Go to app
GuidesMarket researchAn introduction to conjoint analysis

An introduction to conjoint analysis

Last updated

1 April 2024

Reviewed by

Cathy Heath

Customers have different preferences that play a role in their purchase decisions. For businesses, meeting these different needs can be challenging. However, conjoint analysis can help make data-driven decisions that optimize products and services, making them more appealing to customers. 

Read on to learn more about the benefits of conjoint analysis and how it can help businesses make informed decisions about product development, pricing, and marketing strategies.

Market analysis template

Save time, highlight crucial insights, and drive strategic decision-making

Use template

What is conjoint analysis?

Conjoint analysis is a survey-based statistical analysis method to understand how customers value products and services and why they make certain choices when buying. 

A product or service comprises multiple conjoined attributes or features, and this is what conjoint analysis focuses on. A conjoint analysis breaks down a product or service into its attributes and tests the different components to reveal customer preferences. 

Why is it important for researchers?

Conjoint analysis is an essential component of market research because:

  • It helps measure the value the consumer places on each product attribute.

  • It predicts a combination of features that will have the most value to customers. 

  • It helps segment customers according to their perceived preferences. This helps with tailoring market campaigns to the right target customers. 

  • It enables researchers to get customer feedback about an upcoming product. 

Uses of conjoint analysis

Conjoint analysis is primarily used to make informed decisions relating to:

  • Buyer decisions

  • Customer preferences

  • Market sales

  • New product pricing

  • Selection of the best service or product feature

  • Market campaign validation

Why use conjoint analysis in surveys?

Conjoint analysis pinpoints what customers value the most, thus revealing their preferences, what they’re prepared to “trade off”, and why.  

Two types of conjoint analysis 

Two types of conjoint analysis are:

Discrete choice-based conjoint (CBC) analysis

CBC is the most common form of conjoint analysis that asks customers to mimic their buying habits. It asks respondents to choose between a set of product or service concepts. For instance, the choice-based conjoint analysis format presents questions such as "Would you rather?". 

The advantage of discrete choice-based conjoint is that it reflects a realistic scenario of choosing between products rather than directly questioning respondents about each attribute's significance. 

Adaptive conjoint analysis (ACA)

This flexible approach adopts a questionnaire procedure that tailors questions to address personal preferences. The adaptive conjoint analysis targets the respondent's most preferred attribute, thus making the analysis more efficient. 

When to use it? 

Businesses use conjoint analysis for the following:

Conjoint analysis in pricing

Businesses can use conjoint analysis to ask customers to compare different product features to determine how they value them. It’s an excellent way to learn what features customers are willing to pay for. 

When business owners fully understand what customers value, they can determine the price they’re willing to pay for their products or services. 

Conjoint analysis in sales & marketing

With conjoint analysis, businesses discover customer preferences, allowing them to create marketing campaigns that will target their preferences and increase sales. 

Also, findings of a conjoint analysis could help determine whether there’s enough market for a new product or service.

Conjoint analysis in research & development

With conjoint analysis, product developers can define customer needs and bring the right product or service idea to life. 

In addition, at the beginning of product development, a conjoint analysis will help reveal the concepts that aren’t valued by customers, allowing businesses to eliminate them at the early stages. This saves time and valuable resources and minimizes the risk of a failed product launch. 

How to do a conjoint analysis

The steps of performing a conjoint analysis are as follows:

Step 1: Define the study problem

Defining the problem establishes the purpose of the experiment. Whether you want to understand your customers better, find a perfect pricing strategy, or predict the market share, problem definition will define the scope of the study. 

In this step, the business owner must consider the target audience and craft specific, meaningful questions. 

Step 2: Break down the product or service into attributes

The next step is to determine the list of attributes of your product or service. Attributes should have varying levels in real life, be clearly defined, and be expected to influence customer preferences and exhibit strong correlations. 

For instance, if you sell cars, the attributes could be engine capacity, trim level, fuel efficiency, color, pricing, warranty, and design. Again, remember to use short descriptions to avoid misunderstandings. 

Step 3: Choose the conjoint analysis methodology

The next step is to organize the questionnaire according to the type of conjoint analysis preferred. 

Choosing CBC is effective when you want respondents to select a preference from a set of choices. ACA is appropriate when you want more accurate information on an individual level. 

Step 4: Deploy the questionnaires to your target respondents 

The questionnaire should have varying features so that the researcher can observe the attributes driving the choice. If the ACA method is used, ask the respondents to rank the attributes based on their needs. 

When the rankings are complete, the researchers get a clear picture of which feature(s) are highly rated by respondents and which aren’t.

Step 5: Data collection and analysis

This step involves collecting data accordingly and using it for decision-making. The rating given by respondents is a raw set of data. The business owner then assigns weights to each category. 

Finally, you can determine the attribute that ranks as the most important, and this will give you information about what customers value the most in your product or service. 

