Q01
Message
All templates
Budget Allocation & Priority Trade-Off Survey
Give stakeholders 100 points and make them spend it: constant-sum allocation across initiatives forces the prioritization that agree-scale surveys hide. Follow-up questions capture what they'd cut entirely, and the AI interviewer pressure-tests their biggest bet.
Sample questions
A preview of what’s in the template. Every question is editable before you launch.
7 questions · ~5 min
Q02
Multiple ChoiceRequired
Which part of the organization are you closest to?
- Product & engineering
- Sales & marketing
- Operations & support
- Finance & administration
- Leadership
Q03
Point AllocationRequired
Allocate 100 points across these initiatives according to the priority you believe each deserves.
- Improve the core product experience
- Launch in new markets or segments
- Reduce operating costs and tech debt
- Invest in brand and demand generation
- Strengthen customer success and retention
- Build new AI capabilities
Allocate 100 points
Q04
Multiple ChoiceRequired
If we could only fund ONE of these fully, which should it be?
- Improve the core product experience
- Launch in new markets or segments
- Reduce operating costs and tech debt
- Invest in brand and demand generation
- Strengthen customer success and retention
- Build new AI capabilities
Q05
Multiple ChoiceRequired
And which would you cut entirely if forced?
- Improve the core product experience
- Launch in new markets or segments
- Reduce operating costs and tech debt
- Invest in brand and demand generation
- Strengthen customer success and retention
- Build new AI capabilities
Q06
AI Interview
Pressure-test the allocation: what evidence or experience is behind their biggest bet, what would have to be true in 12 months for that bet to look right, why the initiative they cut deserves zero (sunk cost? someone else's problem? genuinely low value?), and where they believe the organization's CURRENT spending diverges most from their allocation.
Q07
Message
Allocation recorded — thank you! Results aggregate into a priority map showing where the organization agrees, and exactly where it splits.
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
- Native constant-sum allocation with a forced 100-point total and option randomization — trade-offs, not agree-scale inflation
- Fund-fully and cut-entirely questions bracket the allocation with hard choices
- The AI interviewer pressure-tests the biggest bet: the evidence behind it and what would falsify it in 12 months
- Role capture lets you compare how different functions would spend the same budget
QuestionPro
Constant Sum Question Type - What You Need To KnowMethodology guide plus feature page for QuestionPro's native constant-sum question, where respondents divide a fixed total (100 points, a $5,000 budget, a 40-hour week) across options. Strong practical design guidance (5-7 options max, base list on prior qualitative research, randomize to avoid order bias) and a genuine question type rather than a workaround, but it's a single question, not a full trade-off study with follow-up.
What it does well
- Genuinely native constant-sum question type that enforces the sum and produces metric data
- Clearly explains the forced-trade-off advantage over 'everything is very important' rating scales
- Flexible totals beyond 100 (dollar budgets, hours) matching real resource-allocation framing
- Actionable design rules: cap at 5-7 options, ground the list in prior qualitative work, randomize order
Where it falls short
- Documents a single question type, not a guided budget-allocation study with segmentation
- No adaptive follow-up asking why a respondent starved or over-funded a given option
- No automatically generated report ranking priorities and surfacing segment differences
- Methodology lives in a blog post rather than a transparent, editable in-product prompt
BlockSurvey
Constant Sum Survey Questions: Allocate Points & BudgetFeature page for BlockSurvey's constant-sum question built from multiple number boxes with an auto-total and a 'Require a Fixed Sum' toggle that validates before submission. Includes a concrete marketing-channel allocation example (100 points across social, email, SEO, paid, events) and a comparison table vs. rank-order. Clean setup and validation, but analysis is mean-based with no adaptive probing or narrative output.
What it does well
- Concrete worked example (100 points across five marketing channels) makes the use case tangible
- Built-in fixed-sum validation prevents submissions that don't total correctly
- Explicitly contrasts constant-sum vs. rank-order so users pick the right instrument
- Explains analysis via mean allocations across respondent groups
Where it falls short
- Assembled from multiple number boxes plus a toggle rather than a first-class trade-off study flow
- No adaptive AI follow-up on surprising allocations
- Analysis stops at group means; no auto-generated priority report
- No pairing with qualitative interview to explain the trade-offs a respondent made
OpinionX
Constant Sum Survey Method [Explanation & Real Examples]Method explainer and product page framing constant-sum as 'points allocation' that reveals the magnitude of preferences, not just the order. Features a memorable Google Docs beta example (testers put 89% of a $100 budget toward formatting, exposing a hidden usability gap) and candidly notes that Google Forms, SurveyMonkey, and Typeform lack native constant-sum. Strong storytelling and honest tooling context, but still a single explicit-preference question.
What it does well
- Vivid real example (Google Docs $100 allocation) showing how the method surfaces hidden priorities
- Clearly frames the value as capturing preference magnitude, not just rank order
- Transparent about which mainstream tools lack native constant-sum and names alternatives
- Distinguishes explicit (constant-sum) vs. implicit (pairwise) preference measurement
Where it falls short
- Focuses on one question method rather than a full guided allocation study
- No adaptive AI follow-up to interview respondents about their allocation logic
- No native auto-report synthesizing allocations into recommendations
- Method write-up is a blog explainer, not an editable in-app methodology prompt
Ready to launch?
Open this template in the editor. Every part is yours to change before the first respondent sees it.