Collaborative R&D project funded by the Spanish organism CDTI with FEDER funds. This project is developed by the consortium formed by Galician companies Coremain, Quobis, R Cable and Optare Solutions as leader with the research support of the University of Vigo
BDA4T project will develop a prototype of an integrated Big Data Analytics platform for the telecommunications sector that will allow the use of the internal data of the operators to increase their customer satisfaction and revenues. BDA4T platform will retrieve the data needed from the OSS systems of the operator automatically and automate the analytics business actions also in the OSS systems. The project is based on the development of four main research lines:
- NoSQL storage systems: study of the NoSQL systems to be used efficiently to manage the internal business data of the operators.
- Advanced analytics platform: development of a platform to develop “Prescriptive Analytics” for telecom operators.
- Automation of telecom actions: development of the components that allow the operators to define automated actions based on the analytical results obtained.
- Analysis of value added BDA4T system: study of the improvement obtained by the operators with the results of this project in real cases of application.
Given its expertise in OSS systems and data management for the operators, Optare Solutions actively participates in the research of all these the lines of this project and leads the development of the following use cases:
- Estimation of the Customer Lifetime Value: this use case will estimate lifetime and future incomes for the operator’s customers to analyze their value. With these results, operators can prioritize the management of these clients to improve customer satisfaction and increase their revenue.
- Churn predictive modeling: with record numbers of portability month by month in Spain, Churn is one of the biggest problems faced by operators to improve their revenues. In this use case, different prediction models will be studied and developed to determine the probability of Churn for a customer according to their business profile. This Churn probability enables operators to focus their loyalty efforts in the most profitable and/or most likely to leave customers.
- QoE modeling: this use case will analyze the conversion between internally available QoS measures in the operators and the QoE perceived by the customers to assess customer satisfaction with the operator’s service.