How to Run a Conjoint Analysis Survey for Pricing?

Pricing is one of the most sensitive and high-impact decisions a business can make. Set prices too high, and customers may churn before they ever convert. Set them too low, and you risk signaling low value while leaving revenue on the table. The real challenge is that customers rarely tell you—honestly and directly—how much they are willing to pay when asked outright.

This is where conjoint analysis research becomes especially valuable. Instead of asking customers what price they want, conjoint analysis simulates real decision-making scenarios to uncover how customers actually trade off price against product features. When designed correctly, it provides pricing teams, product managers, and marketing teams with a data-driven foundation for making confident pricing decisions.

In this article, we’ll walk through how to run a pricing-focused conjoint analysis study—from early planning and survey design to result analysis and decision execution—while also highlighting common pitfalls that can lead to misleading conclusions.

What Is Conjoint Analysis Research?

A conjoint analysis study is a research method that asks respondents to choose between or evaluate multiple product combinations in order to understand how much they value different attributes.

Unlike evaluating a single feature in isolation, respondents are shown complete, realistic product profiles that vary across multiple attributes, such as:

l Price

l Features or functionality

l Service levels

l Brand or delivery options

By analyzing respondents’ choices, conjoint analysis estimates:

l The relative importance of each attribute

l The utility (perceived value) of each attribute level

In pricing research, this approach is particularly effective because price is embedded within a realistic decision context—closely reflecting real-world purchasing behavior.

Why Use Conjoint Analysis for Pricing Decisions?

Traditional pricing research often suffers from bias. When customers are asked directly, “How much would you pay?”, the answers tend to be hypothetical or intentionally low.

Conjoint analysis avoids these issues by:

l Forcing realistic trade-offs between price and value

l Revealing willingness to pay indirectly

l Quantifying how customers value different features

l Identifying price sensitivity across different segments

As a result, conjoint analysis is especially well suited for:

l New product pricing

l Feature-based tiered pricing

l Bundle and package design

l Evaluating price increases

l Competitive positioning analysis

Step 1: Define Your Pricing Objective Clearly

Before designing the survey, it’s critical to clarify what decision the research is meant to support.

Common objectives include:

l Identifying optimal price points

l Testing pricing tiers or bundled offerings

l Understanding feature trade-offs at different price levels

l Comparing perceived value against competitors

A clear objective helps control the number of attributes and ensures that the results remain actionable rather than overwhelming.

Step 2: Select the Right Attributes and Levels

Attribute selection plays a decisive role in the quality of a conjoint analysis study.

Best Practices for Attribute Design

l Limit the study to 4–6 core attributes

l Ensure attributes are independent and non-overlapping

l Use realistic and credible attribute levels

l Treat price as a structured attribute, not an open-ended field

Example Pricing Attributes

l Price: $19 / $29 / $39

l Core Features: Basic / Advanced / Full

l Support Options: Email only / Live chat / Dedicated support

l Contract Term: Monthly / Annual

Avoid including attributes that customers may not understand or that the business cannot realistically deliver.

Step 3: Choose the Right Conjoint Analysis Method

There are several types of conjoint analysis, and the right choice depends on survey complexity, length, and respondent experience.

Common Conjoint Approaches

l Choice-Based Conjoint (CBC): Respondents choose one option from a set (the most common approach for pricing research)

l Adaptive Conjoint Analysis (ACA): Questions adapt dynamically based on earlier responses

l Full-Profile Conjoint: Respondents rate or rank complete product profiles

For pricing studies, choice-based conjoint is usually the preferred method because it closely mirrors real purchase decisions.

Step 4: Design a Respondent-Friendly Survey

Even the most sophisticated model will fail if respondents feel confused or fatigued.

Survey Design Tips

l Keep total completion time under 15 minutes

l Use clear and straightforward language for attributes

l Limit the number of choice tasks per respondent

l Randomize attribute order to reduce bias

Adding a short introduction at the beginning of the survey to explain the task can significantly improve response quality.

Step 5: Recruit the Right Sample

The reliability of a conjoint analysis depends heavily on the quality of the sample.

Key considerations include:

l Targeting actual or potential buyers

l Segmenting by industry, usage scenario, or demographics when needed

l Ensuring a sufficient sample size

As a general rule, the more attributes and levels included, the larger the sample size required.

Step 6: Analyze Results and Extract Pricing Insights

Once data collection is complete, conjoint analysis typically produces several key outputs:

l Attribute importance scores

l Utility values for each attribute level

l Price sensitivity curves

l Market share simulations

These insights enable teams to:

l Identify the most influential features

l Estimate customer willingness to pay

l Test alternative pricing scenarios

l Design more effective bundles or packages

The true value lies in translating these outputs into clear, actionable business decisions—not just charts and tables.

Common Pitfalls to Avoid

Even experienced teams can reduce the value of a conjoint study through poor execution, such as:

l Including too many attributes

l Using unrealistic price ranges

l Assigning too many choice tasks to respondents

l Ignoring qualitative follow-up questions

l Treating results as absolute answers rather than decision guidance

Conjoint analysis is a decision-support tool—not a crystal ball.

How Research Tools Support Conjoint Analysis Studies

Running a conjoint analysis study requires a high degree of flexibility in survey design and data handling. A capable feedback tool typically supports:

l Complex survey logic

l Randomized choice sets

l Custom attribute and level configuration

l Data export for deeper analysis

l Segmentation and filtering

When viewed as part of a broader customer research system, conjoint analysis delivers value far beyond a single pricing decision.

FAQ: Conjoint Analysis and SurveyMars

1.Can SurveyMars be used to run conjoint analysis studies?

Yes. SurveyMars supports flexible survey structures and can be configured to build choice questions aligned with conjoint analysis logic.

2. Does SurveyMars support price attribute testing?

Yes. Price can be set up as a multi-level attribute and analyzed alongside feature trade-offs.

3.Does SurveyMars support the complex logic required for conjoint analysis?

Yes. SurveyMars provides logic and randomization capabilities that help reduce bias and improve data quality.

4. Is SurveyMars suitable for both B2B and B2C pricing research?

Yes. SurveyMars can be used for conjoint studies across SaaS, service-based businesses, and consumer products.

5. Can SurveyMars data be exported for advanced analysis?

Yes. SurveyMars allows data export for further analysis using statistical or BI tools.

6.How does SurveyMars fit into broader pricing and VoC research?

Conjoint analysis can be combined with satisfaction and usage feedback surveys to support more comprehensive and informed pricing decisions.

Final Thoughts

Pricing should never rely on intuition alone. A well-designed conjoint analysis study provides a structured way to understand how customers truly balance price against features.

With clear objectives, thoughtful design, and the right research tools, conjoint analysis becomes one of the most reliable methods for pricing optimization—turning customer trade-offs into confident, data-backed business decisions.