Book Profile
A Practical Guide To Conjoint Analysis
A concise practical guide that explains how conjoint analysis infers the value consumers place on individual product attributes by analyzing their ratings of or choices among realistically described products.
Get the book →If you need to know what customers truly value in a product—and what tradeoffs they'll accept—this practical guide teaches you conjoint analysis, the marketing research technique that uncovers hidden attribute preferences without asking consumers to value features in isolation. Through a running sports-car example, it walks you step by step from constructing an experimental design of attributes and levels, through data collection with off-the-shelf software, to interpreting part-worth utilities. You'll learn three high-value applications: trade-off analysis (e.g., how much more you can charge if you add a sunroof), market-share forecasting using the multinomial logit model, and computing attribute importances as percentage decision weights. Written for managers and analysts, it demystifies the math just enough to make you a confident, accurate user of a powerful marketing decision aid.
What it argues
A Practical Guide To Conjoint Analysis
Key ideas it contributes
- Experimental Design Quality — The structure of attributes and levels chosen for the study, including how tangible and concrete the levels are, how many levels are tested, and whether ranges span realistic alternatives—shaping the validity of all downstream estimates.
- Data Collection Method — The procedure and instrument used to gather respondent ratings or choices among hypothetical product profiles, typically via PC or web-based conjoint survey software that generates profiles from the experimental design.
- Consumer Attribute Preferences — The underlying, often inarticulable, values consumers place on individual product attributes and levels, which conjoint analysis seeks to infer rather than elicit directly from respondents.
- Estimated Part-Worth Utilities — The numerical attribute-level utilities estimated from respondent ratings or choices, scaled within each attribute to sum to zero, representing average consumer preference for each level and serving as the core conjoint output.
- Trade-off Valuation — Computed estimates of how much consumers would give up on one attribute (e.g., price) to gain improvement in another (e.g., a sunroof), derived by additive utility arithmetic and interpolation across quantitative levels.
- Predicted Market Share — The forecasted share of choices a product will capture within a defined competitive set, computed by applying the multinomial logit model to the summed utilities of each competing product profile.
- Attribute Importance — The percentage decision weight assigned to an attribute, calculated as its within-attribute utility range divided by the sum of all attributes' ranges, indicating how much each attribute drives the overall choice process.