Recommendations and personalisation: Three strategies for activating customer behaviour analytics insights

Abstract

This paper describes three approaches to personalisation and explains the methods for implementing each one. The reasons why many organisations have difficulty achieving effective personalisation are discussed, including lack of maturity in the required processes, architecture and data quality. Differences and similarities between personalisation and recommendations are reviewed, as well as the methods described for segmenting customers to present them with products and information tailored to their interests and needs. The paper identifies four dimensions that can be used for personalisation; preferences, location, topics and products/solutions. Methods required to carry out personalisation at scale are explained, and suggestions made regarding the development of the attributes needed for personalisation. The author highlights the importance of correctly modelling the customer journey in order to present the right information or products in response to signals from the customer.