To remain competitive and ensure revenue growth, CSPs clearly need to keep their customers happy. Traditional satisfaction management solutions however are based on ad-hoc surveys, usually with small sample sizes, making it difficult to find and address the root causes of any issues. To help them make the right decisions to increase end-user satisfaction, CSPs can now use a concept known as NPS (net promoter score) management. Through NPS management, potential problems and dissatisfaction among end users can be discovered and resolved in a timely manner, thereby improving customers’ experience of the network and potentially increasing their loyalty to the CSP. This Catalyst will develop an intelligent decision-making solution, underpinned by a digital twin and generative AI technologies, to effectively increase the number of satisfaction samples available for analysis. In this way, the solution will improve the efficiency of root cause analysis, help CSPs improve user satisfaction, reduce churn rates and gain competitive advantage. The solution will employ a real-time digital twin of the network, equipped with a high-performance data processing engine, which can check and correct data and user experience automatically, while maintaining stable and continuous data production. The Catalyst will also apply a large language model to improve CSPs’ assurance activities, and maintain the accuracy of real-time data and the stability of their data assets. The project team will apply TM Forum DT4DI best practices and standards to ensure the solution can scale and help CSPs implement it efficiently. A successful outcome will see CSPs improve satisfaction survey results; reduce network complaints, churn and costs; and increase brand value, operational efficiency and revenue.