Conjoint analysis is the premier approach for optimizing product features and pricing. It mimics the tradeoffs people make in the real world when making choices. In conjoint analysis surveys you offer your respondents multiple alternatives with differing features and ask which they would choose.
With the resulting data, you can predict how people would react to any number of product designs and prices. Because of this, conjoint analysis is used as the advanced tool for testing multiple features at one time when A/B testing just doesn’t cut it.
In this conjoint analysis example, we'll break down the attributes of a car into brand, engine, type, and price. Each of those attributes will have different levels.
Rather than directly ask survey respondents what they prefer in a product, or what attributes they find most important, conjoint analysis employs the more realistic context of asking respondents to evaluate potential product profiles.
Each profile includes multiple conjoined product features (hence, conjoint analysis), such as price, size, and color, each with multiple levels, such as small, medium, and large.
In a conjoint exercise, respondents usually complete between 8 to 20 conjoint questions. The questions are designed carefully, using experimental design principles of independence and balance of the features.
By independently varying the features that are shown to the respondents and observing the responses to the product profiles, the analyst can statistically deduce what product features are most desired and which attributes have the most impact on choice.
In contrast to simpler survey research methods that directly ask respondents what they prefer or the importance of each attribute, these preferences are derived from these relatively realistic tradeoff situations.
The result is usually a full set of preference scores (often called part-worth utilities) for each attribute level included in the study.
When people face challenging tradeoffs, we learn what’s truly important to them. Conjoint analysis doesn’t allow people to say that everything is important, which can happen in typical rating scale questions, but rather forces them to choose between competing realistic options. By systematically varying product features and prices in a conjoint survey and recording how people choose, you gain information that far exceeds standard concept testing.
If you want to predict how people will react to new product formulations or prices, you cannot rely solely on existing sales data, social media content, qualitative inquiries, or expert opinion.
What-if market simulators are a key reason decision-makers embrace and continue to request conjoint analysis studies. With the model built from choices in the conjoint analysis, market simulators allow managers to test feature/pricing combinations in a simulated shopping/choice environment to predict how the market would react.
Just as a golfer doesn’t use the same club for every shot, the researcher picks the right tool for each project’s specific requirements.
CBC (Choice-Based Conjoint)—The most widely used conjoint tool for expertly handling a variety of problems in marketing and economics. The go-to approach for brand-package-price CPG studies.Learn More About CBC
ACBC (Adaptive Choice-Based Conjoint)—When the attribute list grows and for a more in-depth, customized, and engaging experience with the respondent.Learn More About ACBC
MBC (Menu-Based Choice)—Advanced analytical tool for multi-check menu choice experiments. Requires a high level of statistical expertise, but opens a world of opportunities for modeling complex consumer choices.Learn More About MBC