Conjoint Analysis in Market Research: A Step‑by‑Step Guide

Customers rarely choose a product because of one feature.

 

A smartphone buyer may care about price, battery life, camera quality, storage, and brand at the same time. A SaaS buyer may weigh monthly cost against integrations, user limits, support, and automation. A car buyer may trade range against price, charging time, and comfort.

 

This creates a challenge for conjoint analysis market research: customers can tell you what they like, but it is much harder to understand how they trade one benefit against another.

 

Conjoint analysis addresses that problem by asking people to make choices between realistic product alternatives.

 

Instead of asking, "How important is battery life?", researchers can show several product configurations and ask which one a customer would actually choose.

 

That difference makes conjoint analysis particularly useful for product design, pricing, segmentation, and market forecasting.

 

What Conjoint Analysis Actually Measures

 

Conjoint analysis is a research method used to understand how people value different attributes of a product or service.

 

A product is broken into several attributes, and each attribute has different possible levels.

 

For example, imagine a meal-delivery subscription with these attributes:

 

● Price: $20, $30, or $40 per month

● Delivery time: Same day, next day, or two days

● Meal options: 20, 50, or 100 choices

● Customization: Basic or advanced

 

Respondents then evaluate different combinations of these attributes.

 

The important part is that they cannot maximize everything at once.

 

A plan with faster delivery might cost more. A cheaper plan might offer fewer meal choices.

 

These trade-offs reveal what customers are actually willing to give up to obtain something else.

 

The Building Blocks: Attributes and Levels

 

Before creating a conjoint survey, researchers need to define the product carefully.

 

Attributes

 

Attributes are the characteristics customers use to evaluate alternatives.

 

For a laptop, attributes might include:

 

● Price

● Battery life

● Weight

● Storage

● Processor

 

For a hotel:

 

● Nightly price

● Location

● Room size

● Breakfast

● Cancellation policy

 

The attributes should represent factors that genuinely influence the purchase decision.

 

Levels

 

Levels are the specific options within each attribute.

 

For example:

 

Battery life:

 

● 8 hours

● 12 hours

● 18 hours

 

The levels need to be realistic and sufficiently different to create meaningful trade-offs.

 

If two levels are nearly identical, respondents may not distinguish between them. If one level is obviously superior in every way, the choice becomes too easy.

 

Why Choice-Based Conjoint Is So Popular

 

One of the most widely used approaches is choice-based conjoint, often abbreviated as CBC.

 

Instead of asking respondents to rate every attribute independently, CBC presents several hypothetical alternatives and asks them to choose one.

 

For example:

 

Which subscription would you choose?

 

Plan A

 

$20/month

 

20 meal options

 

Two-day delivery

 

Plan B

 

$30/month

 

50 meal options

 

Next-day delivery

 

Plan C

 

$40/month

 

100 meal options

 

Same-day delivery

 

None of these

 

The respondent chooses the option they prefer.

 

The survey then presents another combination.

 

Across many choice tasks, the researcher can estimate which attributes and levels are contributing most to the customer's preference.

 

This approach is powerful because it resembles an actual purchasing decision more closely than asking customers to rate isolated features.

 

Step 1: Define the Business Decision

 

Do not start by building the survey.

 

Start with the decision the research needs to support.

 

For example, a company might want to answer:

 

● Which product configuration should we launch?

● How much are customers willing to pay for faster delivery?

● Which features should be included in the premium plan?

● Which combination is most attractive to a target segment?

● Would customers choose our proposed product over competitors?

 

The research question determines the attributes, levels, and choice scenarios.

 

A conjoint study without a clear business decision can produce sophisticated statistics without producing a useful answer.

 

Step 2: Select the Right Attributes

 

Choose attributes that customers can reasonably consider when making the decision.

 

A common mistake is including every possible product feature.

 

That creates two problems.

 

First, respondents become overwhelmed.

 

Second, the analysis becomes harder to interpret because too many variables compete for attention.

 

A good attribute should generally be:

 

● Relevant to the purchase decision

● Understandable to respondents

● Capable of having multiple meaningful levels

● Something the company can actually change or control

 

For a pricing study, price should usually be included because the purpose is often to understand trade-offs between value and cost.

 

Step 3: Create Realistic Levels

 

Levels should reflect realistic market conditions rather than arbitrary numbers.

