What is Conjoint Analysis in market research?

Conjoint analysis is a quantitative research method used to understand how people evaluate and prioritise product or service features. Participants review sets of product profiles with different combinations of features and are asked to choose or rate their preferences. These choices reveal the relative importance of each feature and how people make trade-offs—insight that guides product development, pricing, and go-to-market decisions.

How Conjoint Analysis Works (with Example)

Conjoint analysis helps brands understand the trade-offs people make when choosing between products. Instead of asking standalone questions, it simulates real-world decision-making by presenting realistic combinations of features.

For example, a smartphone test might compare different combinations of price, storage, screen type, and camera resolution. Participants might be shown:

Option A: £350, 128GB, HD screen, 12MP camera
Option B: £500, 256GB, OLED screen, 24MP camera

By analysing thousands of choices, researchers can quantify the value placed on each feature and forecast which combinations are most likely to succeed—even if they weren’t shown in the test.

This approach uncovers how people weigh function against price in realistic buying scenarios—vital insight for product design, pricing, and marketing strategy.

Understanding the Terminology and Origins of Conjoint Analysis

What Is Conjoint Analysis Also Known As?

Conjoint analysis is sometimes referred to as trade-off analysis. Both terms describe the same technique, though “conjoint analysis” is more widely used in commercial settings. You may also come across the following variants:

Conjoint Study
Conjoint Measurement
Multi-Attribute Trade-Off Study
Conjoint Analysis Method / Technique
Conjoint Methodology
Conjoint Analysis Experiment

All these terms describe the same underlying method: a data-driven way to understand how people make trade-offs between features.

The History of Conjoint Analysis in Market Research

Conjoint analysis emerged from mathematical psychology in the 1960s. It entered commercial market research in the 1970s, led by Dr Fred McCollum, founder of Sawtooth Software, who helped apply it to consumer preference studies.

SSince its early adoption, conjoint analysis has evolved alongside advances in computing and analytics. It is now one of the most trusted methods for guiding product design, pricing strategy, and market positioning across industries.

A Quantitative, Statistical Approach

Conjoint analysis is a quantitative method that translates consumer preferences into numerical data for statistical analysis. Common techniques include:

Part-worth utilities – The core output of most conjoint studies, showing how much value consumers assign to each feature level.
Regression analysis – Identifies the relationship between product features and consumer preferences.
MANOVA (Multivariate Analysis of Variance) – Used to explore how preferences vary across segments or demographic groups.
Logit regression – Commonly used in choice-based conjoint to model binary decisions.
Conjoint simulation – Forecasts how people might respond to different product combinations, enabling scenario testing and market prediction.

Types of Conjoint Analysis

Different types of conjoint analysis serve different objectives, depending on the complexity of the product and the kind of decisions you’re trying to model. Each format offers unique strengths—and limitations. The three most widely used approaches are:

Ratings-Based Conjoint Analysis
Participants are shown individual product profiles and asked to rate each one using a numerical scale. While this method is straightforward to implement, it’s vulnerable to scale bias—participants may interpret rating scales inconsistently, making it harder to compare responses reliably across individuals.

Ranking-Based Conjoint Analysis
Respondents are asked to rank a series of product profiles in order of preference. This approach delivers a clear hierarchy of choices but offers limited insight into the degree of difference between them. It shows which options are preferred, but not by how much.

Choice-Based Conjoint Analysis (CBC)
The most widely used method today, CBC presents participants with sets of product profiles and asks them to choose the one they’re most likely to buy. It mirrors real-world decision-making more closely than ratings or rankings, capturing the trade-offs consumers actually make. Even product combinations not shown in the survey can be modelled using the resulting data.

Choice-Based Conjoint is particularly valuable because it supports predictive modelling. When set up correctly, it can simulate how consumers would react to new product configurations, helping brands make data-backed decisions about future offerings.

Choosing the Right Attributes and Levels

The strength of a conjoint analysis lies in the attributes you choose to test—those product or service features that truly influence customer decisions. Attributes might include price, performance, screen size, brand, or packaging, depending on your category.

To maintain clarity and statistical reliability, most studies focus on five or six high-impact attributes. Including too many can overwhelm respondents and muddy the data, limiting the usefulness of the results.

Each attribute must be broken down into levels—distinct and realistic variations that reflect the actual choices consumers face. For example:

Attribute: Price
Levels: £200, £350, £500
Attribute: Storage Capacity
Levels: 64GB, 128GB, 256GB

These levels should be spaced far enough apart to reveal meaningful trade-offs. Too-similar options reduce the ability to detect preference shifts.

When defining attributes and levels, consider the following:

Relevance to Business Decisions: Focus on the features you’re actively evaluating or could realistically change.
Avoiding Bias: Ensure the levels are plausible and balanced to prevent nudging responses in one direction.
Market Realism: Keep combinations grounded in what your audience might genuinely encounter.

A well-crafted attribute set creates the foundation for reliable modelling and insight. It enables researchers to simulate buying behaviour, assess product-market fit, and predict how consumers might respond to future offerings.

