13 Types of Conjoint Analysis Explained (With Image Examples)
“There are thirteen named types of conjoint analysis, but almost every project uses one of three: Choice-Based Conjoint (CBC), the modern default where people pick a preferred profile from a set; Adaptive Choice-Based Conjoint (ACBC), which tailors the questions to each participant and suits studies with many attributes; and Full-Profile Conjoint (FPC), the original ranking-based form still used for small attribute counts. The rest are historical, niche, or variations on those three. Which one you need comes down to how many attributes you’re testing, whether pricing is central, and how much statistical support you have. This guide defines all thirteen and tells you which fits which situation.”
What is conjoint analysis?
Conjoint analysis is a survey method for working out which attributes drive people's decisions, and how much each option within those attributes is worth to them, by showing people realistic product profiles and seeing which they choose.
Rather than asking someone to rate how much they care about price, brand and battery life separately, it shows whole profiles that combine all three and infers the weightings from the trade-offs people make. The maths goes back to Luce and Tukey's 1964 work on the underlying measurement theory and became a practical survey method through Green and Rao's work in the 1970s.
There isn't one type of conjoint analysis. There's a family of them, developed over fifty years for different attribute counts, survey lengths and analytical goals. Pick the wrong one and you waste participant effort on results that look authoritative and mislead you. So here are all thirteen, grouped by how they present choices, with a decision guide at the end.
The 13 types of conjoint analysis at a glance
| Type | Family | Still widely used? | Best for |
|---|---|---|---|
| Choice-Based (CBC) | Choice | 🟢 The default | Most studies, especially pricing |
| Adaptive Choice-Based (ACBC) | Choice | 🟢 Yes | Many attributes, longer interviews |
| Full-Profile (FPC) | Ranking | 🟡 For small studies | Six attributes or fewer |
| Adaptive (ACA) | Ranking/rating | 🔴 Legacy, ~2% of projects | Many attributes, non-price |
| Choice-Based with constant sum | Choice | 🟡 Niche | Volumetric demand estimation |
| Menu-Based (MBC) | Choice | 🟡 Niche | Configurable products and add-ons |
| Adaptive Partial-Profile | Choice | 🟡 Niche | Very high attribute counts |
| Ranking-Based | Ranking | 🟡 Small studies | Few profiles, offline settings |
| Rating-Based | Rating | 🔴 Largely superseded | Legacy academic work |
| Self-Explicated | Rating | 🟡 As a hybrid component | Very many attributes, quick studies |
| Hybrid | Mixed | 🟡 Specialist | Combining self-explicated with choice |
| Max-Diff (best-worst) | Choice-adjacent | 🟢 Yes, as its own method | Ranking items, not configuring products |
| Brand-Price trade-off | Choice | 🔴 Historical precursor to CBC | Brand and price only |
Choice-based types of conjoint analysis
These present sets of full or partial profiles and ask people to choose. This is where most modern conjoint lives, because choosing a profile mirrors how people actually buy.
1. Choice-Based Conjoint (CBC)
The most widely used variant, also called discrete choice modelling or a discrete choice experiment. Participants see a set of complete profiles and pick the one they'd choose, or a "none" option, and this repeats across several sets.
CBC is the default for a reason. It matches real purchasing, handles pricing well, and its results feed cleanly into market simulation. It's the type running behind the conjoint tool on OpinionX and behind most commercial software in the category. If you don't have a specific reason to pick another type, this is the one.
2. Adaptive Choice-Based Conjoint (ACBC)
ACBC customises the survey to each participant. It usually opens with a build-your-own step where someone configures their ideal product, then a screening step that learns their must-haves and unacceptables, then choice tasks focused on the profiles that matter to them.
It's more engaging than static CBC and better when you have more attributes than a single choice set can sensibly hold. The cost is length and complexity: ACBC interviews take longer and need more setup, and outside specialist platforms you won't find it. It's a Sawtooth-originated method, and their ACBC documentation is the reference. The scoring behind it, like most modern conjoint, rests on multinomial logit models.
3. Choice-Based Conjoint with constant sum
A CBC variant where, instead of picking one profile, participants distribute a fixed number of points or an expected purchase volume across the options in each set. Useful when you care about how much of each option someone would buy rather than a single pick, which matters for consumable or repeat-purchase categories. It shares the magnitude logic of points allocation.
4. Menu-Based Conjoint (MBC)
Participants build their own product from a menu of options with prices attached, like configuring a car or a software subscription. MBC models how people trade up and add on, which standard CBC can't capture, and it's the right choice when your actual product is a configurator. It's setup-heavy and analytically demanding.
5. Adaptive Partial-Profile Conjoint
A choice-based method for very high attribute counts, showing each participant only a subset of attributes per task and adapting which subset based on earlier answers. It's a way of running conjoint on a product too complex for full profiles, and it sits firmly in specialist territory.
Ranking-based types of conjoint analysis
The original forms. Participants rank whole profiles from most to least preferred, which produces rich data but gets unwieldy fast as the number of profiles grows.
6. Full-Profile Conjoint (FPC)
The original form, where participants rank or rate a set of complete profiles, each showing one level from every attribute. FPC gives you a lot of information per participant, but the number of profiles balloons as attributes grow, so it only works for small studies, roughly six attributes or fewer. We've written a full walkthrough on how to create a full-profile conjoint survey if this is the one you need.
