Predictive Personalization Of Conversational Customer Communications With Data Protection By Design

The goal of the research presented here is to support the development of a software platform allowing businesses to improve the way they communicate with consumers throughout the lifecycle of the customer relationship. The motivation is to make communication with consumers more personalized and relevant to each customer's interests, more direct and interactive, while also ensuring data privacy compliance by design. Personalization is made possible by developing predictive models based on a combination of data from past purchase transactions and past exchanges of messages between the business and the customer. This paper provides an overview of the capabilities of the system and the platform architecture that makes use of predictive analytics, data mining, and machine learning technologies.