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Designing an Effective Conjoint Survey

Choosing the right attributes is only half the equation. To unlock the full value of conjoint analysis, the survey itself must be meticulously constructed—clear in purpose, intuitive to navigate, and capable of capturing meaningful data. Good design doesn’t just improve response rates; it enhances the depth and reliability of the insights you generate.

Start with the right respondents
A successful conjoint study begins with the right audience. Use screener questions to filter participants who reflect your target market—whether by age, location, income, purchase behaviour, or decision-making role. Including the wrong respondents risks distorting the data and undermining the study’s purpose.

Explain the task clearly
Conjoint surveys require mental effort. A concise, well-worded introduction sets expectations and improves respondent engagement. Participants should understand what they’ll be asked to do, how the choices work, and why their input matters. Clear instructions reduce abandonment rates and yield more thoughtful responses.

Keep the flow logical
Survey flow influences focus. Group related questions together, use consistent formatting, and avoid abrupt changes in layout or tone. A coherent structure helps respondents remain engaged, especially in longer studies that involve repeated comparison tasks.

Use realistic scenarios
Context improves response quality. Rather than abstract prompts like “Which would you choose?”, frame questions in practical, familiar settings. For example:

“You’re looking to upgrade your current phone. If these were your only options, which would you choose?”

Contextual framing mirrors real decision-making and yields more accurate reflections of consumer preference.

End with demographics
Leave demographic and profiling questions until the end. This keeps the main trade-off tasks uninterrupted, ensuring respondents focus fully on the core activity. Demographic data can then be used to segment findings, revealing preference differences across audience types and improving the study’s strategic impact.

Analysing Results and Turning Insight into Action

Once responses are collected, the real value of conjoint analysis comes into focus. This stage is where strategic insight is extracted from what appears to be simple choice data.

By applying statistical techniques, researchers calculate part-worth utilities—numerical values that quantify how much weight consumers place on each product feature or level. These scores uncover which features drive decision-making, and what trade-offs people are genuinely willing to make.

From this, brands can understand:

Which attributes have the greatest impact on consumer choice
What customers are willing to give up to gain a preferred feature
How preferences vary across different demographic or behavioural groups
Which product or service combinations are most likely to succeed commercially

Advanced methodologies also enable conjoint simulation, allowing brands to test product configurations that weren’t shown in the original survey. For example, if you’re developing a premium product with features still in concept phase, you can model its likely reception before it hits the market.

These insights directly shape:

Product development roadmaps, by highlighting the features that matter most
Pricing strategy, based on willingness to pay across segments
Marketing messaging, tailored to emphasise high-utility features
Investment decisions, supported by robust, data-backed projections

Where traditional research often reveals what people say, conjoint analysis gets closer to what they actually choose—especially when faced with real-world constraints. That distinction is what makes it a powerful tool for brands looking to build, refine, or reposition products with confidence.

Weighing the Pros and Cons of Conjoint Analysis

The true power of conjoint analysis lies in its ability to reveal not just what customers say they want, but how they make decisions when real trade-offs are involved. But like any research method, it comes with strengths and limitations.

Pros	Cons
Insights into consumer preferences – Helps identify what features customers value most and how they make trade-offs.	Limited feature sets – Only a small number of attributes can be tested at once, which may exclude niche or emerging features.
Realistic purchase scenarios – Mirrors real-world decision-making better than traditional surveys.	Response bias – Participants may still rely on brand familiarity or assumptions not presented in the test.
Scalable for large samples – Works well with large respondent groups and supports segmentation.	Complex analysis – Requires specialised statistical tools and expertise to interpret results effectively.
Cost-effective – Often cheaper than qualitative methods for testing feature combinations.	Limited real-world context – Does not fully replicate in-store, online, or social influences on behavior.

How to Run a Conjoint Study: Step-by-Step Workflow

Running a successful conjoint study requires careful planning and execution. From defining objectives to translating results into action, each step builds on the last. Here’s how the process typically unfolds:

Step 1 – Design and Development
Start by clarifying the business question. Then select a manageable set of attributes and levels that reflect real purchase decisions. Write clear survey instructions and program realistic product combinations that participants can evaluate.

Step 2 – Recruitment
Find participants who represent your target audience. Depending on your market, this might involve tapping into online panels, databases, or in-person intercepts.

Step 3 – Data Collection
Launch the survey and monitor progress to ensure high-quality responses. Timelines vary but typically range from a few days to several weeks.

Step 4 – Data Analysis
Apply statistical models to quantify how participants value each feature. This step produces part-worth utilities, identifies feature importance, and enables scenario testing for new product configurations.

Step 5 – Reporting and Action
Translate the data into commercial outcomes: pricing strategies, product bundles, go-to-market plans, and segmentation insights that support more confident decision-making.

Partnering with experts at this stage ensures the outputs are not only statistically sound but also strategically relevant.

Minimizing Bias in Conjoint Analysis

Even the best-designed conjoint study can be undermined by bias if not managed carefully. These steps help protect data integrity:

Use a representative sample to reflect your target population.
Randomize product profiles and feature order to avoid position effects.
Avoid leading or suggestive language that might skew choices.
Provide clear instructions so respondents fully understand the task.
Offer incentives to increase response rates and attention levels.
Conduct a pre-test to catch any confusing wording or design flaws.
Triangulate results with qualitative methods like interviews to validate findings.

