Kano Model
“Noriaki Kano's framework classifies features by their effect on customer satisfaction.”
Your backlog is full and everything's marked high priority. The team argues about what to build next based on gut feel. Nobody's asked whether customers even care about half of it.
The Kano Model is Noriaki Kano's feature classification technique. You categorise each feature by its effect on customer satisfaction — must-be (expected), one-dimensional (more is better), attractive (delighters), indifferent, or reverse (actively unwanted).
Its unique strength is separating what customers expect from what genuinely excites them — a distinction backlogs don't make. Reach for it when prioritisation debates have stalled and everyone's treating features as equally important. The model needs real customer data from its paired questionnaire; assumption-based classification defeats the purpose.
Categories also drift over time — today's delighter becomes tomorrow's baseline expectation, so a Kano map has a shelf life. Skip re-evaluation and you'll be polishing features nobody notices while ignoring the ones that would actually shift perception.
Your next move: Which feature on your roadmap is being defended as a 'delighter' that customers have quietly come to expect — and what's actually delighting them now?
What it looked like for them
Eva Horner and Joshua Ariga, CarePay, post-2020. CarePay's UX team ran a Kano survey on proposed features for their healthcare insurance browsing platform. The results rearranged the team's assumptions. "Price filtering" — a feature the team had assumed would be important — was reclassified as Indifferent.
Users didn't care about it the way the team expected. Meanwhile, direct customer support access and customer reviews emerged as Performance features — the ones where more investment directly increased satisfaction.
The team reported that the survey "really made us reconsider which features to prioritise on our roadmap." A secondary finding sharpened the method: removing a handful of vague survey responses shifted nine features from Questionable to clear categories, demonstrating how sensitive Kano classification is to data quality.
The roadmap changed. Not because the team was wrong about what they could build, but because the model surfaced what users actually valued — and the two lists had less overlap than anyone in the room had assumed.
“A stakeholder keeps changing their mind and I can't lock the scope.”