Using conjoint analysis across the marketing value chain

Marco Vriens and Felix Eggers
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Marco Vriens: Kwantum, Sausalito, USA
Felix Eggers: Copenhagen Business School, Denmark

Applied Marketing Analytics: The Peer-Reviewed Journal, 2025, vol. 10, issue 4, 336-347

Abstract: Conjoint analysis, best known in its choice-based form, has become one of the most commonly applied techniques in marketing research since it was introduced in the early 1970s. Its popularity stems from its ability to provide a systematic, experimental framework for collecting and analysing data on how product attributes and their levels influence consumer preferences and decision making. By simulating real-world trade-offs, conjoint analysis generates managerial insights such as the relative importance of product features, consumers’ willingness to pay and predicted market shares, making it an essential tool for product design, pricing strategies and competitive positioning. Methodological advances have enabled researchers to apply conjoint across the entire marketing value chain, as well as to deal with the associated challenges, such as how to deal with (too) many attributes, which type of experimental design to use, how to minimise hypothetical bias, whether to include benefits or other meta-attributes, how to account for non-compensatory decision making, how to account for consumer budgets, etc. This paper discusses the challenges encountered when applying conjoint across the marketing value chain, and the methods best suited to manage these challenges. In this way, the paper provides a concise user’s guide to making good methodological choices without getting drowned in the vast literature on this topic.

Keywords: marketing; marketing analytics; market research; conjoint; generative AI; GenAI; AI; large language models; LLMs (search for similar items in EconPapers)
JEL-codes: M3 (search for similar items in EconPapers)
Date: 2025
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