Not every feature earns its keep the same way. Sort what you build into five buckets of satisfaction, and stop spending scarce effort where it can't move the needle.
The Kano Model, developed by Japanese quality researcher Noriaki Kano and published in 1984, sorts every feature or improvement you could make into five categories, based on how it actually moves customer satisfaction, not how much work it took to build.
Must-be: expected basics. Invisible when present, infuriating when missing. Performance: the dial you compete on — more is better, and customers notice the difference. Attractive (delighters): unexpected extras that create loyalty, but nobody misses them if they're gone. Indifferent: nobody cares either way. Reverse: some customers are actively annoyed the more you add it.
The practical payoff: most teams waste effort either polishing must-be features nobody will ever thank them for, or piling on indifferent features nobody asked for, while starving the one or two delighters that would actually create word-of-mouth. Kano gives you a sorting test before you spend the next hour, sprint, or dollar.
Each step below shows one category with a real example, then asks you to categorize a new example yourself. Click a step to open it.
Features customers assume will be there. Meeting the expectation creates no extra satisfaction — it just avoids anger. Missing it is a crisis.
Your turn: A banking app that doesn't lose track of your balance — which category?
The attribute where "more is better" and customers can feel the difference along a spectrum. This is usually where competitors fight head-to-head.
Your turn: A food delivery app's estimated delivery time — which category?
Unspoken, unexpected extras. Nobody asked for them, nobody misses them if they're absent, but when they show up, satisfaction jumps disproportionately — and people tell their friends.
Your turn: A tax-filing app that automatically finds a deduction you didn't know about — which category?
Attributes customers simply don't notice or care about, whether present or absent. Building these is pure cost with no satisfaction return — the single most common place teams waste effort.
Your turn: Letting users pick between 40 shades of the same accent color in a project-management tool — which category?
The attribute some customers actively dislike more of. Over-investing here doesn't just waste effort — it actively drives satisfaction down for part of your audience. This is why "no such thing as too many features" is false.
Your turn: A news app that sends you 15 push notifications a day — which category for most users?
Kano's method is a paired survey question for every feature under review:
| Question type | Wording |
|---|---|
| Functional | "How would you feel if the product had this feature?" |
| Dysfunctional | "How would you feel if the product did not have this feature?" |
Each question gets a five-point answer (I like it / I expect it / I'm neutral / I can tolerate it / I dislike it). The combination of the two answers places the feature into must-be, performance, attractive, indifferent, or reverse. The method was introduced with co-authors Seraku, Takahashi, and Tsuji in Kano's 1984 paper on attractive and must-be quality, and is still often paired with Quality Function Deployment to turn the categorization into a prioritized roadmap.
Must-be: the app doesn't lose your data and logs you in reliably. Performance: page load speed, API response time. Attractive: an AI assistant that drafts your next email before you ask. Indifferent: a theme with 12 accent colors nobody picks past the default. Reverse: a daily "tips" popup that power users learn to resent.
Must-be: a clean, quiet room. Performance: check-in speed, Wi-Fi reliability. Attractive: a free upgrade with a handwritten welcome note. Indifferent: a 24-page in-room menu of spa services most guests never open. Reverse: constant upsell calls to the room phone.
Must-be: correct diagnosis and medication, on-time appointments. Performance: wait time in the office, clarity of billing. Attractive: a same-day callback from the doctor after a tricky visit. Indifferent: the magazine selection in the waiting room. Reverse: an automated reminder system that calls three times a day.
Must-be: the syllabus matches what's actually taught, grades post on time. Performance: how clearly the instructor explains hard concepts. Attractive: a surprise guest practitioner session tying theory to a real case. Indifferent: a fourth optional font option for lecture slides. Reverse: mandatory daily quizzes that start to feel punitive.
Must-be: the brakes work every time. Performance: fuel efficiency, acceleration. Attractive: a seat that remembers your exact position across two different drivers. Indifferent: a rarely-used third drive mode nobody switches to. Reverse: an overly chatty lane-assist that beeps at every lane change.
Must-be: replying to a client email within a business day. Performance: how clearly and concisely you write. Attractive: referencing a detail from the client's last message that shows you actually remembered it. Indifferent: the exact font of your signature block. Reverse: cc'ing five extra people "just in case," which some recipients read as distrust.
Skip the Kano Model when a must-be feature is actively broken in production — that calls for an incident response, not a categorization exercise. Skip it when you have no access to real user feedback; categorizing from guesswork alone creates false confidence rather than insight. Skip it for decisions governed by contractual or regulatory requirements, since those are must-be by definition and the analysis adds nothing. And skip it, or at least hold it loosely, in the earliest pre-product-market-fit stage, when the customer segment itself is still unsettled and there's no stable baseline of "expected" to measure against.
Answer the three fields below, and this will assemble a ready-to-use prompt you can run yourself, or hand to an AI assistant, to sort a real decision into Kano's five categories.
This turns the Kano Model into a portable instruction set you can keep and reuse with any AI assistant, or hand to a teammate as a quick-reference.
Describe a real decision you're facing, and this builds a prompt that applies the full skill to it.