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Conjoint Analysis Tutorial

Conjoint analysis is a powerful market research technique that helps marketers understand how consumers value different product or service attributes, such as features, functionality, and benefits, when making real-world decisions. Instead of evaluating attributes in isolation, conjoint analysis presents them in realistic combinations, mirroring how people actually compare product offerings in the marketplace. In this video, you’ll learn how conjoint analysis uses regression-based statistical methods with categorical (nominal) variables to measure the relative importance of each attribute. Even when data starts as ordinal or numerical, it must be converted into categorical variables and represented through dummy variables for proper analysis. We also explain why conjoint analysis often relies on primary research, where respondents are asked to rank or rate different product combinations. As the number of attributes and levels increases, the number of possible combinations grows exponentially, making surveys difficult for respondents. To solve this, researchers use an orthogonal conjoint design, which reduces the number of combinations while still preserving enough variation to accurately estimate attribute value. Whether you’re a marketing student, data analyst, or business professional, this video breaks down conjoint analysis in a clear and practical way. 🔍 Topics covered: 1. What is conjoint analysis? 2. Why conjoint analysis is better than simple attribute ratings 3. How regression is used in conjoint analysis