Kano Model Analysis Calculator
Turn raw Kano survey answers into a ranked feature plan. Enter each functional and dysfunctional response with an importance rating, then see which features are basic must haves, performance drivers, delighters or wasted spend.
🎯 What this calculator does
The Kano model sorts customer requirements by the effect they have on satisfaction. Some features only hurt you when they are missing. Some lift satisfaction in a straight line. Some delight people who never asked for them. This calculator reads your raw survey responses and works out which is which.
You enter one row per respondent per feature. Each row holds the functional answer, which is how the customer feels when the feature is present, and the dysfunctional answer, which is how they feel when it is absent. The calculator applies the standard Kano evaluation table to every response, tallies the categories per feature, then converts the tallies into Better and Worse coefficients.
The output is a funding decision. You get a dominant category for every feature, the strength of agreement behind it, and a ranked action for each one. Nothing is entered as a percentage. The tool does the counting so your data stays raw and auditable.
📐 The formula
Better coefficient, in words: the share of customers who feel more satisfied when you supply the feature.
Better = (A + O) / (A + O + M + I)
Worse coefficient, in words: the share of customers who feel dissatisfied when you fail to supply the feature. It is shown as a negative value.
Worse = -1 x (O + M) / (A + O + M + I)
Reverse and Questionable answers sit outside the denominator because they are not valid satisfaction signals.
🧮 The Kano evaluation table
Every pair of answers maps to one category. Read the functional answer down the left, the dysfunctional answer across the top.
| Functional answer | Dysfunctional: like | Dysfunctional: must be | Dysfunctional: neutral | Dysfunctional: live with | Dysfunctional: dislike |
|---|---|---|---|---|---|
| I like it | Q | A | A | A | O |
| It must be that way | R | I | I | I | M |
| I am neutral | R | I | I | I | M |
| I can live with it | R | I | I | I | M |
| I dislike it | R | R | R | R | Q |
M is Must-Be. O is One-Dimensional. A is Attractive. I is Indifferent. R is Reverse. Q is Questionable.
📋 How to use it
- Pick 4 to 8 features you are considering funding. Write them in customer language, not project language.
- Survey your customers with the paired question format. Ask the functional question first, then the dysfunctional question, for every feature.
- Add an importance rating from 1 to 10 to each response so the calculator can weight the portfolio result.
- Open the Calculator tab and add one row for each respondent answer. Use the same feature name every time so the tool groups the rows correctly.
- Load a sample dataset first if you want to see how a completed analysis reads.
- Click Analyse responses. The tool groups by feature, applies the evaluation table and calculates the coefficients.
- Read the Results tab. Check the category strength column before you act on any single feature.
- Download the Excel workbook and take it to your funding review.
📊 How to read the categories
| Category | Signal | Typical coefficients | What you do |
|---|---|---|---|
| Must-Be (M) | Absence causes anger, presence earns nothing | Low Better, high Worse | Meet it reliably. Never over invest past the threshold. |
| One-Dimensional (O) | More of it means more satisfaction | High Better, high Worse | Invest and measure. This is where competitive gaps show. |
| Attractive (A) | Delights when present, no penalty when absent | High Better, low Worse | Use to differentiate. Expect it to decay into Must-Be over time. |
| Indifferent (I) | Customers do not care either way | Low Better, low Worse | Stop funding it. This is the fastest saving in the list. |
| Reverse (R) | Customers prefer the feature absent | Excluded from coefficients | Remove it, or make it optional and off by default. |
| Questionable (Q) | Contradictory answers | Excluded from coefficients | Rewrite the question and re-survey that feature. |
⚠️ Common mistakes
- Running the survey on your project team. Kano only works with real customers.
- Wording the dysfunctional question as a negative of a negative. Customers misread it and you generate Questionable results.
- Acting on a dominant category that only 40 percent of respondents agreed with. Low category strength means you have segments, not a single answer.
- Treating Attractive features as permanent. Free wifi was a delighter once. It is a Must-Be now.
- Ignoring Indifferent features. Every one you keep funding is money that could go to a One-Dimensional feature.
💡 Pro tips
- Aim for 20 to 30 respondents per customer segment. Below 20 the category counts get unstable.
- Run the analysis separately for each segment when category strength drops under 50 percent. Two segments averaged together produce a category that fits nobody.
- Plot Better against Worse before you argue about priority. The chart settles most debates in one glance.
- Fix every Must-Be gap before you fund a single Attractive feature. A delighter cannot rescue a broken basic.
- Re-run the survey every 12 to 18 months. Categories migrate in one direction, from Attractive to One-Dimensional to Must-Be.
- Pair Kano output with your CTQ tree so each funded feature carries a measurable specification.
🔎 Where this sits in DMAIC
Kano belongs in Define. You use it straight after Voice of Customer collection and before you build the CTQ tree. It answers the question that project charters usually skip, which is whether the requirement you are about to improve actually changes how customers feel.
It also earns a second run in Improve. When you have several solution options on the table, categorising them stops the team gold plating features that customers rate Indifferent.
🚦 One rule that saves projects
Must-Be features are pass or fail. Performance above the threshold buys you nothing, so a Must-Be feature scoring 99 percent instead of 95 percent is not a win. Spend the difference on One-Dimensional features where every point of performance converts to satisfaction.
📝 Enter your survey responses
Each row is one respondent answering one feature. Repeat the feature name across rows so the calculator groups them. Importance is that respondent's rating of how much the feature matters, from 1 to 10.
📦 Load a sample dataset
Reading the answer options. Functional means the feature is present. Dysfunctional means it is absent. Ask both questions of the same person before you move to the next feature.
| # | Feature or requirement | Functional answer (present) | Dysfunctional answer (absent) | Importance 1 to 10 | Action |
|---|
✅ Before you analyse
Check that every feature carries at least four responses. Fewer than that and the dominant category is a coin toss rather than a finding.
No analysis yet
Load a sample dataset or enter your own responses on the Calculator tab, then click Analyse responses.
