10 Most Popular Conjoint Analysis Tools (Free vs Paid)
Conjoint analysis is a popular research method for understanding which attributes are most important to customers when purchasing a product. But even the most experienced researchers can struggle to run conjoint studies for several reasons:
Expensive: Conjoint analysis is always on the most expensive pricing tier of survey tools. It’s considered an advanced research method and prices go all the way up to $30,000/year for basic access to a conjoint analysis tool.
Support: Conjoint surveys are very complicated to set up correctly. Many providers of conjoint tools make the majority of their revenue from the support services customers end up having to buy (hiring an expert tends to be more expensive than the initial product cost).
Narrow: Conjoint is known as a tool for measuring customer preferences, however, conjoint analysis is only suitable for a handful of research scenarios. Many customers pay expensive fees for conjoint solutions but end up using other (cheaper) choice-based research methods that companies offer bundled together with expensive conjoint-focused products.
These limitations mean that picking the wrong conjoint tool can kill an entire research project. In this article, we’ve studied 10 of the most popular Conjoint Analysis tools to help you understand what each one is best suited for, how expensive they are, and how easy they are to get started on. Let’s jump in…
10 Most Popular Tools For Conjoint Analysis
10 Most Popular Tools for Discrete-Choice Modeling
The 10 research tools listed below all cover the most important aspects of discrete-choice modeling — ie. using choice-based questions to understand people’s preferences without directly asking them to state what their preferred options are.
Discrete-Choice Formats: Pairwise Comparison.
Free Plan: Yes — unlimited surveys/questions, unlimited collaborators, no time limit.
Price: Free tier available + paid tiers start from as little as $30/month.
OpinionX is a tool for creating comparison-based ranking surveys. Thousands of product and research teams from companies like Google, Amazon and Shopify use OpinionX to measure the preferences of their customers, colleagues or community members.
The most used research method on OpinionX is called Pairwise Comparison, which is a simple form of discrete-choice modeling and explains why pairwise comparison is sometimes known as “Conjoint-Lite”. While conjoint analysis shows multiple attribute levels simultaneously, pairwise comparison only shows options at a time in a head-to-head vote. It then measures and ranks the options based on the percentage of these pair votes each option wins.
Pairwise Comparison Voting (Left) and Results (Right) on OpinionX
Pairwise comparison has a bunch of advantages over conjoint analysis as a discrete-choice research format:
Flexible Context: Conjoint analysis can only be used to simulate purchase scenarios where a customer is comparing products based on tangible attributes. Pairwise comparison can be used for a much wider range of contexts like measuring the relative importance of a list of problems, value statements, personal opinions, or ideas.
Understandable Results: Conjoint analysis uses linear regression to calculate part-worth utilities of each attribute level, which is pretty complex. Pairwise comparison uses a simple formula (wins/total votes) to measure relative importance which is easy to explain to teammates while still being compatible with advanced analysis methods like respondent segmentation.
Lower Cognitive Load: Conjoint analysis shows respondents up to 6 profiles with 7 attribute levels in each, adding up to a total of 42 data points for respondents to consider for each vote they must cast. Pairwise comparison only shows two options at a time, which is much easier to complete.
Options Limit: Conjoint analysis limits you to 7 ‘levels’ per attribute. Pairwise comparison doesn’t have this same limit — in fact, I’ve seen pairwise surveys include over 300 options in one comparison set.
Relative Importance: Conjoint analysis and pairwise comparison both measure relative importance using choice-based methods that have been extensively researched in academic and commercial scenarios, so both are valid options to consider.
Low Cost: Pairwise comparison is available for free on OpinionX, while conjoint analysis features on other tools cost between $3,000 to $30,000 per year (as shown in the tool comparison section below). OpinionX lets you set up multiple pairwise comparison “blocks” in each survey and customize the number of pair votes per participant — all on the free tier!
So pairwise comparison is available for free, it’s easier to set up, easier to explain, and easier to complete as a participant. It’s built on the same principles of discrete-choice research as conjoint analysis, so it’s no less robust or reliable. And its flexibility means you can use pairwise comparison for conjoint-style surveys in scenarios outside of product purchase simulations.
Let’s look at some ways that teams use pairwise comparison today for ‘conjoint-lite’ research:
Use Case #1: Preference Testing
Humans don’t tend to be great at articulating what they like or dislike, but they’re good at showing it in their actions. Preference testing helps you figure out what people care about without asking them directly to tell you. Instead, by showing participants a series of options and asking them to choose the ones they feel strongest about, preference testing measures which problems, opinions, or ideas matter most to them. Here’s an example:
Back in March 2021, we launched an early version of OpinionX but couldn’t get a single paying customer. We had interviewed 150+ people but nothing seemed to be working. Before giving up entirely, we decided to run one last experiment — we wrote a list of 45 problem statements (including the key problem our product was solving) and asked a sample of target customers to each vote on 20 problem pairs.
