Conjoint Analysis vs. Analytic Hierarchy Process (AHP) — Explanations, Differences, Examples, Use Cases
It’s easy to get confused about the differences between Conjoint Analysis and the Analytic Hierarchy Process (AHP). Both are survey methods that help you measure what matters most in scenarios with many criteria. Both survey methods use comparison-based voting methods. Both are widely misunderstood and usually described in unnecessarily complicated ways.
If you ask ChatGPT, it will tell you that conjoint analysis is for marketing research and AHP is for strategic decisions, but that is completely incorrect. Conjoint is often used to inform strategic decisions, and AHP is often used in market research.
The real difference is structural. It's about whether you should evaluate everything together or separately. Once you understand that difference, choosing between conjoint analysis and the AHP method becomes a lot easier.
Contents
Explanation
Differences
When to Use Conjoint Analysis
When to Use The AHP Method
Common Mistakes
Recommended Tools
Explaining the difference between Conjoint Analysis and Analytic Hierarchy Process (AHP)
When you want to rank a list of options, there are a bunch of different survey methods you can use:
Ask people to rank the entire list of options from 1st down to last place → ranked choice voting.
Give everyone 100 points to allocate them amongst the list of options → points-based ranking.
Break the list into head-to-head pairs and rank by how often each option is picked as the winning choice → pairwise comparison.
Break the list into sets of 4 options at a time and ask people to pick the best and worst choice in each set → maxdiff analysis.
But what happens when you’re trying to rank things that don’t belong to just one list? What happens when you have a multi-dimensional scenario? That’s where methods like conjoint and AHP come in handy.
Conjoint Analysis
In conjoint analysis, your list of options belong to categories, and you want to know which categories are most important, but also which options within each category people prefer most.
To do this, conjoint analysis gives people a set of profiles. On each profile, you see the same categories (eg. color, size, weight), but the options within each category are randomized (like blue, green, red, etc. for the ‘color’ category). Each time someone picks the profile they like most, those profiles disappear and a new set with randomized options appears in their place.
Example of pairwise voting on a conjoint analysis survey hosted on OpinionX
By tracking which profiles people pick as their preferred choice each time, conjoint analysis identifies the categories that influence people’s decisions the most and which options they’re most attracted to.
Example of conjoint analysis results from an OpinionX survey
Analytic Hierarchy Process (AHP)
Unlike conjoint analysis, where measuring the importance of categories and options happens at the same time, the AHP method has two separate voting phases.
In the first phase, you’re checking which criteria are most important to people when making a decision. For example, if you’re trying to decide between a set of streaming platforms, your criteria might be price, catalog, and seats per subscription. To see which are most important, you put them into head-to-head pairs and vote on a 1-9 scale to show which criteria is more important:
Let’s imagine the first stage of our AHP results tells us that when we’re considering a streaming platform to buy:
50% of our decision is based on the shows/films they offer (catalog).
35% of our decision is based on their monthly subscription price.
15% of our decision is based on how many people can share the account (seats).
These percentages are called weights and they tell us how each criteria matters to us when considering the list of candidates.
In the second phase, you take one of those criteria points and compare each candidate for that. For example, you start by comparing Netflix, Disney+ and HBO in head-to-head pairs and you vote on that same 1-9 scale again based on which offers a better price. Then you repeat this again for catalog, and again for seats.
This second phase of voting gives us a score for how well a candidate performs in each criteria.
For the last step in the AHP method, you combine the scores and weights to create a total for each candidate. This combined total tells you which candidates are strongest overall when considering how much each criteria matters to you. This would work like this:
So AHP is basically separating "what matters most" from the "who's best at that" question, letting you answer each one independently, and then combining them into a single result to help you pick the best choice.
Main Differences Between Conjoint Analysis and The AHP Method
Both methods help you make multi-criteria decisions, but they are almost never interchangeable.
1. Implicit vs. Explicit Data
Conjoint analysis acts like a hypothetical purchase decision between a set of different offerings or choices. It looks at people’s behavior and calculates what matters most based on their choices. This is implicit scoring — weights are based on observed.
On the other hand, AHP directly asks people to say exactly how much each thing matters to them. It’s explicit scoring — weights are based on the numbers people pick to say how important the various criteria are to them.
2. Nested vs. Independent Information
In conjoint analysis, options are nested — they belong to separate categories and two options from different categories can’t be directly compared. You’d never ask someone if they’d prefer a blue car or a $50,000 car — instead, you compare the different color options against each other, the different price points against each other, and you consider whether price or color overall is more important to you.