 

Suppose a streaming service currently charges $10, $15, and $20.

 

Testing $2, $50, and $100 may technically produce different preferences, but the results would not represent a realistic purchasing environment.

 

Researchers should also avoid creating levels that imply an unrealistic combination.

 

If an ultra-premium feature would never be available at the lowest price, the survey should not repeatedly present that combination unless the research specifically intends to test such a scenario.

 

Realism improves the usefulness of the final model.

 

Step 4: Build Choice-Based Conjoint Tasks

 

Now combine attributes and levels into product alternatives.

 

A respondent might see:

 

Which mobile plan would you choose?

 

Plan A

 

$25/month

 

30 GB data

 

5G included

 

International calls not included

 

Plan B

 

$35/month

 

50 GB data

 

5G included

 

International calls included

 

Plan C

 

$45/month

 

Unlimited data

 

5G included

 

International calls included

 

The respondent chooses one.

 

Then the survey generates another choice scenario with a different combination.

 

The goal is not to make every choice difficult. The goal is to collect enough varied trade-offs to estimate the relative value of the attributes.

 

Step 5: Include a "None" Option When Appropriate

 

In real purchasing situations, customers can decide not to buy.

 

A conjoint study should sometimes reflect that reality.

 

For example:

 

● Product A

● Product B

● Product C

● I would choose none of these

 

The "none" option can help researchers distinguish between choosing the best available product and actually having enough preference to make a purchase.

 

Whether it should be included depends on the research objective and study design.

 

Step 6: Collect Responses From the Right Audience

 

A sophisticated conjoint model cannot compensate for the wrong respondents.

 

If a company is launching enterprise software, surveying mostly students will not provide a reliable picture of enterprise purchasing preferences.

 

Define the target audience before fieldwork begins.

 

Depending on the research question, you may also want to compare groups such as:

 

● Existing customers vs. prospects

● Budget-conscious vs. premium customers

● New users vs. experienced users

● Different geographic markets

● Different usage frequencies

 

Segment-level analysis can reveal major differences in product preferences.

 

Step 7: Estimate Preference and Utility

 

After data collection, statistical models are used to estimate the relative utility associated with different attribute levels.

 

In simple terms, utility represents the value a respondent associates with a particular product configuration.

 

Suppose the analysis suggests:

 

● Lower price increases utility

● Faster delivery increases utility

● More customization increases utility

 

But the size of those effects differs.

 

The researcher can then estimate how customers trade these benefits against each other.

 

This is where conjoint analysis becomes more powerful than a simple ranking question.

 

A customer may say that both price and delivery speed are important. Conjoint analysis can help estimate how much additional value the customer places on faster delivery relative to a lower price.

 

Step 8: Calculate Willingness to Pay

 

One of the most commercially useful applications of conjoint research is estimating willingness to pay.

 

Suppose customers strongly prefer a premium feature.

 

The question is not simply whether they want it.

 

The more useful question is:

 

How much additional price can that feature support?

 

If the estimated utility of a feature can be compared with the utility associated with price changes, researchers can estimate the approximate monetary value customers assign to that feature.

 

This can inform:

 

● Pricing strategy

● Premium tiers

● Feature bundling

● Product packaging

● Promotional offers

 

It is important to remember that conjoint-derived willingness to pay is a model-based estimate, not a guarantee that customers will actually pay that amount in the market.

 

Step 9: Simulate Different Product Configurations

 

Once the model is built, researchers can move beyond individual feature preferences.

 

They can simulate potential products.

 

For example:

 

Product A

 

Lower price + basic features

 

Product B

 

Medium price + balanced feature set

 

Product C

 

Higher price + premium features

 

The model can estimate the relative preference or predicted choice share for each configuration.

 

Researchers can then change individual attributes and see how the simulated market response changes.

 

This makes conjoint analysis especially useful for market research because it connects survey responses to potential business scenarios.

 

Step 10: Turn the Results Into a Business Decision

 

The final report should not simply say:

 

"Customers prefer Feature X."

 

That is only the beginning.

 

A useful conclusion might instead say:

 

"Customers strongly value faster delivery, but the additional preference does not justify the current proposed price increase. A moderately faster service at a smaller premium may create a more attractive market position."

 

That is a decision.

 

The value of conjoint analysis comes from translating preference data into choices about products, pricing, positioning, and market strategy.

