A data-driven approach to predicting consumer preferences for product customization

Product customization is a complex task that relies heavily on consumer preferences. Eliciting these preferences can be challenging for firms looking to develop novel products and require significant investments of both time and effort. Prediction models can serve to replace traditional methods of understanding consumer preferences such as elicitation, focus groups or the designer's intuition, while speeding up the production cycle and saving cost. Current prediction models generally focus on one specific product type and require large amounts of data or historical product data. The idea of predicting consumer preferences for products based on the product type and its features using a clustering approach has not been explored in literature. This paper presents a proof-of -concept consumer preference prediction and decision support model based on a data-driven approach to design for product customization. First, consumer preference information is crowdsourced using surveys with 307 individual responses that are collected for a data set of thirty-seven training products and three validation products. Second, clustering techniques are assessed for user-generated clustering variables along with features that are extracted with artificial intelligence (ChatGPT). A threshold metric is proposed to evaluate the accuracy of different clustering algorithms. Third, a recommendation model is developed for customization decisions, and it is validated with three different products with an average accuracy of 70%. Areas for future work to improve the accuracy and expand the scope of the model are discussed including the use of a larger training data set, different machine learning approaches, and the improved use of ChatGPT.