The communication of sustainability values shared between product developers and customers is an important catalyst for effective collaboration that inspires sustainable consumption. Despite the many tools developed for assessing and communicating product’s sustainability performance, customers are facing difficulties in understanding product sustainability information. The knowledge gaps remain underexplored about how product sustainability information is perceived and how this impacts customer purchasing behaviour. This paper outlines a new approach, driven by both backcasting and forecasting thinking, for understanding and modelling customer preferences for product sustainability information. We report findings from a case study of a large workplace furniture manufacturer. The study explored the potential of i) identifying prioritised sustainability attributes using Sustainability Design Space (SDS), and ii) applying machine learning to model customer preferences.