Symptom: Many features show high Questionable rates.
Cause: Functional and dysfunctional prompts are vague or double-barreled.
Fix: Rewrite each pair with one clear behavior and re-test wording with a small group.
Kano Survey Builder + Scorer (Free)
Use this Kano survey builder as a Kano model survey for product feature prioritization (functional / dysfunctional questions) to build a Kano questionnaire, score paired responses, inspect the full category distribution, coefficients, segment differences and classification stability, then carry that evidence into the real Product decision.
- Draft functional/dysfunctional question pairs and export a copy-ready questionnaire
- Score A/O/M/I/R/Q responses with Better/Worse coefficients and full distributions
- Inspect dominance gaps, single-response stability, segment divergence, and data-quality flags
File parsing runs locally. Parsed Kano response data is autosaved in this browser unless you clear it; CraftUp does not persist the uploaded file on its backend.
Allowed responses: I like it, I expect it, I am neutral, I can tolerate it / live with it, I dislike it. Parser aliases such as love or hate are accepted on import but are not respondent-facing options.
Review policies (your team's rules, not Kano standards)
These thresholds only change review flags. They never alter raw A/O/M/I/R/Q counts or Better/Worse coefficients, and they are not scientifically validated cutoffs.
Upload or paste data to compute Kano results.
How it works
Step 1
Draft paired functional/dysfunctional questions per feature, or upload a CSV/XLSX file or pasted response table in the template schema.
Step 2
The scorer maps each answer pair to A, O, M, I, R, or Q with a documented matrix, then shows distributions, coefficients, segment divergence, and classification stability.
Step 3
Inspect the evidence, record caveats, export an evidence record, and carry it into Prioritization, RICE, ICE, or MoSCoW for the investment choice.
What Kano actually tells you
The Kano model classifies how respondents react to the presence and absence of a feature: Attractive (A), One-dimensional (O), Must-be (M), Indifferent (I), Reverse (R), or Questionable (Q). That reaction shape is useful customer evidence. It does not, by itself, tell you what should be built first, because Kano evidence says nothing about effort, reach, cost, dependencies, strategy, or market demand.
Why the distribution matters more than one badge
A single category badge hides how close the vote was. A feature at 31% Must-be, 29% One-dimensional, and 24% Indifferent is a genuinely different situation from one at 74% Must-be, even though both can carry the same label. This tool leads with valid response counts, unique respondent counts, the full A/O/M/I/R/Q distribution, the dominance gap, and a leave-one-out stability check that shows whether removing one response flips the category.
The evaluation matrix we apply
Kano analysis depends on paired questions: how users feel when a feature is present (functional) and how they feel when it is absent (dysfunctional). Each pair maps to a category through a documented Berger-style matrix: Like plus Dislike maps to One-dimensional, Expect, Neutral, or Tolerate plus Dislike maps to Must-be, and only the Like-plus-Like and Dislike-plus-Dislike diagonals map to Questionable. Published matrices differ around some diagonal cells, so the exact version is stated in every export and covered by automated tests over all 25 pairs.
Better and Worse coefficients
Better estimates satisfaction upside from delivering a feature; Worse estimates dissatisfaction risk when it is missing. Both exclude Reverse and Questionable responses from their denominators. They describe reaction shape under the model, not ROI, probability, effort, reach, or willingness to pay, so always read them next to the distribution, the segment split, and the stability diagnostic.
Segment disagreement
One overall category can hide a real conflict: enterprise respondents may classify a feature as Must-be while SMB respondents classify it as Indifferent. The tool shows overall versus per-segment categories with exact denominators instead of averaging the disagreement away, and it never decides which segment should win.
Why small samples can be unstable
There is no universal validated minimum number of Kano respondents, so this tool sets none. Sample adequacy depends on the decision at stake, the population, segment splits, and recruitment quality, and survey-science reviews urge caution with small Kano samples. Configure your own small-sample review threshold, treat the dominance gap as descriptive rather than confident, and check whether one response can flip the label before presenting a category as settled.
Questionable responses
A Questionable pair, liking or disliking both presence and absence, is a data-quality signal, not a normal feature class. It can reflect confusing wording, incompatible answer meanings, inattentive answering, or a feature that is hard to imagine. A high Questionable rate means review the questionnaire before trusting the category, and a feature where every response is Questionable has no interpretable Kano category at all.
