8 Alternatives To Conjoint Analysis For User Research
“The eight most useful conjoint analysis alternatives are pairwise comparison, points allocation, ranked choice voting, MaxDiff analysis, agreement voting, the Kano Model, Van Westendorp PSM and TURF analysis. The first four force a trade-off the way conjoint does. The other four measure something else.”
Conjoint analysis earns its complexity when you're modelling market share, calculating willingness to pay for attributes you already know about, or testing a bundling strategy. For mapping customer needs, prioritising feature ideas or researching a purchase with several decision-makers, a simpler method gets you a better answer faster.
Pairwise comparison is the closest substitute for most teams. MaxDiff collects more data per vote at a higher cognitive cost. Van Westendorp is the one to reach for when you have no price benchmark at all. Seven of the eight run free; TURF is the exception.
When should you use conjoint analysis alternatives instead?
Conjoint analysis is a method for working out which attributes drive purchase decisions, and it's rightly loved by pricing specialists, product marketers and market researchers.
It's also one of the most misused methods in research. Because it's filed as "advanced", it intimidates people who aren't specialist quantitative researchers, and several platforms make good money from that: the licence is the small part, and the consultants who help you design and analyse the study are the rest.
There are free conjoint tools that skip the paid-consultant step. But plenty of teams looking at conjoint don't need conjoint at all. They need something simpler that answers their actual question.
I've written a full piece on the ten scenarios where you should not use conjoint analysis. The short version is that conjoint only works when your research meets every one of these conditions:
You're simulating a purchase between similar products.
One person makes the purchase decision.
That person already knew what kind of product they needed.
You already know which attributes they use to compare products.
Those attributes don't overlap in meaning or possible options.
Miss any of those and the results will look convincing and mean nothing, which is worse than having no data.
Conjoint is best suited to modelling market share scenarios, calculating willingness to pay for existing attributes, or informing a bundling strategy. If you're mapping customer needs, prioritising feature ideas, or researching a complex business purchase with several stakeholders, one of the conjoint analysis alternatives below will serve you better.
↑ interactive example of a conjoint analysis survey (hosted on OpinionX)
What is choice-based research, and why does it matter here?
Conjoint analysis is a choice-based method. It gives people a series of options and works out what they care about from which options they pick, rather than from what they say they care about.
Several other methods follow the same principle, and that's what makes them viable conjoint analysis alternatives rather than different things entirely. Choice-based methods imitate real life, where options are limited and you have to trade one thing off against another.
Of the eight covered here, the first four work this way and the last four don't, which is the distinction to hold onto: a method that never forces a trade-off can't tell you what someone would give up.
The 8 conjoint analysis alternatives at a glance
| Method | Choice-based? | Use it when | Main limitation |
|---|---|---|---|
| Pairwise comparison | 🟢 Yes | Ranking any list, short or long, with minimal setup | No separate attributes and levels |
| Points allocation | 🟢 Yes | You need magnitude, not just order | Leaves gaps where people spend nothing |
| Ranked choice voting | 🟢 Yes | Six to ten options and everyone sees the full list | Data quality drops sharply past ten options |
| MaxDiff analysis | 🟢 Yes | Long lists, and you want more data per vote | Higher cognitive load than pairwise |
| Agreement voting | 🔴 No | Consensus matters more than preference | No trade-off, so it doesn't model real behaviour |
| Kano Model | 🔴 No | Sorting features into basic, performance and delight | A rating scale in disguise, with the same problems |
| Van Westendorp PSM | 🔴 No | You have no price benchmark at all | Pricing only, and it gives a range not a number |
| TURF analysis | 🔴 No | Picking a bundle that reaches the most people | Analysis only, so it needs another method to feed it |
1. Pairwise comparison
Explanation: Pairwise comparison is a simple, flexible version of conjoint. Instead of showing people profiles made up of several attributes, it shows two options at a time and scores which get picked most often. The underlying maths goes back to paired comparison work in psychophysics and is far easier to explain to a stakeholder than a conjoint utility.
Advantages: Simple to set up yourself, low cognitive load for participants, handles short or long lists, and the scoring is easy to follow. You can check the workings in our write-up of how the ranking formula works.
Disadvantages: Lacks some of conjoint's sophistication, with no separate attributes and levels. Can need a lot of voting if you have many options and few participants.
