Comparing by fit
Two ways of thinking, side by side. There is no winner here — read across each row and choose the one that fits your situation.
Innovation
Not all features are equal: some delight, some merely satisfy, and some only hurt when missing.
By 6days
Decision Making
Strip a problem back to what must be true, then rebuild — instead of copying what exists.
By 6days
When to use — Kano Model
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 to use — First-Principles Thinking
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.
When not to use — Kano Model
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 It is also expensive to run properly and stale quickly; a three-year-old Kano study is a historical document.
When not to use — First-Principles Thinking
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.
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.
Most reasoning is analogy: we do it this way because that is how it is done, because the incumbent does it, because we did it last time. Analogy is efficient and usually right, which is exactly why it is dangerous — it carries forward constraints that were real once and quietly expired. Whole industries hold assumptions nobody has tested in decades, because testing them was never anyone's job.
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.
First-principles thinking decomposes a problem to the things that must be true — physics, arithmetic, contract terms, hard constraints — and reasons upward from there, deliberately ignoring how the problem is currently solved. The hard part is not the rebuilding; it is telling a real constraint from an inherited convention. 'Water boils at 100°C at sea level' is a principle. 'Enterprise software is sold through channel partners' is a convention wearing a principle's clothing. The method is expensive and usually unnecessary — its value is concentrated in the rare cases where a load-bearing assumption is simply wrong.
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.
Worked example — Kano Model
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.
Worked example — First-Principles Thinking
A team is quoted £40,000 for a piece of lab equipment and treats it as fixed. Decomposed: what is it actually made of? A precision stage, a camera, a light source, a controller, an enclosure, and software. Priced as components, roughly £6,000. The remaining £34,000 is not physics — it is certification, low production volume, support, and margin, all of which are real costs but not laws. For a regulated clinical setting the £40,000 is justified and the analysis ends there. For an internal research rig that needs no certification, most of that gap is avoidable, and the team builds it for £9,000.
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.
The idea is ancient: Aristotle described reasoning from first principles, and the method underpins mathematics and physics as disciplines. Its modern circulation in business owes much to engineering culture and to founders who have publicly credited it for cost-structure decisions. It is common intellectual heritage rather than anyone's proprietary framework.
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.
Write the problem so that no current approach is embedded in the wording. 'How do we make our call centre more efficient' has assumed a call centre. 'How do customers get their question answered' has not, and the two questions have very different answer sets.
Write down everything you believe about the problem, especially what feels too obvious to state. The load-bearing assumptions are always the ones nobody thought worth writing down, because obviousness is what protects them from scrutiny.
For each, ask what would happen if it were false, and demand evidence for why it holds. Laws of physics, arithmetic, and binding contracts survive. Industry practice, precedent, and 'the customer expects it' usually turn out to be conventions that someone chose, under conditions that may no longer apply.
Construct a solution using nothing but the constraints that survived. Do not check it against the existing approach yet — the comparison will pull you back toward the familiar before the new idea has finished forming.
Set the rebuilt solution against the status quo. Often the status quo wins, which is a real and useful result: you now know why it is right rather than merely inheriting it. Occasionally the gap is enormous, and that is what the whole exercise was for.