate network performance optimization and to prioritize future investment based on NPS metrics, as well as the ability to use this insight to inform marketing campaigns and business decisions,” said Snipper.
Virtual Network NPS integrates with CellMining’s Network CEM Solution, which provides marketing and customer experience teams with market-wide customer satisfaction metrics that correlate subscriber experience data with parameters that include device type, cells, technology, service, and quality.