Suggested tool: Pair Rank on OpinionX. Free tier is $0 with unlimited surveys and a cap of 25 participants per survey.
2. Points allocation
Explanation: Points allocation gives participants a pool of credits to spend across a set of options. How they spend tells you which options matter and how much more they matter, which is the thing a ranking can't give you.
Advantages: It measures magnitude rather than order alone: someone doesn't just prefer apples to pears, they put nine of their ten points on apples. That makes it more flexible than conjoint for testing willingness to pay for specific functionality or benefits.
Disadvantages: People often feel strongly enough about a few options that they allocate nothing to the rest. That's a real signal, but it leaves gaps in your data, especially at small sample sizes.
Suggested tool: Points Rank on OpinionX, on the same free tier. There's also a Google Forms workaround if you want to try the format before committing to anything.
3. Ranked choice voting
Explanation: Ranked choice voting shows participants the full list and asks them to put it in their own order of preference. It's the same family of methods used in ranked voting systems in elections, which is why most people already understand it without instruction.
Advantages: Well known, easy to set up, and very simple for participants.
Disadvantages: Use six to ten options at most. Past that, data quality falls away sharply, and you should switch to pairwise comparison or MaxDiff. Ranking is also awkward on touchscreens, though most tools have adapted: OpinionX lets people rank by tapping, dragging or using arrows to reorder.
Suggested tool: Order Rank on OpinionX, which works on any screen size.
4. MaxDiff analysis
Explanation: MaxDiff analysis, also called best-worst scaling, shows participants a subset of options, usually four to seven, and asks them to pick the best and the worst. Then the set resets with a new subset.
Advantages: By showing four to seven options at a time it collects more data per vote than pairwise comparison, and it's much less rigid to set up than conjoint.
Disadvantages: Cognitive load is several times higher than pairwise comparison, because each vote asks for two considered judgments across a bigger set. The trade is speed of collection against effort from the participant. MaxDiff is also priced like conjoint on most platforms, which is the reason many teams never get to run one.
Suggested tool: Best/Worst Rank on OpinionX, which runs on the free tier with every analysis feature unlocked. When I tested nine MaxDiff platforms, it was the only one where a complete MaxDiff survey runs free; Sawtooth has since added a free tier capped at 50 participants, which is worth knowing if you want to compare.
5. Agreement voting
Explanation: Agreement voting measures which option has the highest level of consensus across a group, rather than which has the most enthusiasm.
Advantages: In collaborative planning, education and conflict resolution, finding the option everyone can live with matters more than finding the divisive option with the most votes. It's quick and easy to understand for researchers and participants alike.
Disadvantages: There's no forced comparison and no trade-off, so it doesn't simulate how people behave when they can't have everything. Simple aggregate voting shouldn't be your method in most research scenarios, and I'd rather you knew that before running one.
Suggested tool: Agreement Rank on OpinionX, on the free tier alongside the other ranking formats.
6. Kano Model
Explanation: The Kano Model shows a participant a statement, usually about a feature, and asks them to pick one of five responses: I like it, I expect it, I am neutral, I can tolerate it, I dislike it.
Advantages: It separates features that are expected (basic) from those that drive satisfaction (performance) and those that create delight (excitement). That framing is genuinely useful for thinking about a roadmap.
Disadvantages: In my opinion Kano is not a good approach to trade-off analysis or to understanding what matters most. It needs participants to understand precisely what value each feature delivers them, which they rarely do. It's also a rating scale wearing a costume, with the same well-documented problems: people can give everything the same score, and nothing forces a comparison. Central tendency bias does the rest.
Suggested tool: Any survey tool with a multiple-choice or matrix grid question, OpinionX included. Of all the conjoint analysis alternatives here, this is the one I'd think hardest about before running.
7. Van Westendorp PSM
Explanation: Conjoint is famous for pricing research, but it only works if you already have two to seven candidate price points. For a new product in an emerging category, you often have no benchmark at all. The Van Westendorp Price Sensitivity Meter fixes that with four open questions that give you a starting range. Our full guide to Van Westendorp walks through the setup and the chart.
Advantages: The fastest way to find a rough price range for something new. Simple to set up and simple to read.
