Operations & Data

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.

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샘플 질문

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질문 7개 · 약 5분

Q01

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You have 100 points to spend — they represent our budget, time, and attention for the next cycle. Spend them across the initiatives the way YOU believe they should be spent. The forced trade-off is the point: not everything can win.

Q02

객관식필수

Which part of the organization are you closest to?

Product & engineering
Sales & marketing
Operations & support
Finance & administration
Leadership

Q03

점수 배분필수

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

100점 배분

Q04

객관식필수

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

객관식필수

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 인터뷰

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

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Allocation recorded — thank you! Results aggregate into a priority map showing where the organization agrees, and exactly where it splits.

포함된 기능

AI 후속 질문
정형화된 설문이 놓치는 세부 내용을, 주관식 답변에 맞춰 AI가 심층 질문으로 끌어냅니다.
주의력 확인 장치
성의 없는 답변과 저품질 응답자를 걸러내는 내장 안전장치입니다.
AI가 작성한 문안
문구, 질문 순서, 분기 로직까지 AI가 연구 목표에 맞춰 작성합니다.
자동 리포트
응답이 모이면 주요 주제, 인용문, 이해하기 쉬운 요약이 자동으로 작성됩니다.

다른 서비스와 비교

다른 설문 도구의 가장 유사한 템플릿을 검토했습니다. 그 도구들이 잘하는 점과, 이 템플릿이 한발 더 나아가는 지점을 정리했습니다.

이 템플릿을 선택하는 이유

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 Know

Methodology 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.

잘하는 점

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

아쉬운 점

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 & Budget

Feature 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.

잘하는 점

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

아쉬운 점

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.

잘하는 점

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

아쉬운 점

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

자주 묻는 질문