In under two hours, we could see that our key problem statement was ranking dead last. But instead of quitting, we quickly pivoted to focus on the problems at the top of the ranked list. One week later, we had five paying customers! We realized at that moment that pairwise comparison was such a simple yet powerful way to scientifically measure people’s preferences.
Use Case #2: Assumption Testing
While preference testing was great for course-correcting, we started using a similar approach to test our assumptions before building big new features too. Here’s an example:
Shortly after our preference test pivot, users started asking us for a way to buy participants for their surveys. We discovered it would take us about 6 months to integrate an API provider, so we decided to run a quick assumption test first to see whether this was really the biggest activation problem for new users.
We turned our list of 22 feature requests into problem statements and sent them to a sample of users. Later that evening, we could see that problem of not having participants was ranked 11th on our list of problems.
We realized there were more important things for us to focus on, so instead of spending 6 months building the feature, I used 1 hour to write a blog post explaining how to use Prolific to recruit participants for OpinionX surveys and our engineers focused on solving more impactful problems instead.
Use Case #3: Roadmap Prioritization
The best product teams don’t build roadmaps full of feature ideas. Instead, they identify their overall objective, map out all the opportunities (customer needs, pains and desires) that could help them make progress towards their objective, and then they focus on the opportunity that will be most impactful. Only after identifying this top-priority customer need do they compare and contrast the possible solutions. This is how product teams at scaling startups like Gnosis Safe and Glofox use pairwise comparison to plan their roadmaps:
2. Send a pairwise comparison survey to a sample group of customers, which automatically measures the relative importance of each problem.
3. Filter the ranked results to see which problems are the highest priority to solve according to their most important customer segment. See how easy it is to do pairwise segmentation in this video:
Give pairwise comparison a try for yourself by creating a free research project on OpinionX in under 5 minutes (no credit card required).
Discrete-Choice Formats: Conjoint Analysis and MaxDiff Analysis.
Free Plan: Conjoint questions are not included in any free plan or trial.
Price: $30,000 for the Elite tier.
Pollfish has a pool of 250M respondents across 160+ countries, which allows researchers to quickly source participants for their surveys. Conjoint analysis is only available on the “Elite” plan of Pollfish, which starts at $30,000/year as an “upfront deposit”. Survey participants are billed at $0.95 plus incentives and recruitment fees. The Elite plan is targeted specifically at professional research clients and agencies. They have an “Elite SMB” version for small to medium-sized businesses that starts at $12,000.
Conclusion: Suitable for high-volume researchers that need access to a global recruitment panel 🟠
Discrete-Choice Formats: Conjoint Analysis and MaxDiff Analysis.
Free Plan: DesignXM free trial does not include Conjoint or MaxDiff questions ❌
Price: $13,000 ($5,000/year for one CoreXM user + $1/respondent + Conjoint add-on).
Qualtrics is an expensive solution for running conjoint analysis surveys. The minimum price annual price works out to roughly $13,000 for an annual single-user product license, participant fees (which you must pay even if you source participants yourself), and the add-on for conjoint questions. There’s also no way to try the product or get a pricing quote without engaging with salespeople that will push you into a video call and ask 20+ irrelevant questions before sharing any pricing information. I jumped through those hoops and I think you can still sense my frustration…
I got my hands on a demo version of the Qualtrics conjoint add-on and, as you can see in the screenshot above, it looks like a developer at Qualtrics threw together the survey design in just one day back in 2014 and hasn’t looked at it again since.
Conclusion: Avoid ❌
Discrete-Choice Formats: Seemingly none available despite what their website says.
Free Plan: No conjoint free plan or trial available ❌
Price: $5,000/year for a 2-seat license + Conjoint add-on.
Although SurveyMonkey claims to offer a conjoint analysis add-on for its Enterprise plan (which starts at $5,000/year for a 2-seat license), I could not find a single shred of evidence that it actually exists. Their support bot says it has no information about conjoint analysis and they have ignored all of my emails asking for more information.
Conclusion: I don’t think Conjoint Analysis actually exists as a question type on SurveyMonkey… ❌
Discrete-Choice Formats: Conjoint Analysis (CBC format only).
Free Plan: 30-day free trial.
Price: $23,800 per user (perpetual).
SPSS was first released in 1968 and was acquired by IBM in 2009. With 54,070 companies using SPSS, it is more than triple the customer base of survey juggernaut Qualtrics. Conjoint is only available on the most expensive tier of SPSS (named “Premium”), which costs $23,800 per user for a perpetual license.
