What is conjoint analysis?

Introduction to conjoint analysis

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.

How does conjoint analysis work?

Step 1: Break products into attributes and levels

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. 

Step 2: Show product profiles to respondents

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.

Step 3: Quantify your market’s preferences and create a model

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.

Why use conjoint analysis?

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.

 

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What sets Sawtooth Software's Conjoint Analysis apart?

For over 30 years we’ve specialized in conjoint analysis software.  We’ve even been accused by academics and statisticians that we make conjoint analysis too easy!  Indeed, our web-based Discover platform makes conjoint analysis as simple as it can get.  But, if you need more features and power, we’ve got that too.  In spades.  Plus, flexibility to customize your conjoint experiment and analysis. 

Sawtooth Software offers more than just software platforms. You don’t have to go it alone. With decades of experience we’re here to answer your questions and help you on your journey to master the methods of conjoint analysis. Through friendly support, trainings, and our vibrant community we all want to help you find the right solution and succeed.  

 

What are the outputs of Conjoint Analysis?

The preference scores that result from a conjoint analysis are called utilities. The higher the utility, the higher the preference.  Although you could report utilities to others, they are not as easy to interpret as the results of market simulations that are market choices summing to 100%. 

Attribute importances are another traditional output from conjoint analysis.  Importances sum to 100% across attributes and reflect the relative impact each attribute has on product choices.  Attribute importances can be misleading in certain cases, however, because the range of levels you choose to include in the experiment have a strong effect on the resulting importance score. 

The key deliverable is the what-if market simulator.  This is a decision tool that lets you test thousands of different product formulations and pricing against competition and see what buyers will likely choose.  Make a change to your product or price and run the simulation again to see the effect on market choices.  You can use our market simulator application or our software can export your market simulator as an Excel sheet. 

 

How are outputs used? 

Companies use conjoint analysis tools to test improvements to their product, help them set profit-maximizing prices, and to guide their development of multiple product offerings to appeal to different market segments.  Because graphics may be used as attribute levels, CPG firms use conjoint analysis to help design product packaging, colors, and claims.  Economists use conjoint analysis for a variety of consumer decisions involving green energy choice, healthcare, or transportation.  The possibilities are endless. 

Varieties of conjoint analysis

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