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. Organisational benefits

Objective decision making

Authority and gut feeling lead to bad decisions

Last updated 9 months ago

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We've all been there: someone with more authority than you is adamant that their idea must be implemented. I've seen one executive force a product decision because his wife didn't like the initial idea.

It's not only authority that makes features appear on top of a roadmap. Most times, economics are in play.

Yet what's good for the company isn't always good for the customer. Hostility towards customers compounds. Short-term gains can lead to long term losses. Small cuts do almost nothing by themselves, but customers with a thousand of such cuts will leave.

A local telecommunications company of which I was a customer once introduced an extra section in their support app. Customers were presented with "engaging content from selected partners" in the app.

Economically, this makes sense. Suppose:

  • The content partner pays €.2 per click

  • The telecommunications company has 3 million customers who use the support app twice a month

  • 1% of app users clicks on the "engaging content".

That would mean the company can make € 12.000 per month from its content partner, just by adding a line of JavaScript to its webapp. Considering a support app is typically seen as a cost, this is a compelling idea.

But it doesn't feel right, does it? As a customer, I am irritated. But it's a small cut, and it won't show up in the management Excel files.

Feelings are a weak counter-argument to the hard numbers of economics. Except when you can turn feelings into data, and that's where the Kano model shines.

If someone were to survey a sample of the telecommunication company's customers and show them the "engaging content" in the support app, I'm sure they'd find that half of the customers would feel indifferent about it and the other half would hate it. That means that twice every month, 1.5 million customers are irritated with the app (and by extension, the company). Surely that's not worth €12.000 to the company. It'd cost a lot more than that to bring back these customers to more positive feelings.

The Kano model ensures that decisions are based on objective data. Gone are pet features and decisions that hurt customers. Turning feelings into data creates a balance between what's good for the company and what's good for the customer and that's what benefits the company the most.