 

Common Conjoint Analysis Mistakes

Asking About Too Many Attributes

 

More variables do not automatically produce better research. Excessive choice complexity can increase respondent fatigue.

 

Using Unrealistic Product Combinations

 

If respondents see combinations that could never exist in the real market, the resulting model may have limited practical value.

 

Choosing Attributes Internally

 

Product teams sometimes select attributes based entirely on what engineers or executives consider important.

 

Customer research should validate whether those attributes actually matter to buyers.

 

Treating All Customers as One Market

 

Different segments can have dramatically different preferences.

 

A premium customer may value performance more than price, while another segment may behave in the opposite way.

 

Confusing Preference With Purchase Behavior

 

A conjoint model estimates choices under defined conditions. It does not perfectly reproduce every factor influencing a real purchase, such as brand trust, distribution, promotions, or availability.

 

Use the findings alongside behavioral and market data.

 

Conjoint Analysis vs. Other Prioritization Methods

 

Conjoint analysis is particularly useful when the research problem involves trade-offs between multiple product attributes.

 

MaxDiff is often better when you need to prioritize a list of individual features or benefits.

 

Kano analysis is useful when you want to understand how different features affect customer satisfaction.

 

Simple rating questions are useful for measuring attitudes toward individual attributes.

 

Conjoint analysis goes one step further by asking:

 

"When these attributes appear together, which combination would the customer choose?"

 

That makes it especially valuable for product configuration and pricing decisions.

 

Building Conjoint Surveys With SurveyMars

 

A conjoint study requires carefully structured choice tasks, clear attribute descriptions, and a survey experience that does not overwhelm respondents.

 

SurveyMars can support the research workflow with its intuitive survey editor and advanced question types designed for preference research. Teams can use AI-assisted survey creation to develop an initial questionnaire structure and refine product descriptions before launching the study.

 

For broader market research projects, flexible survey logic can help tailor follow-up questions to different respondents or research paths. Once responses are collected, AI-powered data analysis can help researchers explore response patterns and identify differences across customer groups.

 

For teams conducting product research regularly, this can make it easier to move from raw customer preferences toward more structured decision-making.

 

When Should You Use Conjoint Analysis?

 

Conjoint analysis is a strong choice when customers need to make trade-offs between multiple product characteristics.

 

It can be particularly useful for:

 

● New product development

● Product configuration

● Pricing research

● Feature bundling

● Competitive positioning

● Market segmentation

● Premium package design

 

It may be unnecessary when the decision involves only one simple attribute or when the product is so early-stage that researchers do not yet know which attributes customers consider relevant.

 

Final Thoughts

 

The strength of conjoint analysis in market research lies in its ability to recreate an important part of real-world decision-making: trade-offs.

 

Customers rarely evaluate price, features, convenience, quality, and service independently. They consider combinations.

 

Choice-based conjoint captures that behavior by presenting realistic alternatives and asking respondents to choose between them.

 

When designed carefully, the resulting data can help companies answer much more valuable questions than "Which feature do customers like?"

 

It can help determine which product configuration has the strongest appeal, what customers may be willing to pay, how segments differ, and how potential offerings could perform against one another.

 

The goal is not to make the research more complicated.

 

It is to make the decision more informed.

 

FAQs

 

1. What is conjoint analysis used for in market research?

 

Conjoint analysis is used to understand how customers value different product or service attributes and how they trade those attributes against factors such as price.

 

2. What is choice-based conjoint?

 

Choice-based conjoint presents respondents with multiple product or service alternatives and asks them to choose their preferred option. Repeated choices are then used to estimate relative preferences.

 

3. How many attributes should a conjoint study include?

 

There is no universal number. The study should include enough attributes to represent the business decision without making the choice tasks unnecessarily difficult or tiring.

 

4. Can conjoint analysis be used for pricing research?

 

Yes. When price is included as an attribute, conjoint analysis can help estimate how customers trade price against product benefits and can be used to model potential willingness to pay.

 

5. Can SurveyMars be used to create conjoint surveys?

 

Yes. SurveyMars provides advanced survey question types and tools for building product and market research surveys, including preference-focused research.

 

6. Can SurveyMars analyze conjoint survey results?

 

SurveyMars provides data analysis capabilities that can help researchers review collected responses and identify patterns. For advanced conjoint modeling, researchers may also use specialized statistical software depending on their specific needs.