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Conjoint analysis is a market research and analytics category focused on measuring how people value different product or service attributes and the trade-offs they are willing to make. Books in this category span primers to advanced handbooks, covering survey and experimental design, discrete choice questionnaires, and utility estimation methods (e.g., part-worths, hierarchical Bayes, latent class). They explain core techniques such as choice-based and adaptive conjoint and show how to turn results into pricing, product configuration, feature prioritization, segmentation, willingness-to-pay estimates, and market share simulations. Emphasis is on practical application with case studies across consumer goods, technology, healthcare, travel, and B2B, along with guidance on software workflows (e.g., Sawtooth, R, Python) and common pitfalls. This category serves marketing researchers, product managers, data scientists, and students seeking rigorous preference modeling for decision-making.

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