Five advantages of conjoint analysis

The advantages of using conjoint analysis include the following:

  1. Researchers can determine customer preferences at an individual level.

  2. It reveals the hidden drivers of why customers make certain choices.

  3. It’s a perfect tool for experimenting with attributes such as price before launching a new product or service. 

  4. Conjoint analysis is highly flexible and can be used to develop almost every product or service.

  5. It’s a versatile method that realistically reflects an everyday purchase decision.

Conjoint analysis examples

The following are two real-world examples of conjoint analysis: 

Example one: A manufacturer seeking to launch a new laptop

When launching a new laptop, manufacturers must know what customers value the most to ascertain what feature draws them to their offerings. Therefore, businesses must conduct a conjoint analysis. The manufacturer will develop a questionnaire that will gather insights from the respondents. 

The attributes that define the laptop are:

  • The operating system is either Microsoft Windows, Linux, or MacOS. 

  • The processing speeds

  • Storage space: is it a 500GB hard drive or 1TB?

  • Battery life

  • Price

  • Screen size

With the help of conjoint analysis, the manufacturer puts a value on each attribute and tailors the product to what’s valued most by a customer. Findings of customer preferences allow the manufacturer to design the "best" laptop technically possible.  

Example two: A restaurant owner seeking to attract a broad customer base 

The restaurant owner may want to differentiate themselves from the competition and attract a wider customer base. They will conduct a conjoint analysis based on what people value the most to understand customer choices. 

People go to restaurants for several reasons, including:

  • Ambiance

  • Quality of food

  • Meal purposes (business, tourist, family, etc.)

  • Low prices

  • Type of food served (seafood, Chinese food, etc.)

The restaurant owner will carry out a conjoint analysis based on the above criteria. The survey response will reveal what customers value the most and allow the restaurant owner to maximize the highly valued feature.


What is an attribute in conjoint analysis?

It’s a product characteristic such as price, size, brand, or color. 

What are attribute levels?

Attribute levels are the values that each characteristic can take. For instance, the attribute shape can have small, medium, large, or extra-large levels. 

How do you identify an attribute?

When defining an attribute, use a language that a customer understands. You can also use images to minimize confusion.

How many people do you need for conjoint analysis?

The sample size for a conjoint analysis depends on the target market. If the target market is relatively small, use a small sample size and vice versa. A general rule of thumb is to use sample sizes that range from 150 to 1,200 respondents. 

What are the real-life applications of conjoint analysis?

You can use conjoint analysis to test the appeal of new products such as soft drinks, footwear, or home appliances. 

How do you calculate market share in conjoint analysis?

You can determine market share by taking a business's sales over a period and dividing it by the industry's total revenue over the same period.

Should you be using a customer insights hub?

Do you want to discover previous research faster?

Do you share your research findings with others?

Do you analyze research data?

Start for free today, add your research, and get to key insights faster

Get Dovetail free

Editor’s picks

How to use psychographics to understand your audience

Last updated: 1 April 2024

An introduction to conjoint analysis

Last updated: 1 April 2024

What is a representative sample?

Last updated: 1 April 2024

Market intelligence: A quick guide

Last updated: 1 April 2024

Competitor analysis templates

Last updated: 13 May 2024

Buyer persona templates

Last updated: 13 May 2024

Market analysis templates

Last updated: 22 July 2023

Competitive pricing analysis 101

Last updated: 1 April 2024

Deep dive into target audience analysis

Last updated: 1 April 2024

Win-loss analysis template

Last updated: 13 May 2024

SWOT analysis template

Last updated: 13 May 2024

Focus group templates

Last updated: 13 May 2024

Latest articles

Competitor analysis templates

Last updated: 13 May 2024

Win-loss analysis template

Last updated: 13 May 2024

SWOT analysis template

Last updated: 13 May 2024

Focus group templates

Last updated: 13 May 2024

Buyer persona templates

Last updated: 13 May 2024

Deep dive into target audience analysis

Last updated: 1 April 2024

Market intelligence: A quick guide

Last updated: 1 April 2024

Competitive pricing analysis 101

Last updated: 1 April 2024

An introduction to conjoint analysis

Last updated: 1 April 2024

What is a representative sample?

Last updated: 1 April 2024

Market analysis templates

Last updated: 22 July 2023

Related topics

User experience (UX)Product developmentMarket researchPatient experienceCustomer researchSurveysResearch methodsEmployee experience

Decide what to build next

Decide what to build next

Get Dovetail free


OverviewAnalysisInsightsIntegrationsEnterpriseChannelsMagicPricingLog in


About us
© Dovetail Research Pty. Ltd.
TermsPrivacy Policy

Log in or sign up

Get started for free


By clicking “Continue with Google / Email” you agree to our User Terms of Service and Privacy Policy