PROGRESS

AutoSprice reached its objectives through the completion of the following tasks

Algorithmic Design for the a) predicting of usage and user profiles, b) finding a product and price proposal and c) proposing a price for the supply of prepaid service package. It was preceded by an extensive analysis of relevant academic literature and other research results where revenue optimization in telecommunications products was attempted with machine learning algorithms.

Measuring the attractiveness of scenarios for a customer

Implementation of processing flows in integrated end-to-end architectures with embedded algorithms on processing nodes. Big data technologies were leveraged for better and more reliable data processing with the application of machine learning at various processing stems. Implementation tasks delivered a. the processing flow for Post-paid telecommunication products and b. real-time processing pipeline to find the best prepaid package supply for pre-paid service buyers.

Deployment graph for the Data Pipeline processing Pre-paid Proposals in real time

Visualisation of results using a flexible GUI and dashboard technology.

Indicative visualization graphs of output analysis

Evaluation of the results and Exploitation Planning (quality and performance evaluation) in the context of Neurocom ’s activities. Analytical tests of processing flows were performed with appropriate methodology and with the use of synthetic data from data generators that were developed for the project needs. The prospects of exploitation in the wider market of data analytics and sales performance management applications in telecommunications were analyzed.

The global telecommunications data analytics market