In the AHP Method, there is no nesting like this. You have a list of criteria that you’re using to help make the decision, and you have a set of candidates to choose from. Those are two completely separate and independent things.
3. Why vs. What Outcome
The output of conjoint analysis is a model that helps you understand how people think when they’re making decisions. It’s like a trade-offs engine, helping you see which categories they spend the most time considering before they decide, and whether each option would make the product/service seem more attractive or less attractive overall. Conjoint insights can help you make decisions a lot easier for people, by better communicating the factors you know they care about, or checking which combination of options they’d pick before you commit to a big launch. It’s a like model for why people act the way they do, which you can use in lots of interesting ways afterwards.
On the other hand, the AHP Method is better thought of as a way to inform one specific decision on what to do. While you do calculate weights for each criteria, those weights aren’t the key insight. You’re not running an AHP survey to discover that 50% of someone’s purchase decision is based on catalog. You’re running an AHP survey because you’re trying to decide whether to buy Netflix or Disney+. This makes the Analytical Hierarchy Process more useful when you want bring structure to a big decision in your life or to include many people’s inputs towards one collective decision.
Incorrect Explanation of Their Differences
Despite what ChatGPT might tell you, choosing between conjoint and AHP based on your job title is terrible advice. Both can be used for market research, and both can be used to inform internal decision-making. These are simply two survey methods that suit different scenarios and produce quite different outcomes. If in doubt, paste the URL for this article into your LLM chat with an explanation of your scenario and ask it to help you decide which method better suits your needs.
When to Use Conjoint Analysis vs. the AHP method?
TLDR:
Choose conjoint if you're evaluating multi-dimensional bundles, your criteria interact, you need pricing data, or you want to simulate real-world choices.
Choose AHP if your criteria and alternatives are separate dimensions, you want explicit and transparent weights, the criteria are abstract or qualitative, or you need a clear audit trail.
Consider simpler ranking methods (pairwise comparison, maxdiff) if you just need to rank a list of items by importance and don't need the full machinery of either advanced method.
When to Use Conjoint Analysis
Your options belong to discrete categories. This is often the case when you’re assessing products, subscription plans, service packages, or feature configurations.
The categories interact. The value of one thing depends on what else is in the bundle. A premium feature set might be worth paying $30/month for, but not if it comes without any customer support. Conjoint analysis captures these trade-offs naturally because respondents see complete profiles. AHP would miss this interaction entirely because it evaluates each criterion in isolation.
You need to model pricing or willingness to pay. Conjoint is particularly strong when price is one of your categories, because you can calculate exactly how much each upgrade or change is worth in dollar terms. It’s not limited to pricing, but this analysis possibility does make it a go-to method for pricing research because it handles price trade-offs better than any alternative.
You want to simulate real-world choices. The comparison format (pick between two complete options) mirrors how people actually evaluate products and services. This makes conjoint results more predictive of real behaviour than methods where people rate abstract criteria in isolation.
You need predictive modelling. Conjoint data can be used to simulate hypothetical scenarios, like how customers would react to a choice between two different product offerings, like “What happens to preference if you raise the price by $5 but add a feature?” Conjoint simulators let you interrogate your results in interesting ways without having to run another survey.
Some example scenarios where someone might use a conjoint analysis survey:
A product manager testing different configurations ahead of launching a new SaaS tier.
A growth lead trying to improve conversion rate by testing different subscription bundles.
A UX researcher evaluating which combination of features and onboarding experience users prefer.
A marketing manager comparing landing page value propositions that combine different benefit claims.
Despite its reputation as a "marketing" method, conjoint works wherever the decision involves choosing between multi-dimensional options. If you're a PM evaluating different product directions where each direction involves trade-offs across features, timeline, and resource cost, that's a conjoint problem — even though it's an internal strategic decision.
When to Use Analytic Hierarchy Process (AHP)
Your criteria are independent of each other. The importance of "ease of use" doesn't change depending on the price. "Strategic alignment" matters the same amount regardless of development effort. If this describes your situation, AHP's independent weighting makes sense. If your criteria interact, you'll get more relevant results from conjoint.
You want to weigh the criteria explicitly before evaluating options. AHP separates the "what matters?" question from the "which candidates are best at it?" question. This is valuable when the criteria weighting itself is the contentious part of the decision — maybe your team can't agree on whether user demand or revenue impact should drive the roadmap. AHP lets you resolve that question first, then apply the agreed weights to evaluate options.