Attention to these details ensures your conjoint results are a true reflection of customer preferences—not artifacts of survey design.

Industries That Commonly Use Conjoint Analysis

Conjoint analysis is especially valuable in markets where customers must weigh multiple competing features. Common use cases include:

Consumer Goods – To optimise packaging, product features, or flavour options.
Healthcare – To understand patient or provider preferences for treatment alternatives.
Financial Services – To test appetite for bundled products like credit cards or insurance plans.
Automotive – To prioritise features such as safety, performance, or technology.
Telecommunications – To design plan tiers, hardware options, and value-added services.

These sectors rely on conjoint to navigate complexity and make informed trade-offs in product development.

What Can Conjoint Analysis Help You Achieve?

Used correctly, conjoint analysis becomes a strategic asset. It provides insight that can drive decisions across your organisation:

Better Product Design – Identify which features matter most to your audience and build around them.
Stronger Pricing Strategy – Understand willingness to pay and adjust pricing to capture more value.
Deeper Customer Insight – Reveal how people really make decisions—not just what they claim to prefer.
Effective Segmentation – Uncover distinct groups with different trade-offs and tailor your strategy accordingly.
Higher Launch Success – Test concepts before they hit the market and prioritise those with the strongest appeal.
Confident Decision-Making – Replace guesswork with statistically grounded evidence.

How to Prioritise Product Attributes in Conjoint Studies

Deciding which features to include in a conjoint study is one of the most critical parts of the process. Overloading the survey with too many variables makes results less reliable—and the experience more fatiguing for respondents.

Start with Qualitative Discovery
Use internal workshops, focus groups, or early-stage interviews to identify the features that matter most. Align the findings with your business goals.

Keep It Manageable: 4 to 10 Attributes
The sweet spot for most studies is four to ten attributes. Fewer might miss key trade-offs; more can lead to poor data quality. For example:

A smartphone study may test six attributes like battery life, screen size, camera quality, brand, and price.
An automotive study might include ten features, such as safety systems, fuel efficiency, and design.

Evaluate Each Attribute Carefully
Only include features that:

Show clear variability across levels
Can be implemented or changed in your product roadmap
Are understood by your audience without ambiguity
Have real influence on decision-making

Pilot the Study First
Run a small-scale version to refine language, survey length, and level combinations. This ensures everything is clear before full launch.

Why Fewer Features Yield Better Trade-Off Data

At its core, conjoint analysis is a test of choices. The more attributes you include, the harder it becomes for participants to make realistic trade-offs. This complexity increases survey fatigue and can compromise the quality of your data.

For each chosen attribute, define clear levels that reflect real-world options. For instance:

Storage: 64GB, 128GB, 256GB
Price: $200, $350, $500

By simplifying the decision set, you force respondents to reveal what matters most. This leads to cleaner statistical models and more reliable insights.

The Conjoint Research Process: From Setup to Insight

Once the attributes and levels are defined, the study moves through a structured research pipeline. While timelines vary, the process typically follows this structure:

Step 1 – Study Design
Clarify the research question, select attributes, draft the survey script, and program the conjoint experiment.

Step 2 – Recruitment
Secure a sample that matches your customer base. Depending on geography and sample size, this may take several days or weeks.

Step 3 – Data Collection
Field the survey and monitor responses in real time to ensure quality and completeness.

Step 4 – Data Analysis
Use models such as part-worth utilities and segmentation to quantify trade-offs and predict market outcomes.

Step 5 – Reporting
Translate the findings into feature priorities, pricing strategies, product bundles, and strategic recommendations.

A rigorous process doesn’t just ensure statistical precision—it helps brands act confidently on the insights uncovered.

Why Work With a Market Research Agency?

While some brands run conjoint studies in-house, working with an experienced research partner like Kadence International brings distinct advantages:

Expert Design: We know how to craft meaningful trade-offs and avoid survey fatigue.
Advanced Modeling: From segmentation to simulations, we apply advanced techniques to extract deeper insights.
Objective Perspective: An external partner brings neutral interpretation—free from internal pressures or bias.
Resource Efficiency: We manage recruitment, fieldwork, and analysis so your team can stay focused on strategy.
Credibility and Quality: A third-party study often carries more weight with internal and external stakeholders.

Explore our conjoint analysis services or speak with us about tailoring a study for your product or market challenge.

Work with Experts to Maximise Your Impact

Conjoint analysis can unlock the features, pricing, and combinations that truly influence customer decisions—but only when executed with precision. From defining meaningful attributes to designing the right survey and applying advanced analytics, every step requires expertise.

At Kadence International, we’ve conducted conjoint studies across sectors including consumer goods, telecoms, healthcare, and financial services. Our team ensures your study is grounded in sound methodology, free from bias, and focused on outcomes that inform real-world decisions.

Whether you’re testing new product concepts, evaluating pricing strategies, or preparing for market expansion, we help you generate insights that lead to growth.

Explore our conjoint analysis services or get in touch to discuss your next project.