In an era where customer relationship management plays an important role in business success, firms endeavour to develop profitable associations with their existing and future consumer base. With customer-driven industries becoming highly competitive, there is a huge demand for efficient business intelligence (BI) approaches to facilitate high performance results while optimising on the costs invested, mainly in marketing activities. Therefore, researchers around the world are exploring BI techniques to maximise customer satisfaction and customer loyalty. Recently, machine learning models are being adopted to guide businesses for investing on new marketing strategies through intelligent mining of their existing customer data. This paper adopts a neural network approach to analyse and develop a model of business rules establishing relationships that can exist between customer demographics and the final purchase made by the customers. Then this model can be employed for the prediction of the purchasing patterns that might occur in the future. The dataset obtained from Kaggle's 'Allstate Purchase Prediction challenge' consisting of a car insurance customer base is employed for developing our prediction model. The experimental results confirm that the neural network approach adopted is able to extract the purchasing rules exhibited by the customers of a firm by discovering the customer behaviour patterns and their relationships with different demographic variables. An efficient prediction of the customer purchasing patterns would help organisations to make appropriate business proposals to their prospective customers in order to achieve higher customer satisfaction. © 2019 IEEE.