Kano Must-be is not MoSCoW Must
Kano Must-be describes a satisfaction pattern: absence causes dissatisfaction while presence adds little delight. MoSCoW Must Have means guaranteed scope for a defined timeframe. One is customer-reaction evidence; the other is a delivery commitment. Never map one to the other automatically, and never turn Kano Attractive into build-first, Kano Indifferent into delete, or Kano Reverse into never-build without a separate investment decision.
Kano does not prioritize the roadmap for you
A global Must-be → One-dimensional → Attractive order is not a roadmap algorithm: a Must-be can be expensive, out of strategy, or already satisfied, while an Attractive feature can be a high-leverage differentiator. Use Kano to understand customer-response shape, then make the investment choice in the prioritization workspace, RICE, ICE, or MoSCoW with the Kano evidence attached as context.
How to write good Kano features
Keep feature prompts concrete, singular, and easy to imagine in real product use. Avoid combining multiple behaviors in one item, and avoid terms that imply value judgment. High Questionable rates usually come from ambiguous wording, overlong descriptions, or confusing response labels. Test wording with a small group before broad rollout so the full sample produces interpretable data.
How to interpret results
Read the full distribution before the badge: valid responses, unique respondents, category shares, the dominance gap, and whether one response flips the label. Then compare segments, check Questionable and duplicate-response flags, and record what the evidence still leaves open. Treat review thresholds as your team's policy, not scientific law, and hand effort, reach, and sequencing to a prioritization method built for those tradeoffs.
Pro tips
- Write each feature as one concrete behavior, not a bundle of multiple outcomes.
- Anchor every feature to one target segment before collecting responses.
- Use the same response option wording across all questions to reduce noise.
- Pilot wording with a small group and fix confusing items before broad distribution.
- Keep feature descriptions under two sentences to limit interpretation drift.
- Treat a high Questionable rate as a review signal and rewrite unclear items first.
- Decide your own minimum-evidence rule per feature before roadmap discussions.
- Compare CS and DS together so delight and dissatisfaction risk stay in one view.
- Review segment splits before drawing conclusions to catch hidden disagreement.
- Export an evidence record after every run so stakeholders review the same snapshot.
Common mistakes
Symptom: Every feature looks Must-be.
Cause: Respondents were primed by onboarding copy before the survey.
Fix: Randomize question order and remove leading context from introductions.
Symptom: Conflicting categories appear between teams.
Cause: Segments with different needs were mixed into one overall analysis.
Fix: Add segment labels and review the segment-by-feature classification table first.
Symptom: Roadmap decisions change every week.
Cause: Thin-evidence features are treated as final verdicts.
Fix: Record sample caveats and stability per feature before any investment discussion.
Symptom: Attractive ideas crowd out reliability work.
Cause: CS is reviewed without DS and segment context.
Fix: Use CS and DS together with segment divergence, then hand the tradeoff to Prioritization.
Symptom: Imported files fail validation frequently.
Cause: Header names or response values do not match the accepted schema.
Fix: Use the template file and normalize responses before uploading.
Symptom: Reverse features are ignored in final decisions.
Cause: Team assumes all reverse signals are noise.
Fix: Investigate reverse signals by segment before removing the feature.
Symptom: Stakeholders distrust the analysis output.
Cause: No shared summary is provided after scoring runs.
Fix: Export the evidence record with distributions, stability, and caveats after each run.
FAQ
Is this Kano survey builder free?
Yes. The no-login builder and scorer are fully free and run in your browser. You can draft paired Kano questions, upload CSV or XLSX files, paste response tables, inspect Kano categories, and export evidence outputs without paywalls or response-collection fees.
What is the difference between functional and dysfunctional questions?
Functional asks how users feel when a feature exists. Dysfunctional asks how users feel when that same feature does not exist. The pair is required for Kano classification because customer sentiment depends on both presence and absence, not only positive preference in isolation.
Which evaluation matrix does CraftUp use?
