Choice and modeling
Most measurable survey content falls under choice (pick from options), scales (rate or rank), or advanced modeling (trade-offs between many attributes). In the builder these appear as two picker groups:
- Response types — everyday questions participants answer often
- Choice modelling — trade-off methods for product and pricing research
All are available in surveys, usability tests, and in-product surveys.
Response types
Section titled “Response types”Choice (single or multiple)
Section titled “Choice (single or multiple)”What it is
Participants select one or more options from a list you define. Display as radio buttons, checkboxes, dropdown, or list box.
Why choose it
The default for most survey questions — demographics, behaviours, attitudes with fixed answers.
Results you get
Bar or pie charts showing option shares; segment by language or variables.
Examples
- “Which role best describes you?” (single)
- “Which tools do you use weekly?” (multiple, select all that apply)
- Yes / No / Not sure screener questions
Open text
Section titled “Open text”What it is
Free-text answer in a short or long field.
Why choose it
Capture wording, reasons, and ideas you did not anticipate in options.
Results you get
Response lists; AI qualitative highlights and Ask Data on text where enabled.
Examples
“What frustrated you most?”, “Describe your ideal workflow”
List response
Section titled “List response”What it is
Several short text fields in one question — e.g. “List your top three priorities.”
Why choose it
Structured lists without repeating the same question three times.
Results you get
Per-field text lists and counts.
Examples
“Name three competitors you considered”, “Top five features you use daily”
Rating
Section titled “Rating”What it is
An ordered scale for one statement — stars, emojis, or numbered scale with optional endpoint labels.
Why choose it
Satisfaction, agreement, or difficulty on a single item.
Results you get
Mean score and distribution (CSAT-style charts).
Examples
“How easy was checkout?” (1–5), “Rate the clarity of this page” (stars)
Net Promoter Score (NPS)
Section titled “Net Promoter Score (NPS)”What it is
Standard 0–10 recommendation scale with promoter / passive / detractor grouping.
Why choose it
Benchmarkable loyalty metric comparable across waves and industries.
Results you get
NPS score, gauge visualization, promoter/detractor breakdown.
Examples
“How likely are you to recommend [product] to a colleague?”
Slider
Section titled “Slider”What it is
Continuous or stepped scale along a track (e.g. 0–100).
Why choose it
Intensity, probability, or agreement when a full numeric range feels natural.
Results you get
Histogram or distribution of values.
Examples
“How confident are you? (0–100)”, “What % of your work uses this tool?”
Ranking
Section titled “Ranking”What it is
Order a fixed list from most to least important (or preferred).
Why choose it
Priorities when relative order matters more than absolute scores.
Results you get
Average rank per item and rank distribution charts.
Examples
“Rank these benefits in order of importance”, “Order these delivery options by preference”
Choice modelling
Section titled “Choice modelling”Use these when you need trade-off insight — what people would give up to get something else — not just “which option do you like best.”
MaxDiff (best–worst scaling)
Section titled “MaxDiff (best–worst scaling)”What it is
Repeated sets where participants pick the most and least important items from a subset of attributes.
Why choose it
Prioritise many attributes (features, messages, benefits) without asking participants to rate every item on long scales.
Results you get
Utility scores and best–worst summaries per item.
Examples
- Which of these 12 value propositions matter most / least?
- Message testing for campaign claims
Conjoint analysis
Section titled “Conjoint analysis”What it is
Participants choose between realistic bundles (e.g. price + speed + brand) built from attributes you define.
Why choose it
Simulate market choices and estimate relative importance of price, features, and brand.
Results you get
Importance charts and choice-based preference profiles.
Examples
- Subscription plan packages (price, storage, support level)
- Concept profiles for a new product line
Adaptive conjoint
Section titled “Adaptive conjoint”What it is
Conjoint where follow-up choices adapt based on earlier answers — fewer tasks, tailored to each participant.
Why choose it
Long attribute lists where standard conjoint would feel too heavy.
Results you get
Similar to conjoint with adaptive design efficiency; importance and utility-style outputs.
Examples
Large attribute sets in pricing studies or complex service bundles
Choosing between response types and modeling
Section titled “Choosing between response types and modeling”| Research question | Start with |
|---|---|
| Who are they / what did they do? | Choice |
| Why / verbatim feedback? | Open text |
| How satisfied? | Rating or NPS |
| What matters most from a long list? | MaxDiff |
| What would they buy if options differ on several attributes? | Conjoint |
Paste import with AI often detects NPS, ranking, and simple choice from wording — see Paste content with AI. MaxDiff and conjoint are usually built in the picker.