How do you prioritize and test your product features using the Kano model?

1 What is the Kano model?

The Kano model, developed by Noriaki Kano, a Japanese professor of quality management in the 1980s, categorizes product features based on how they affect customer satisfaction. It assumes customers have different expectations and preferences for different features, and not all features are equal. The model identifies five types of features: must-be features that are necessary but not sufficient to satisfy customers, one-dimensional features that have a linear relationship with satisfaction, attractive features that increase satisfaction when present, indifferent features that have no impact on satisfaction, and reverse features that decrease satisfaction when present. For example, a car must have brakes and a steering wheel (must-be), its speed and fuel efficiency are one-dimensional, a sunroof or heated seats are attractive, its color or shape of the dashboard are indifferent, and loud noise or high maintenance cost are reverse.

2 How do you use the Kano model?

The Kano model can help you prioritize and test your product features by following a few steps. Begin by identifying potential features through customer interviews, surveys, feedback, reviews, competitors, market trends, etc. Then, survey customers on a five-point scale from "I like it" to "I dislike it". Plot the results on a Kano diagram to determine which category each feature falls into. Calculate the satisfaction coefficient for each feature too. Rank the features according to their importance and desirability. Focus on delivering must-be features first, followed by one-dimensional features and attractive features. Avoid or eliminate indifferent and reverse features. Finally, use lean startup methods such as prototyping, MVPs, experiments, A/B testing to measure the impact of your features and monitor how they change over time.

4 What are the limitations of the Kano model?

The Kano model is not a perfect tool and has some limitations that you should be aware of. It requires a large and representative sample of customers to get reliable and valid results, which may be difficult or expensive to obtain. Additionally, it may not capture the complexity and diversity of customer preferences, as customers may have differing opinions or emotions for the same feature, or their preferences may change over time or in different contexts. Furthermore, it may not account for the interactions and trade-offs between features, as customers may value some features more or less depending on the presence or absence of other features. Finally, it does not necessarily reflect the actual behavior and outcomes of customers, as they may say one thing and do another, or be unaware of their own needs or wants.