Designing experiments with a large number of product attributes can be challenging. We propose a novel approach, Token-Based Conjoint (TBC), which reframes stated choice experiments to manage complexity more effectively. TBC is especially useful for products like subscription services with many binary features. By dynamically adjusting feature selection and incorporating a dual-response likelihood question, TBC delivers deeper insights into which feature combinations drive adoption, providing marketers with actionable results.
