The complete guide to the Kano model
  • The complete guide to the Kano model
  • Why I wrote this guide
  • A short note on terms used
  • The value of the Kano model
  • The Kano model in a nutshell
  • Step-by-step guide to a Kano study
    • First rule of a Kano study
    • Gathering features
    • Designing your Kano survey
      • The art of formulating good questions
      • More on questions
      • Wording the answers
      • Test your survey
    • Administering your Kano survey
      • In person or online?
      • Selecting survey participants
      • Survey layout
    • Analysing the results of your Kano study
      • Classic Kano survey analysis
      • Continuous analysis
      • Validity and reliability
  • Applying your Kano study results
    • Prioritizing features
      • Prioritising by Kano category
      • Prioritising within categories
      • Prioritising by the value of a feature's presence and the cost of its absence
    • The product development lif
      • Understanding Kano categories to make the right decisions
      • Removing features
      • Identifying areas of improvement
      • The under-utilisation of the Reverse category
      • Disrupting conventions
    • Uncovering customer segments
    • Tracking the life cycle of customer attitudes and product features
      • The life cycle of successful product features
      • Other patterns
      • Customer satisfaction over time
    • Product communication
    • Organisational benefits
      • Objective decision making
      • Product process
      • Resource allocation
    • When not to use the Kano method
  • History of the Kano model
    • Genesis of the Kano model
    • Extensions to the Kano model
    • alternative-kano-methods
    • kano-model-critique
  • Appendices
    • appendix-i-answer-labels
    • appendix-ii-bibliography
  • Deleted
    • Preface
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  1. Applying your Kano study results
  2. Prioritizing features

Prioritising within categories

What about features that are in the same category?

Last updated 10 months ago

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When the Kano categories are clear-cut, prioritising is easy. If 9 out of 10 answers categorise feature A as a Natural and feature B is an Indifferent 9 out of 10 times, it's easy to make a decision about what features to work on first.

But what about features that fall in the same category?

Suppose you only have time to work on one of three features. And suppose these are the results of your survey:

Feature
N
O
A
I
Category
Priority

A

53

24

2

21

Natural

?

B

46

20

27

7

Natural

?

C

38

28

18

16

Natural

?

In the classic approach, you'd take on feature A first, because it has the most Natural answers. But feature A also has the most Indifferent answers. Is that really worth deprioritizing the other features for? (Actually, this kind of result also means you should look for , but let's start with prioritising).

Berger (1993) describes a way of ranking features based on their total impact on customer satisfaction. It's called the M>O>A>I rule. (I'll use Must-Be here instead of Natural, because that's the category label the researchers used).

You apply this ranking by listing categories by frequency:

Feature
Most frequent
Second most frequent
Third most frequent
Fourth most frequent
Priority

A

M

O

I

A

?

B

M

A

O

I

?

C

M

O

A

I

?

Then you rearrange the rows into groups according to the M>O>A>I order.

So the feature where Must-Be is the most frequent category gets listed first. But when two or more features tie, you look at the second most frequent category. If the second most frequent category for the first feature is One-Dimensional and for the second feature Atrractive, the first feature gets ranked higher than the second.

Using this rule, priorities are assigned thusly:

Feature
Most frequent
Second most frequent
Third most frequent
Fourth most frequent
Priority

A

M

O

I

A

2

B

M

A

O

I

3

C

M

O

A

I

1

C is the feature you should start working on first in this case. It has a less "Must-Be" answers than A, but its total impact on customer satisfaction is higher. The total of Must-Be, One-Dimensional and Attractive response categories is higher for feature C than it is for feature A.

Of course, if none of the tied features are in the Must-Be category, you apply the same rule, leaving out the category they're tied in.

In this example, all features are Attractive:

Feature
M
O
A
I
Priority

Feature A

2

24

53

21

?

Feature B

27

20

46

7

?

Feature C

18

28

38

16

?

Ranking means simply applying the M>O>I rule (because A is already the most frequent):

Feature
Most frequent
Second most frequent
Third most frequent
Fourth most frequent
Priority

A

A

O

I

M

3

B

A

M

O

I

1

C

A

O

M

I

2

Were you to use the classic approach and just count the Attract answers, feature A would have been at the top of your list. But because it has so few Must-Be answers, feature A is now at the bottom. Features B and C have more overall impact on customer satisfaction, so they must be prioritised.

segments in your customer base