Background. Markets nowadays are increasingly competitive as many companies are focusing themselves on minimising their churn rate and maximising their growth rate of customers. Therefore, it is useful to understand the product preferences from the customers’ perspective which could help the companies to learn their products’ most attractive components. A similar approach can be used in terms of sustainability information, where sustainability in terms of products refers to the making of the products out of materials that are easily available and processed [42]. It might be the case that customers with limited knowledge are unaware of the product’s additional sustainability components and that this information would be interesting to them. Hence, the companies can expand their information or present it in a more easily perceiving form to increase customer’s awareness about the product’s sustainability attributes.

Objectives. Firstly perform a literature review to select the appropriate data mining (DM) technique required to build a preference model for existing customers. Next, experiment to compare the machine learning (ML) algorithms and choose the appropriate algorithm best suited for the current data to build a predictive preference model. Later, analyse whether the preference models built in this research project show consistent patterns in customer data.

Methods. To have a better understanding of the DM and ML techniques for modelling and predicting customer preferences, a literature review was conducted. With the use of an unsupervised learning approach, the different customers’ common behaviours and deviating behaviours were modelled. Additionally, analysing the models helped in understanding how customers behave concerning different products.

Results. The Literature Review indicated k-medoid as a suitable algorithm for clustering customers and the experimental results indicated that the K-Nearest Neighbours (KNN) is a better performing classification algorithm based on accuracy. The outcome from modelling the customer preferences towards sustainability are analysed. The results indicated that the data mining and machine learning techniques are capable of modelling the sustainability preferences of customers. Additionally, the comparison between the cluster solutions provided useful information about customer behaviour patterns.

Conclusions. The main goal of this study was achieved by proposing an approach that can model the common and deviating preference behaviours of the customers in terms of sustainability. This type of analysis is helpful for the companies in understanding their customers’ preferences towards sustainability which in turn helps them in building more sustainable products.