CraftUp applies a documented Berger-style five-by-five matrix where Like plus Dislike maps to One-dimensional, Expect or Neutral or Tolerate plus Dislike maps to Must-be, and only the Like-plus-Like and Dislike-plus-Dislike diagonals map to Questionable. Published matrices differ around some diagonal cells, so the exact version is regression-tested and stated in every export.
What do the A, O, M, I, R, and Q categories mean?
Attractive means presence delights without absence hurting much. One-dimensional means satisfaction moves with delivery quality. Must-be means absence dissatisfies while presence adds little delight. Indifferent means presence or absence barely moves reactions. Reverse means some respondents prefer absence. Questionable means the answer pair is contradictory and signals a data-quality review.
What do Better and Worse coefficients mean?
Better estimates satisfaction upside from delivering a feature, while Worse estimates dissatisfaction risk when it is missing. Both exclude Reverse and Questionable responses from their denominators. They describe reaction shape under the model, not revenue, effort, reach, or probability, and they never include implementation cost.
What does a Questionable response tell me?
A Questionable pair means the respondent liked both presence and absence, or disliked both, which usually signals confusing wording, incompatible answer meanings, inattentive answering, or a feature that is hard to imagine. Treat a high Questionable rate as a review signal for the questionnaire, not proof that customers are clear or confused.
What is the dominance gap, and is it statistical confidence?
The dominance gap is the percentage-point distance between the most common and second-most common non-Questionable categories for one feature. It is purely descriptive and is not statistical confidence, significance, reliability, or the probability that the category is correct. A narrow gap with a fragile leave-one-out result means the label could flip with one more response.
How many respondents do I need per feature?
There is no universal validated minimum, so CraftUp does not set one. Sample adequacy depends on the decision at stake, the population, segment splits, recruitment quality, and desired precision, and survey-science reviews urge caution with small Kano samples. Configure your own small-sample review threshold, show exact denominators everywhere, and treat thin samples as hypotheses.
Why can segments classify the same feature differently?
Different audiences can genuinely experience one feature as Must-be, Indifferent, or Reverse depending on workflow, expectations, and alternatives. The tool shows overall versus per-segment categories with exact denominators so divergence stays visible. Never average conflicting segments into one universal label without showing the disagreement.
Does a Must-be result mean build it first?
No. A Kano Must-be pattern describes a satisfaction reaction, not an investment order, because Kano evidence says nothing about effort, reach, cost, dependencies, or strategic fit. An expensive Must-be can wait while a high-leverage Attractive ships, so carry the evidence into Prioritization, RICE, ICE, or MoSCoW instead of ranking by category.
Is Kano Must-be the same as MoSCoW Must?
No, and confusing them causes real scope mistakes. Kano Must-be describes how respondents react to absence, while MoSCoW Must Have means guaranteed scope for a defined timeframe. One is customer-reaction evidence and the other is a delivery commitment, so never map one to the other automatically.
Does CraftUp collect survey responses through this tool?
No. The builder produces an exportable questionnaire draft and shareable draft-configuration links, but there is no hosted response-collection backend. Copy the questions into your survey platform, collect responses there, and return here to score the imported file. Any link that only encodes a draft is labeled exactly that.
What is stored in my browser?
File parsing runs locally and parsed Kano response data plus your review thresholds and builder draft are autosaved in this browser under a versioned CraftUp key. CraftUp does not persist the uploaded file on its backend. Use the Clear local Kano data control to remove saved rows, thresholds, and draft state without touching anything else.
What file format should I use for imports?
You can use CSV or XLSX. Required columns are feature_id, feature_name, respondent_id, functional_response, and dysfunctional_response, with optional segment and timestamp columns. Use the template download to avoid header mismatches, and keep canonical answer labels stable across questions so the evaluation matrix stays comparable.
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Freshness
Last updated: 2026-09-23
- Launched no-login Kano scorer with CSV/XLSX import, paste parsing, and full A/O/M/I/R/Q categorization.
- Added CS/DS coefficients, review-policy flags, segment comparison outputs, and CSV/Markdown/JSON exports.
- Rebuilt as an evidence-integrity tool: classification stability diagnostics, segment divergence, duplicate and questionnaire-quality checks, honest draft sharing, and no automatic roadmap ranking.