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Introduction to conjoint analysis

Conjoint analysis is a category of research methods, among which choice-based conjoint is the most popular, that mimics the respondent’s real world tradeoffs when making decisions. It is used for pricing studies, product optimization, healthcare options and many other things. To get a more indepth understanding of conjoint analysis, refer to our page on conjoint analysis.

Learn about conjoint analysis

What is choice-based conjoint (CBC)?

When you want to understand and predict how people make choices when facing challenging tradeoffs, Choice-Based Conjoint (CBC) is the most widely-used survey-based approach. CBC also is known as discrete choice modeling (DCM) or discrete choice experiments (DCE).

How does choice-based conjoint work?

Choosing a preferred product from a group of products is a simple and natural task that everyone can understand. The difference between choice-based and the earliest approaches to conjoint analysis is that the respondent expresses preferences by choosing from sets of concepts, rather than rating or ranking them.

Choice task

Cbc Task

The combination of attribute (feature) levels we ask respondents to evaluate is critical to making conjoint analysis work properly. Fortunately, Sawtooth Software takes care of those details (though you may import your own designs if you wish).

Each level appears nearly an equal number of times and appears with levels from other attributes nearly an equal number of times. This makes for a fair and balanced (orthogonal) experiment where the utility value (the preference) of each attribute level can be measured independently and with high precision.

Build a preference model

Car Conjoint Example3

We can assess the relative impact of each attribute level on choice just by counting "wins." But, it’s more precise to fit models such as multinomial logit (MNL), latent class MNL, and hierarchical Bayesian (HB) estimation.

HB is the most popular approach and leads to a set of utility scores for the attribute list for each respondent. Latent class can find groups of respondents who are very similar to one another in their choice preferences, while being very different between the groups. Thus, latent class is an excellent approach to leverage CBC data for needs-based segmentation and strategy.

Want help with your first conjoint study?

Want in-depth help with your first project that goes beyond our free technical support? Our Sawtooth Analytics consulting team has deep expertise, decades of experience, and is ready to help.

Learn about our consulting team

Use Cases for CBC

Researchers often use CBC to study the relationship between price and demand, especially when the price-demand relationship can differ from brand to brand. One of the strengths of CBC is its ability to deal with interactions, such as between brand and price, or when different colors work better with different styles.

Choice-Based Conjoint (CBC) is used in marketing and economics applications across a variety of cases and industries, including:

  • New product design, existing product redesign or line extension
  • Pricing studies
  • Market segmentation
  • Healthcare choices, including cancer regimens
  • E-commerce
  • Employee research (health plans, benefits)
  • Transportation choice
  • Green energy, electric vehicles, ride sharing
  • Public health
  • Education
  • Environmental impact
  • Finance, banking
  • Technology and innovation

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Lighthouse Studio

For developing web-based, CAPI (mobile devices not connected to the web), or paper-and-pencil (3rd party platform) CBC studies. This is installed, Windows-based software that seamlessly integrates with our web-based services for free survey hosting.

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Our streamlined web-based platform. Entry-level CBC capabilities in an intuitive, easy to use tool. Low cost entry point and you can import your projects to Lighthouse Studio if you ever need more advanced capabilities.

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