By 6days · v1.0 · Updated 7/20/2026
Not all features are equal: some delight, some merely satisfy, and some only hurt when missing.
Fill this in for your own situation — a private worksheet only you can see.
When to use
Use it when a roadmap needs to balance table-stakes against differentiation, when entering an established category where expectations are already set, or when heavy feature investment is somehow producing no measurable satisfaction gain.
When not to use
Avoid it in a genuinely new category, where customers have no expectations to classify against and the survey returns noise. It is survey-based, so it inherits every weakness of stated preference — people are poor predictors of their own delight. It is also expensive to run properly and stale quickly; a three-year-old Kano study is a historical document.
Teams treat features as a single list ranked by how much customers say they want them. But customer satisfaction does not respond linearly to features. Some things generate no goodwill when present and fury when absent. Some delight now and will be unremarkable in two years. Ranking everything on a single 'wanted' axis guarantees you will over-invest in things that cannot make anyone happy and under-invest in the ones that can.
The Kano model sorts features by how their presence and absence affect satisfaction, producing distinct categories rather than one ranking. Must-be features are invisible when present and damaging when missing — nobody praises an aircraft for landing. Performance features scale: more is better, roughly linearly. Attractive features delight when present but are not missed when absent, because customers never expected them. Indifferent features move nothing. The model's sharpest insight is temporal: today's delighter becomes tomorrow's expectation, so categories decay and must be re-measured.
Framework by 6days on 6days — https://6days.apexaion.ai/framework/kano-model
List the features to evaluate in customer-meaningful terms. If a customer cannot tell whether they have it, it cannot be classified — internal work does not belong in this exercise.
For every feature ask two questions: how would you feel if it were present, and how would you feel if it were absent? The pair is the entire mechanism. Asking only the first turns the survey into a wish list, which is the thing you are trying to escape.
Map each respondent's pair to a category. 'Like it if present, expect it if absent' is attractive; 'expect it if present, dislike if absent' is must-be; 'like if present, dislike if absent' is performance. Contradictory answers usually mean the question was ambiguous rather than the customer confused.
Look at the distribution rather than the average, and split by customer type. A feature that is must-be for enterprise buyers and indifferent to individuals will average to 'performance' — a category that describes nobody and misleads everyone.
Cover every must-be adequately before investing anywhere else — they are hygiene and no amount of delight offsets a missing one. Then compete on selected performance features and invest in a small number of attractive ones. Re-run periodically, because the categories will have moved.
Worked example
A hotel books group evaluates features. Hot water and a clean room: must-be — no guest has ever left a five-star review for functioning plumbing, and every guest leaves a one-star review for its absence. Wi-Fi speed: performance, and now the primary driver of business-traveller ratings. A handwritten note from the manager: attractive, delights disproportionately, costs almost nothing, and nobody misses it if it is absent. Same-day laundry: indifferent for this segment. The finding that changes behaviour is that Wi-Fi has migrated from attractive to performance in under a decade and is drifting toward must-be — the investment case for it is not about delight any more, it is about avoiding damage.
The model was developed by Noriaki Kano and colleagues at the Tokyo University of Science around 1980, within the Japanese quality management tradition that also produced much of modern quality function deployment. Its academic origin gives it an unusually explicit methodology — the paired question format and classification table are part of the original work rather than later additions.
Related ways to think about this.
Understand the person, frame the real problem, build something rough, and learn from their reaction.
Use when Use it on ill-defined problems where the need is genuinely unclear, where the people affected are not you, and where the cost of building the wrong thing is high. It is strongest early, when the framing is still open and cheap to change.
Avoid when Avoid it when the problem is well-specified and the answer is known — it is expensive ceremony for work that needs execution. It struggles with problems whose constraints are technical or regulatory rather than human. And it is the most ritualised framework in common use: workshops, sticky notes and a five-stage poster routinely produce the appearance of the method with none of the substance, because nobody left the building to observe anyone.
People don't buy products, they hire them for a job — find out what the job is.
Use when Use it when demographic segmentation has stopped generating insight, when you cannot explain why customers churn or convert, when entering an adjacent market, or when a roadmap has become a queue of the loudest requests with no organising logic.
Avoid when Avoid it for incremental optimisation of a well-understood product — you know the job, and reopening it is procrastination. It is weak for infrastructure and compliance work with no discretionary hiring decision. Done badly it collapses into vague poetry about customer aspirations, and 'the job' becomes whatever the loudest person already wanted, now with better rhetoric.
Strip a problem back to what must be true, then rebuild — instead of copying what exists.
Use when Use it when an industry's costs or practices have been stable for a long time without obvious justification, when you are entering a field as an outsider and lack the incumbents' assumptions, or when repeated incremental attempts have all failed and the problem may be framed wrongly.
Avoid when Avoid it for routine decisions — it is slow, effortful, and analogy is right most of the time. It is also a common vehicle for arrogance: reasoning from first principles while lacking domain knowledge tends to rediscover why the convention exists, expensively. If experts cannot explain why a practice exists, that is worth investigating; if they can, listen.