Prioritizing Features using Kano Model

The Product Backlog provides a collection of features that the Product should ideally implement. But not every feature has the same priority. Some of the features are more important than others and of course, the Product Owner doesn’t go around picking random features while prioriterzing the features.

There are various models and in this example, we will explore the Kano Model.

The Kano Model

As per Nariako Kano’s model, the features could be broadly categorized into 3 categories.

  • Thresholds/Must Have Features These represent the minimum set of features that should be present to meet User expectations. Improving the must have features beyond a limit would have little impact on the customer satisfaction. For example, for accommodation, a minimum requirement of User would be a clean room with basic ammenities.
  • Linear Features Features which increases customer satisfaction as it increases is known as Linear Features. This includes the size of the room or bed, freebies in the room etc.
  • Delighters Delighters on other hand are features which adds to the premium quality of product, often adding greatly to customer satisfaction. These could include private pools. These are features which the Customer might not quite miss if not present, but would be delighted if present.

Now that we have understood how we would like to group the features, the process of actually grouping them begins. As per Kano, this could be done by asking two questions per feature to the user group.

  • Assuming the feature is present, also known as Functional question
  • Assuming the feature is not present, also known as Dysfunctional Question.

The answers are questions are typically collected as

  1. I like it that way
  2. I expected it that way
  3. I am neutral
  4. I can live with it that way
  5. I dislike it that way

The answers could be mapped to 3 feature groups using the following table.

The answers from the user groups (typically 20-30 users) are aggregated and their distribution could be observed to determine the priority group.

The responses with high values are considered.

This was one approach for User Story Prioritization. In the next post, we will explore Relative Weighting approach.