Conclusion: Aimed at traditional large enterprises, not tech/startups ❌
Discrete-Choice Formats: Conjoint Analysis (many types) and MaxDiff Analysis.
Free Plan: Demo version available upon request.
Price: $11,990/year for their “Lighthouse Studio Advanced Suite” Windows desktop app or $6,600/user/year for the ‘À La Carte’ CBC-only version of their product.
Sawtooth Software was the pioneer of online conjoint analysis surveys and has been a major player in the online market research space for over 30 years. They have a range of expertise in traditional and digital conjoint research that is hard to compete with. If you plan on hiring their in-house experts alongside their product, you’ll be in good hands.
Conclusion: Particularly suited for traditional business categories like consumer goods or those planning to hire expertise alongside a research product 🟢
Discrete-Choice Formats: Conjoint, MaxDiff, A/B testing.
Free Plan: Yes but it doesn’t include Conjoint Analysis ❌
Price: $5,000/year for one seat and up to 5,000 responses.
Conjoint analysis is only available on the Enterprise version of QuestionPro, which is called their “Research Suite” offering. You pay an initial $5,000, which covers up to 5,000 responses over 12 months. Additional responses can be purchased for $5000 per 5k responses (bundled). QuestionPro’s product strategy is basically to offer every question format possible, so it can be a bit of an overwhelming user experience.
Conclusion: Expensive and complex for a self-serve offering 🟠
Discrete-Choice Formats: PAPRIKA-Based Adaptive Conjoint Analysis
Free Plan: Schedule a demo call or start a free trial.
Price: N/A — annual license calculated on a per-case basis and available only via sales call.
1000minds is a decision-making tool that was created in 2002. Its primary clients are academics and public sector bodies that require a bespoke decision-insights platform. 1000minds offers three main decision-making tools, one of which is their conjoint analysis solution. Their main differentiator is that their conjoint surveys are built on top of PAPRIKA, a patented method created by the 1000minds founders for ‘partial-profile’ conjoint surveys (ie. using a single-variable method like pairwise comparison or maxdiff analysis).
Conclusion: Suitable if you’re open to experimental / alternative formats 🟠
Discrete-Choice Formats: Choice-Based Conjoint Analysis.
Free Plan: You can run a “Market Test” conjoint survey for $289 with up to 20 responses.
Price: $1,795/year for one user or $7,995/year for a team account.
Conjointly is the market leader in conjoint analysis surveys. As the name suggests, they offer a range of conjoint solutions alongside some other techniques like Van Westendorp PSM and TURF Analysis. Additionally, Conjointly offers a team of in-house research experts that you can hire for an additional fee to help you design or analyze your conjoint project.
Conclusion: Currently the best self-serve solution for research that requires conjoint analysis 🟢
Discrete-Choice Formats: Conjoint Analysis (CBC format only).
Free Plan: 7-day free trial.
Price: $3,000 per user for the “Full Access” tier (billed as $275/month).
Alchemer positions itself in the survey market as a general-purpose, cost-effective SurveyMonkey alternative (specifically for on small teams) that’s less complex than Qualtrics — it doesn’t necessarily offer anything groundbreaking beyond that. Pricing plans are limited to 3 people max before per-user pricing kicks in, so a team of three billed monthly would end up paying $9,000 each year for access to conjoint questions on Alchemer’s “Full Access” tier.
Conclusion: Viable “no frills” / budget option for teams that dislike SurveyMonkey/Qualtrics 🟠
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Skip the thousand-dollar tools and start your conjoint research with free pairwise comparison surveys on OpinionX
With OpinionX, you can design, distribute and digest the results of your research without needing to hire an expensive conjoint expert. You can get data showing you what’s most important to your research participants in 2 hours instead of the 2 weeks a conjoint project will take.
OpinionX may be easy to set up but that doesn’t mean it’s a basic tool — it includes branching logic, segmentation filters to compare the priorities of participant subgroups, and real-time collaboration features so that you can invite your teammates to build your research project alongside you.
Learn more about how OpinionX can help you measure your customers’ preferences or jump right in and create a free pairwise comparison survey in minutes.
Further Reading About Conjoint Analysis:
• 8 alternatives to conjoint analysis for trade-off research
• 5 criteria for assessing whether conjoint analysis suits your research scenario
• 10 examples of research scenarios that should NOT use conjoint analysis
• 13 types of conjoint analysis explained (with picture examples)
• How to calculate conjoint analysis results in 8 Steps [Advanced]
About The Author:
Daniel Kyne is the Co-Founder of OpinionX, a free research tool for stack ranking people’s priorities — used by thousands of product teams to better understand what matters most to their customers. OpinionX has a bunch of free research methods for ranking people’s preferences — including Conjoint-style ranking methods like Pairwise Comparison and Constant Sum. Try it now!
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