The criteria are qualitative or abstract. Things like strategic alignment, team morale impact, "brand fit, or long-term scalability are hard to put into conjoint analysis because they don't have a discrete list of options within each one like ‘price’ or ‘seats’. AHP handles abstract criteria comfortably because it only asks "which of these two matters more?" — a judgment people can make even for fuzzy concepts.
You need a transparent audit trail. Because AHP produces explicit weights at each stage, the final ranking is fully traceable. You can show exactly why a decision came out the way it did: "Vendor A won because we weighted integration capability at 35%, and Vendor A scored highest on that criterion." While conjoint analysis does produce relative importance weights for categories and part-worth utility scores for options, AHP’s approach is valuable when you need to quantitatively justify a decision to leadership or other stakeholders.
You're working with a small group of evaluators. Traditional AHP was designed for expert panels and small stakeholder groups. It works well when 1-10 people need to reach a structured consensus. For larger respondent pools, survey-based methods like conjoint analysis, pairwise comparison, or maxdiff analysis can offer similar relative importance scores without the same fixed structure as AHP.
Some examples of scenarios that are well-suited to the Analytical Hierarchy Process:
A product team deciding which of three strategic initiatives to pursue this year, weighted by criteria like user impact, revenue potential, and engineering feasibility.
A UX research lead evaluating vendors for a new research platform based on cost, feature set, support quality, and integration with existing tools.
A hiring committee scoring final candidates against independently weighted criteria.
A leadership team choosing a new office location based on commute time, cost, talent pool access, and client proximity.
Common Mistakes When Choosing Between Conjoint & AHP
"Conjoint is for marketing, AHP is for internal decisions"
This is the most common misconception and the one that leads people astray most often. Both are used to inform decisions, both can be used for market research, and both are multi-dimensional ranking methods. Conjoint can be used for internal strategy instead of AHP if the scenario better fits it. The difference is about the structure of the problem or decision you’re trying to make — like whether your criteria and options naturally bundle together or are separate dimensions — not about which department you sit in.
Using AHP when your criteria are nested
If the value of one criterion depends on the level of another, AHP's independent weighting will miss that relationship. For example, if "fast delivery" matters a lot at a high price point but barely matters at a low price point, AHP struggles to capture that because it weights "delivery speed" the same regardless of price. Whereas conjoint analysis would naturally capture this nuance because respondents see both dimensions together.
Using conjoint or AHP when you just need a simple priority ranking
Not every multi-criteria decision needs conjoint. If you have a list of features and you just want to know which ones matter most to your users, then you should just use a simpler ranking method. Check out pairwise comparison, maxdiff analysis, points-based ranking, or even just a ranking poll. Use conjoint and AHP when needed, not just for the sake of it.
Assuming AHP requires the Saaty Scale
The Analytic Hierarchy Process is a framework. You build your hierarchy (goal, criteria, candidates), compare candidates in pairs, weight them, and then score the candidates. The Saaty Scale, the 1-9 rating system traditionally used to collect the comparison data, is not a mandatory component of AHP. You can easily opt for binary pairwise comparison (which is more important, criteria A or B) without the scale to simplify the process. If you've dismissed AHP because the Saaty Scale feels impractical, you may have dismissed the framework when you only needed to replace the scoring method.
Trying to force one method when your problem needs the other
If someone asks, "What combination of features will customers prefer?" and you try to answer it with AHP, you'll get criteria weights and feature scores but miss the trade-off dynamics. If someone asks, "Which strategic initiative should we prioritize based on four independent criteria?" and you try to answer it with conjoint, you'll struggle to define meaningful profiles because the criteria don't naturally bundle into options.
Tools for Conjoint Analysis, AHP, and Ranking Surveys
Conjoint Analysis Tools
I’ve previously written a post that reviewed the 10 most popular conjoint analysis survey platforms in detail. Overall, the best survey platform for building and running your own conjoint analysis surveys is OpinionX.
OpinionX is built for teams who want to run conjoint analysis projects themselves without being experts or requiring specialist training. By using simple language, a flexible survey builder, clever results automations, and a library of templates and sample surveys, OpinionX turns conjoint analysis from an advanced market research method into an accessible survey method that anyone can use.
Beyond the survey itself, OpinionX has automated results features for conjoint analysis like a scenario simulator, marginal willingness to pay chart, rejection rate analysis, and more. It also lets you filter, segment, and compare your results by group to see how sub-groups voted differently.
