Framework library · Innovation and product

Kano model

The Kano model asks customers two questions about each feature: how they would feel if the product had it, and how they would feel if it did not. The pair of answers places the feature in a class. Must-be features cause dissatisfaction only when missing, one-dimensional features satisfy in proportion, attractive features delight when present and are not missed when absent, and indifferent features do neither. The classes show what to fix first and where to stand out.

LevelIntermediate
TimeA day to write and pilot the questions; two to four weeks to field a survey and classify the answers
Who to involveThe product owner and a researcher, with someone from customer care who knows what customers complain about.
Also calledKano analysis, Kano questionnaire, attractive quality, must-be quality, delighters and basics

Use it when

  • You have a list of candidate features for a new or revised product and need to know which are expected, which add value in proportion and which could set it apart.
  • Customers rate everything as important, and a ranking by stated importance does not separate the features.
  • You suspect the team is investing in features customers take for granted, or neglecting one whose absence drives complaints.
  • You want to track how a feature's class changes as the market catches up, so that today's differentiator does not become tomorrow's gap.

Avoid it when

  • Customers cannot picture the feature. People cannot say how they would feel about something they do not understand; show a prototype first.
  • The question is price or which segment to serve. Kano classifies features for a given segment; it does not price them. Use a pricing test or conjoint study.
  • You need to rank a long backlog by value and effort. Classify with Kano, then rank with RICE scoring.
  • You can reach only a handful of customers. With few answers the classes are unstable; use interviews to understand the job instead.

How to run it

  1. List features as customers would describe them

    Write each as a benefit the customer would recognise ("my number moves across the same day"), not an internal name. Ten to twenty is plenty for one survey.

  2. Ask the paired questions

    For each feature, ask how the customer would feel if the product had it and how they would feel if it did not. Offer the same five answers to both, from "I like it" to "I dislike it".

  3. Classify the answer pairs

    The evaluation table turns a pair into a class: attractive, one-dimensional, must-be, indifferent, reverse (they would rather not have it) or questionable (a contradictory pair). The register classifies the pair most respondents gave.

  4. Count the classes and read the coefficients

    Classify every respondent's pair and count each class. Better is the share of attractive and one-dimensional answers. Worse is the share of one-dimensional and must-be answers, and shows how much the feature's absence would hurt.

  5. Analyse each segment separately

    Business and consumer customers, or new and long-standing ones, often place the same feature in different classes. Averaging them hides the difference.

  6. Decide in class order

    Meet every must-be first, compete on the one-dimensional features that matter most, and add one or two attractive features to stand out. Drop indifferent features unless they are nearly free.

Work through it

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Mistakes to avoid

  • Wording the questions so they suggest the answer, which inflates the attractive class. Pilot the wording with five customers first.
  • Reading the classes as permanent. Attractive features drift towards must-be as rivals copy them, so repeat the survey every year or two.
  • Ignoring reverse answers. A feature some customers actively dislike may need to be optional rather than standard.
  • Trusting the class from the typical answers when the counts disagree. When the register warns of a mismatch, go with the counts.

Where it comes from

Noriaki Kano, Nobuhiko Seraku, Fumio Takahashi and Shin-ichi Tsuji, "Attractive Quality and Must-Be Quality", Hinshitsu (Journal of the Japanese Society for Quality Control) 14(2), 1984. The evaluation table and the better and worse coefficients used here follow Charles Berger and colleagues, "Kano's Methods for Understanding Customer-Defined Quality", Center for Quality of Management Journal 2(4), 1993. Source.

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