Data-Driven Consumer Preference Prediction for Product Customization Using Machine Learning and Crowdsourcing

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University of Guelph

Product customization has become an increasingly popular paradigm driven by advances in manufacturing technology and demand for more personalized products. The customization process has many challenges and can be costly and complex for firms to implement. Decision support tools can help to guide firms through the customization process and offer objective recommendations on how customization strategies can be implemented. Through an extensive literary review, several shortfalls of current decision support methodologies have been found; namely that they lack comprehensiveness, structure, and automation. A data-driven approach to predicting consumer preferences for decision support is presented using 307 crowdsourced consumer preferences and a machine learning clustering model. Using a validation study, the model provides a 70% accuracy in the prediction of consumer preferences with opportunities for improvement with a larger data set. This method offers a novel approach to decision support for product customization that addresses shortfalls of other recommender systems.

Product Customization, Product Design, Consumer Preferences, Data-Driven Design, Machine Learning, ChatGPT, Mass Customization, Decision Support