When everything is important,
what comes first?
Customers can want everything. Your roadmap needs priorities. MaxDiff asks people to choose the most and least important features in small groups, helping you decide what to build next.
Which improvement would matter most and least when buying tickets?
Choose one option in each column.
| Most important | Improvement | Least important |
|---|---|---|
| Upfront total price | ||
| Seat-view previews | ||
| Easier ticket transfers | ||
| Price alerts |
Small choices.
Clearer priorities.
Try the ticket-buying example. The same simple choices, repeated across a study, show which improvements customers value most.
- 01Upfront total price
- 02Seat-view previews
- 03Finding seats together
Iterate is trusted by 10,000+ teams including
MaxDiff
Simple choices,
a few features at a time
Priorities grounded
in customer preferences
Feature combinations
with broader appeal
Make a long list easier to compare
A long list is hard to rank. With MaxDiff, customers compare a few features at a time and choose what matters most and least across several rounds.
Add your features and write your question. Iterate generates balanced rounds automatically, varying the groups and the order of the options.
Which matters most? Which matters least?
| Most | Improvement | Least |
|---|---|---|
| Chosen as most important | Upfront total price | |
| Seat-view previews | ||
| Easier ticket transfers | ||
| Price alerts | Chosen as least important |
Each round brings a different group to compare.
See what deserves attention first
The live response summary shows how often each feature is chosen as most or least important. Bring those results into your next roadmap discussion.
Go beyond counting most and least selections. Research Assistant uses hierarchical Bayesian modeling to estimate each person’s preferences, drawing on their choices and patterns across respondents.
See how strongly customers prefer each feature, with 95% credible intervals that show where priorities clearly separate and where there’s still uncertainty.
Illustrative study results
- Upfront total price24%
- Seat-view previews21%
- Finding seats together17%
- Easier ticket transfers12%
- Price alerts10%
- Saved favorite venues7%
- Parking bundles5%
- Split payments4%
Lines show uncertainty in the estimates (95% credible intervals).
Shares show relative preference within this list and add up to 100%.
Find the feature mix with wider appeal
Your top three features may win over the same customers. A feature further down the list could matter more to people they miss.
TURF analysis uses individual preference estimates to identify features that complement each other, helping you reach beyond the audience your top-ranked features already serve. Use the results alongside effort, cost, and your product strategy to choose what belongs on your roadmap.
Compare two feature combinations
Highest-scoring three
- Upfront total price
- Seat-view previews
- Finding seats together
Wider reach
- Upfront total price
- Seat-view previews
- Easier ticket transfers
Ticket transfers rank lower overall, but help this combination reach more people.
Example data. First-choice reach counts each person once if their modeled top choice is in the combination.
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Teams that stopped guessing.
Product, research, and lifecycle teams use Iterate to get answers straight from the people who use what they build.
“Iterate makes it super easy to ask for feedback in a way that is consistent with our brand’s look and feel. And the customizable colors and fonts are just the beginning. This type of attention to detail is apparent throughout the entire product, making it a pleasure to use both for us and our users.”
“Iterate’s Braze integration was a game changer for us. It allowed us to seamlessly deploy surveys in-app, gather contextual insights, and significantly reduce the workload on our development team. The ability to embed feedback points throughout the app during beta testing was invaluable.”
Got any questions?
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