Q01
メッセージ
すべてのテンプレートOperations & Data
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.
設問の例
テンプレートの内容をプレビューできます。すべての設問は公開前に自由に編集できます。
全8問・約5分
Q02
選択式必須
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
設問必須
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
オピニオンスケール必須
How confident are you in your own forecast?
スケール: 1 – 7
最小:Very unsure — wide error bars最大:Very confident — I'd bet on it
Q05
自由回答（短文）必須
What is the single biggest factor that could push the outcome toward the LOW end?
Q06
自由回答（短文）必須
And the single biggest factor that could push it HIGH?
Q07
AIインタビュー
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
メッセージ
Forecast submitted — thank you! Individual distributions aggregate into a crowd forecast, and the interviews document the assumptions worth monitoring.
含まれる機能
AIによる深掘り
自由回答に合わせてAIが追加で質問し、固定のフォームでは拾えない具体的な内容を引き出します。
注意確認設問
急いだ回答や質の低い回答者を除外する仕組みを標準で備えています。
AIが作成する設問文
文言、設問の順序、条件分岐をAIが調査の目的に合わせて作成します。
自動レポート
回答が集まると、テーマ、引用、わかりやすい要約が自動で作成されます。
他ツールとの比較
ほかのアンケートツールで最も近いテンプレートを調べました。それぞれの優れている点と、このテンプレートがさらに踏み込んでいる点をまとめています。
このテンプレートを選ぶ理由
- 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.
優れている点
- 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
物足りない点
- 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.
優れている点
- 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
物足りない点
- 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
よくあるご質問
「Probabilistic Forecast & Estimation Survey」テンプレートにはどのような設問が含まれていますか？
すぐに使える設問が8問含まれており、最初の設問は次のとおりです：「This is a forecasting exercise — but instead of asking for one number, we'll ask how you'd spread your confidence across…」・「How close are you to the thing being forecast?」・「Distribute 20 chips across the outcome ranges to show how likely you think each is. More chips = more likely. (Template…」。すべての設問は上でプレビューでき、自由に編集できます。
このアンケートの回答にはどのくらい時間がかかりますか？
回答者は通常、8問を約5分で回答し終えます。
テンプレートは編集できますか？
はい。公開前であれば、すべての設問、選択肢、順序を編集できます。設問の追加や削除のほか、調査の目的に合わせた作り直しをAIエディターに依頼することもできます。
このテンプレートは無料で使えますか？
はい。エディターで開けば、すぐに編集を始められます。お試しにアカウントは不要で、無料プランでアンケートを公開できます。
公開の準備はできましたか？
このテンプレートをエディターで開いてみてください。最初の回答者が目にする前に、すべてを自由に変更できます。
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