The free tier of OpinionX gives you all survey question types and analysis methods for up to 10 respondents per survey, so that you have everything you need to check whether conjoint analysis is the right method for you. And if it is, you’ll also benefit from OpinionX being the cheapest provider of conjoint analysis surveys, costing anywhere from 50% to 83% less than alternatives like Conjointly, Sawtooth, or Qualtrics.
OpinionX’s 'Scenario Simulator’ lets you test different combinations of options from your conjoint survey and see projected market share and revenue outcomes for each profile.
AHP Tools
The Saaty Scale and the calculation method associated with it means that AHP is generally not possible to run on most survey tools (at least not in an automated way that avoids manual spreadsheet work). The main options are:
Expert Choice by Comparion is the enterprise standard for AHP. It was developed by Ernest Forman, a collaborator of AHP’s original creator Thomas Saaty, and supports group decision-making, sensitivity analysis, and resource allocation. It's the most full-featured AHP tool (and is priced accordingly).
SuperDecisions is a free desktop application created by the Creative Decisions Foundation (founded by Saaty himself). It supports both AHP and the more advanced Analytic Network Process (ANP). The interface is dated, but it's the most capable free option for serious AHP work.
AHP-OS is a free web-based tool by Klaus Goepel that handles group AHP decisions in the browser. It's lightweight, well-documented, and a good starting point if you want to try AHP without installing anything.
SpiceLogic AHP Software is a desktop tool with a more modern interface than SuperDecisions. It includes features like transitivity enforcement (which reduces the number of comparisons needed) and sensitivity analysis.
ComcastSamples is a free open-source tool for single-player AHP voting, which cuts out all the fluff and offers a basic input tool to help you come to an informed decision on your own.
Ranking / Prioritization Survey Tools
If you've realised by reading this post that you just need to rank a list of items by importance, not the full machinery of conjoint analysis or analytic hierarchy process, then here are the survey methods you should consider using:
Pairwise Comparison takes your list of ranking options, shows respondents two items at a time, and asks which matters more. This is the same ‘pairwise comparison’ as AHP, but without the 9-1-9 intensity scale. Results are ranked using a ‘win rate’, ie. the percentage of pairs in which an option was picked as the preferred choice. OpinionX is the number one survey platform for pairwise comparison.
Example of a pairwise comparison ranking survey on OpinionX
MaxDiff Analysis is very similar to pairwise comparison, but it shows 3-6 options at a time instead of just a pair, and asks respondents to pick the most and least important option in each set. It also produces an easy-to-understand ranked results list that you can sort, segment, search, and so on. OpinionX offers maxdiff analysis surveys on its free tier.
Example of a MaxDiff Analysis survey from OpinionX
Points-Based Ranking gives each respondent 100 points and asks them to ‘spend’ them on the list of options based on how important each one is to them. This approach has the benefit of more freely surfacing the intensity of someone’s preferences, and is well-suited to budgeting consensus, deciding on investment priorities, or simulating bundling purchase decisions. OpinionX is the most popular survey platform for points-based ranking polls.
Example of a points-based ranking survey hosted on OpinionX
Ranked Choice Voting is the best-known survey method for measuring people’s preferences and priorities. It shows the full list of options and asks people to rank them from highest to lowest preference. It should only really be used with small lists of 3-10 options max, otherwise a sample-based approach like pairwise comparison or maxdiff analysis that breaks options into more digestible sets is better suited. Create free order ranking surveys on OpinionX.
Example of a ranked choice voting survey hosted on OpinionX
These four methods cover the middle ground of prioritization research where you need more rigor than a simple 1-5 star rating scale, but you don't need to create an advanced model of attribute bundles (conjoint) or build a full decision hierarchy (AHP). Feature prioritization, user needs ranking, initiative scoring, and value proposition testing all tend to land here.
Whether you choose an advanced method or a simple voting poll, OpinionX is a research platform built specifically for ranking surveys. Its free tier lets you create your own surveys that include methods like conjoint and maxdiff analysis. All plans include prepaid consulting time with expert researchers, so if you get stuck or need a hand with survey setup, distribution, or analysis, an OpinionX expert will be available to help.
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About The Author:
Daniel Kyne is the Founder & CEO of OpinionX, the platform for advanced market research surveys. Hundreds of the world’s top product teams use OpinionX to measure their customers needs, map customer segments, and test pricing changes — all inside this one easy-to-use platform for advanced surveys.
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