Choose the right survey before you start building.
Conjoint and MaxDiff are both preference methods, but they solve different kinds of business decisions. One helps clients test complete offers. The other helps them rank what matters most.
Conjoint is for
Testing full bundles, trade-offs, and the strongest complete proposition.
MaxDiff is for
Ranking features, claims, or needs when the list is long and priorities must be clear.
What changes between them
Conjoint compares combinations. MaxDiff isolates priorities.
This distinction is what clients need to understand before they launch anything. The survey task itself is different, so the output is different too.
Use Conjoint when the client asks
Which combination of price, features, and support will people choose?
Use MaxDiff when the client asks
Which messages, benefits, or features deserve to lead and which can fall back?
Conjoint Analysis
How we run a conjoint study.
Conjoint is built to understand how people choose between complete offers. The method works because respondents are not reacting to isolated claims. They are making a trade-off between realistic alternatives.
Result: the client sees which offer structure wins, which trade-off is acceptable, and where preference is actually being created.
We define the attributes that shape the offer.
That can include pricing, feature depth, support model, pack size, delivery speed, or any other factor the client is actively deciding between.
We turn those attributes into realistic concept combinations.
Instead of asking people to rate features one by one, we show them structured alternatives that look like real purchase options.
We collect forced choices across repeated tasks.
Each choice reveals a trade-off. Over multiple screens, that gives us a stable read on what respondents are consistently willing to choose over something else.
We model the results into preference and utility outputs.
The client gets a view of attribute importance, part-worth utility, and which full proposition has the strongest chance of winning in market.
Result in practice: this usually leads to a direct recommendation on which offer the team should launch, refine, or test next.
What the respondent sees
A choice between complete alternatives
Option A
Standard features, email support, monthly reporting, lower price
Option B (Selected)
Advanced features, priority support, weekly reporting, mid-tier price
Option C
Premium features, dedicated support, live dashboard, highest price
Simulated output
Preference shift across offer concepts
Plan Balanced
Offer Premium
Suite Value
Bundle
What Conjoint is best at
Product bundles, price-feature trade-offs, concept optimization, packaging decisions, and any case where the client needs to know which complete offer wins.
It stands out when several levers have to work together and the client needs the strongest full proposition rather than isolated scores.
What the respondent sees
A best-versus-worst selection task
Product reliability
Ease of use
Customer support
Simulated output
Importance score by decision driver
What MaxDiff is best at
Message prioritization, feature hierarchy, claim testing, benefit sorting, need-state ranking, and any case where the client needs a clean order of importance.
It stands out when everything sounds important in discussion but the client needs a defensible ranking that forces real focus.
MaxDiff Analysis
How we run a MaxDiff study.
MaxDiff is built for situations where clients have too many features, benefits, or messages to evaluate cleanly with standard ratings. The method sharpens priorities by forcing trade-offs inside a short list.
Result: the client gets a clear order of importance, so messaging, product emphasis, and decision focus stop competing with each other.
We define the full item list.
These items can be features, reasons to buy, barriers, value propositions, benefit statements, or brand claims that the client wants to prioritize.
We rotate those items through short balanced sets.
Respondents only see a few items at a time, which keeps the task manageable and prevents the survey from becoming noisy or repetitive.
We ask for the most important and least important choice.
That best-versus-worst task creates separation much faster than asking people to say everything is important on a five-point scale.
We model the final priority hierarchy.
The client gets a clear ranking of what to lead with, what to support, and what can move down the communication or product agenda.
Result in practice: this usually produces a sharper shortlist of what should lead first and what can safely move down the priority stack.
Use Cases
Where each methodology is typically used.
These are common examples you can show clients when they want a concrete sense of where Conjoint fits and where MaxDiff fits.
Conjoint Use Cases
Product and feature bundling
Choosing the right combination of features, tiers, and support for a new offer.
Pricing and packaging decisions
Understanding how price shifts preference when features or service levels also change.
Concept optimization before launch
Testing multiple concept structures to see which full proposition is most compelling.
Variant or portfolio design
Deciding how many variants to offer and what each one should include.
MaxDiff Use Cases
Feature prioritization
Finding out which features matter most when a roadmap has too many competing requests.
Message and claim testing
Ranking which marketing statements or benefit claims deserve to lead communication.
Needs and driver hierarchy
Understanding which customer needs truly drive consideration versus which are secondary.
Brand or experience priorities
Separating the strongest strengths from weaker attributes in brand, service, or CX studies.