7. Adaptive Conjoint Analysis (ACA)
One of the older variants, ACA customises the interview to each participant, focusing on their most relevant attributes to avoid overload. Developed in the 1980s for studies with many attributes, it's now a legacy method: Sawtooth reports it at roughly 2% of conjoint projects, and it's weak on pricing, where CBC and ACBC are far stronger. Worth knowing about, rarely worth choosing.
8. Ranking-Based Conjoint
Participants rank profiles in order of preference without the adaptive or choice mechanics. Simple, intuitive, and workable offline with cards on a table, but it doesn't scale past a handful of profiles and gives you nothing on pricing. Fine for a quick, small, in-person study.
Rating-based conjoint types
Participants score profiles on a scale rather than choosing or ranking between them. These are largely historical, because a rating scale reintroduces the very problem the method was built to avoid: people don't make trade-offs when they can give everything a high score.
9. Rating-Based Conjoint
Participants rate each profile, usually one to ten. It once dominated academic conjoint and has been largely superseded by choice-based methods, for the trade-off reason above. If you're looking at this type, CBC almost certainly serves the same goal better.
10. Self-Explicated Conjoint
Not really a conjoint method in the trade-off sense. Participants rate the importance of each attribute and the desirability of each level directly, then the two are combined. It's fast and handles a very large number of attributes, and it's most useful as a component inside a hybrid design rather than on its own.
11. Hybrid Conjoint
Combines self-explicated ratings with choice or full-profile tasks, using the quick self-explicated step to narrow the attributes before the heavier trade-off step. A specialist approach for studies with more attributes than a pure choice design can handle.
Choice-adjacent and historical types
12. Max-Diff (best-worst scaling)
Often grouped with conjoint, MaxDiff shows a subset of items and asks for the best and the worst. Strictly it isn't conjoint, because it ranks standalone items rather than modelling trade-offs between attributes inside a product. Reach for it when you want to prioritise a list, not configure a product. We've compared the two directly in our guide to conjoint alternatives.
13. Brand-Price Trade-Off
A historical precursor to modern CBC, examining only brand against price. It's essentially a stripped-down two-attribute study, and CBC has absorbed everything it did. Included for completeness rather than recommendation.
How do you choose the right type of conjoint analysis?
Three questions settle it for almost every project.
How many attributes are you testing? Six or fewer, and Full-Profile or CBC both work. More than that, and you want CBC, and past ten or so, ACBC or a partial-profile method. The attribute count is the single biggest driver of which type is even feasible.
Is pricing central to the study? If yes, use CBC or ACBC. They're built for it, and the older ranking and rating methods, ACA especially, are weak on price. Willingness to pay is where the wrong type costs you most, so there's a separate guide to pricing methods if that's your main question.
How much statistical support do you have? CBC on a modern tool is self-serve. ACBC, menu-based and the partial-profile methods reward a specialist and often require one. If you're running this in-house without a statistician, CBC is the realistic choice, and it's rarely the wrong one.
For most teams reading this, the honest answer is CBC. The other twelve exist for specific constraints: very high attribute counts, configurable products, volumetric demand, or legacy academic comparability. If none of those describe your study, CBC is your answer and you can stop reading here.
Once you've picked a type, the complete conjoint guide covers designing the survey, and the tools comparison covers where to run it. If conjoint turns out to be the wrong method entirely, the alternatives guide covers what to use instead.
Frequently asked questions
What is the most common type of conjoint analysis? Choice-Based Conjoint (CBC), also called discrete choice modelling. Participants pick a preferred profile from a set, which mirrors real buying, handles pricing well, and feeds directly into market simulation. It's the default on almost every modern conjoint platform.
What is the difference between CBC and ACBC? CBC shows every participant the same choice sets. ACBC adapts the survey to each participant, starting with a build-your-own step and a screening step before the choice tasks. ACBC suits studies with many attributes and produces more engaging interviews, at the cost of length and setup complexity.
Which type of conjoint analysis is best for pricing? CBC or ACBC. Both are built for pricing and feed willingness-to-pay and market-share simulation. The older ranking and rating methods, and ACA in particular, are weak on price.
Is MaxDiff a type of conjoint analysis? Not strictly. MaxDiff ranks standalone items by asking for the best and worst in a set, rather than modelling trade-offs between attributes inside a product. It's often grouped with conjoint because it's choice-based and shares some maths, but the two answer different questions.
How many types of conjoint analysis are there? This guide covers thirteen named types of conjoint analysis, but the practical field is much smaller. Most real projects use one of three: Choice-Based, Adaptive Choice-Based, or Full-Profile. The rest are historical, niche, or components of hybrid designs.
Thirteen types sounds like a decision to agonise over. Count your attributes, decide whether pricing matters, and be honest about your statistical support, and you'll land on CBC most of the time and know exactly why the rest of the time.
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CBC runs on the OpinionX free tier: $0, unlimited surveys, unlimited researcher seats, capped at 25 participants per survey, then $900 a year to lift the cap (full pricing). Build a conjoint survey, or read the complete guide to conjoint analysis first.
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!