Product and design analytics is emerging as a promising area for the analysis of large-scale data and reflection of the extracted knowledge for the design of optimal system. The Continuous Preference Trend Mining (CPTM) algorithm and a framework that are proposed in this study address some fundamental challenges in the context of product and design analytics. The first contribution is the development of a new predictive trend mining technique that captures a hidden trend of customer purchase patterns from large accumulated transactional data. Different from traditional, static data mining algorithms, the CPTM does not assume the stationarity, and dynamically extract valuable knowledge of customers over time. By generating trend embedded future data, the CPTM algorithm not only shows higher prediction accuracy in comparison with static models, but also provide essential properties that could not be achieved with a previous proposed model: avoiding an over-fitting problem, identifying performance information of constructed model, and allowing a numeric prediction. The second contribution is a predictive design methodology in the early design stage. The framework enables engineering designers to optimize product design over multiple life cycles while reflecting customer preferences and technological obsolescence using the CPTM algorithm. For illustration, the developed framework is applied to an example of tablet PC design in leasing market and the result shows that the selection of optimal design is achieved over multiple life cycles.