Q01
Message
All templates
Probabilistic Forecast & Estimation Survey
Replace single-point guesses with honest uncertainty: experts build a probability distribution over outcomes instead of naming one number, and the AI interviewer elicits the assumptions behind their shape. Ideal for sales forecasts, launch estimates, and planning reviews.
Sample questions
A preview of what’s in the template. Every question is editable before you launch.
8 questions · ~5 min
Q02
Multiple ChoiceRequired
How close are you to the thing being forecast?
- I own the number
- I contribute to it directly
- I observe it closely
- I'm an informed outsider
Q03
Distribution builderRequired
Distribute 20 chips across the outcome ranges to show how likely you think each is. More chips = more likely. (Template note: relabel the bins for your own metric before launching.)
Q04
Opinion ScaleRequired
How confident are you in your own forecast?
Scale: 1 – 7
Min:Very unsure — wide error barsMax:Very confident — I'd bet on it
Q05
Short TextRequired
What is the single biggest factor that could push the outcome toward the LOW end?
Q06
Short TextRequired
And the single biggest factor that could push it HIGH?
Q07
AI Interview
Elicit the model behind the forecast: what base rate or history anchors their central estimate, which assumption they'd abandon first if early data disappointed, whether their tails reflect real scenarios or just hedging, and what leading indicator they would watch to know which way it's breaking. If their stated confidence and their distribution shape disagree, point at the gap and explore it.
Q08
Message
Forecast submitted — thank you! Individual distributions aggregate into a crowd forecast, and the interviews document the assumptions worth monitoring.
What’s included
AI follow-ups
Adaptive probes on open-ended answers that pull out detail a static form would miss.
Attention checks
Built-in safeguards against rushed answers and low-quality respondents.
AI-drafted copy
Wording, ordering, and branching written by the AI — tuned to your research goal.
Auto report
Themes, quotes, and a plain-English summary write themselves once responses come in.
How it compares
We reviewed the closest templates from other survey tools. Here’s what they do well — and where this template goes further.
Why this template
- A native distribution builder elicits probability across outcome ranges — honest uncertainty instead of falsely precise point estimates
- Confidence calibration is checked against the distribution's actual shape, and mismatches get probed
- The AI interview documents each forecaster's assumptions and the leading indicator they'd watch — the inputs a planning review actually needs
- Aggregates individual distributions into a crowd forecast with the reasoning attached
SurveyMonkey
What is Purchase Intent And How To Measure ItMethodology resource (not a one-click template) that supplies the closest real analog to a forecasting/estimation study: purchase-intent questions that forecast demand, project inventory, and identify segments ready to buy within 3-12 months. Provides concrete Likert intent, timing, budget, 0-10 likelihood, and ranking questions plus a Purchase Intent Score formula. Strong elicitation examples, but a static questionnaire with no probabilistic or adaptive estimation.
What it does well
- Directly ties survey design to forecasting: demand projection, inventory, and purchase timing within 3-12 months
- Concrete question bank: Likert intent, timing multiple-choice, willingness-to-pay ranges, 0-10 likelihood-vs-competitor, brand ranking
- Defines a Purchase Intent Score (combining 'definitely' + 'probably will buy') as a summary metric
- Segments respondents by readiness/timeframe for planning
Where it falls short
- Point-estimate self-reports with no calibration, confidence intervals, or probabilistic elicitation
- No adaptive AI follow-up to pressure-test an optimistic 'definitely will buy' response
- It's a methodology article, not a ready-to-field template or an auto-generated forecast report
- No mechanism to compare forecasts against realized behavior or to aggregate expert estimates
QuestionPro
Top 7 Product Concept Test Survey Questions + Sample Questionnaire TemplateA fielding-ready concept-test template that doubles as demand estimation before launch: it captures buying interest on a five-point interested-to-not-interested scale, expected price point, feature importance, favorability, and usage frequency. Good for gauging pre-launch appeal and expected price, but the estimation is a single stated-intent snapshot with no adaptive probing or forecast synthesis.
What it does well
- Ready-to-field template with a direct purchase-interest question (five-point interested-to-not scale)
- Elicits an expected price point respondents would pay, supporting revenue estimation
- Combines feature-importance (1-5) and favorability (Poor-Excellent) with usage-frequency questions
- Frames the survey explicitly as testing a concept before market launch
Where it falls short
- Stated purchase interest is a single snapshot with no calibration or probability weighting
- No adaptive AI follow-up to probe why interest is low or what would raise it
- No native constant-sum to force feature/price trade-offs behind the estimate
- Interpretation and any demand projection are left to the analyst; no auto-report
Ready to launch?
Open this template in the editor. Every part is yours to change before the first respondent sees it.