Disadvantages: Pricing only, so it's narrow. It also gives you a range to test rather than a price to charge, and the range still needs validating.
Suggested tool: You can write the four questions into any survey tool and plot the chart yourself in a spreadsheet, though there's little reason to now. OpinionX has a purpose-built Van Westendorp question type with the four questions prefilled, automated charts, and results that feed straight into segmentation. It's on the free tier with no trial, sales call or card required. Gabor Granger shipped a fortnight later and is the method to use once you do have a price range and want the revenue-maximising point inside it.
8. TURF analysis
Explanation: TURF analysis isn't a data collection method like the others. It's an analysis technique that takes ranked data and finds the combination of options with the highest combined reach: which two options score well while appealing to different people.
Advantages: Mostly used for bundling strategy, where it's genuinely useful for choosing a product or attribute mix.
Disadvantages: Because it isn't a collection method, TURF needs pairwise comparison, MaxDiff or conjoint to establish relative importance first. It's also only useful when you're looking for the lowest input that maximises reach, which isn't a common requirement.
Suggested tool: This is the one method on the list OpinionX doesn't do, so I'll point you elsewhere. Sawtooth Software and QuestionPro both offer TURF, though neither publishes a price for the tier that includes it any more, so you'll be requesting a quote. Since June 2026, SurveyMonkey ships automated TURF alongside its MaxDiff study, which is the cheapest route in if you only need it once. Sawtooth's technical paper library is the best free reading on TURF design regardless of which tool you use.
Image Source: Quantilope
How do you pick the right choice-based research method?
Do you need a trade-off? If the answer you want is "what would they give up", you need a choice-based method: pairwise comparison, points allocation, ranked choice voting, MaxDiff or conjoint. If you need consensus or a feature classification, agreement voting or Kano will do.
How long is your list? Under ten options, ranked choice voting is fine and easiest for participants. Over ten, use pairwise comparison or MaxDiff. Over a hundred, pairwise comparison.
Do you already know the attributes? Conjoint requires that you do. If you're still working out which attributes matter, you're at an earlier stage of research, and the Discovery Sandwich covers how to get there. Most of the conjoint analysis alternatives above work fine at that stage; conjoint itself doesn't.
Where conjoint genuinely fits, it's hard to beat and you should use it. But most projects people consider conjoint for weren't built for conjoint, and one of these conjoint analysis alternatives will be more flexible, easier to run and considerably cheaper.
Frequently asked questions
What is the best alternative to conjoint analysis? Pairwise comparison, for most teams. It forces the same kind of trade-off, handles lists of any length, takes minutes to set up, and the scoring is simple enough to explain to a stakeholder. MaxDiff is the better choice if you need more data per vote and your participants can handle the extra effort.
Is MaxDiff better than conjoint analysis? They answer different questions. MaxDiff ranks a list of individual options by relative importance. Conjoint measures how people weigh attributes against each other inside a whole product. If you're asking which features matter most, that's MaxDiff. If you're asking what someone would pay for a specific configuration, that's conjoint.
What is a choice-based research method? A method that gives people options and infers what they value from which they pick, rather than asking them to rate or score. Conjoint analysis, pairwise comparison, points allocation, ranked choice voting and MaxDiff are all choice-based. Rating scales and the Kano Model are not.
Can you run conjoint analysis alternatives for free? Yes, seven of the eight. Pairwise comparison, points allocation, ranked choice voting, agreement voting, MaxDiff and Van Westendorp all run on the OpinionX free tier at $0, with unlimited surveys and a cap of 25 participants per survey, and Kano needs nothing more than a multiple-choice question. TURF analysis is the exception, and it needs a paid tool.
When should you not use conjoint analysis? When more than one person makes the purchase decision, when the customer doesn't yet know what kind of product they need, when you don't yet know which attributes they compare on, or when those attributes overlap in meaning. Any of those breaks the model.
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Further reading on conjoint analysis
Every method above except TURF runs on OpinionX's free tier: $0, unlimited surveys, unlimited researcher seats, 25 participants per survey, then $900 a year to lift the cap (full pricing).
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 free Conjoint Analysis surveys alongside other ranking methods like Pairwise Comparison and Points-Based Voting.
→ Create a FREE Conjoint Analysis Survey now