Frequently Asked Questions (FAQs)

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What is the Kano model? What research scenarios is it suitable for?

The Kano model is a product development framework developed by Dr. Noriaki Kano in the 1980s to help companies understand and prioritize product features. Based on customer needs and preferences, it categorizes product features into five types: basic features, performance features, excitement features, indifferent features, and reverse features. It is suitable for scenarios such as product feature prioritization, service design optimization, user experience improvement, product roadmap planning, and customer satisfaction enhancement, and is especially useful for research that needs to understand the degree to which features affect customer satisfaction.

What do the five function types in the Kano model represent? How should they be understood?

The five function types and their meanings are: 1) Must-be quality: the basic functions customers expect a product to have; if missing, they will be dissatisfied, but having them does not necessarily increase satisfaction; 2) Performance quality: has a linear relationship with customer satisfaction; better performance increases satisfaction, while worse performance decreases satisfaction; 3) Attractive quality: functions customers do not expect, but whose presence significantly increases satisfaction; an important source of product differentiation; 4) Indifferent quality: whether present or not, they have no significant impact on customer satisfaction; 5) Reverse quality: functions that, when present, instead reduce customer satisfaction and may be seen as unnecessary or annoying. Understanding these types helps with product feature prioritization and resource allocation.

How do I create Kano model questions? What should I pay attention to?

Steps and precautions: 1) Select the "Kano" question type to add it to the survey; 2) Enter the names of the functions or services you want to study, one per line. These names will become the questions; 3) The system will automatically generate the default Kano model format options, including a 5-point scale and two question lines: How would you rate it if this feature exists/is missing? 4) You can adjust question settings such as required/optional, display logic, skip logic, grouping settings, etc. Precautions: feature names should be clear and specific, and avoid vague descriptions; make sure the feature description is understandable to participants; consider the relative importance of the features and avoid including too many indifferent features.

In what situations should the Kano model be used instead of other question types?

The Kano model is recommended in the following situations: 1) when you need to understand the different types of impact product features have on customer satisfaction; 2) when the research goal is to identify product differentiation opportunities and competitive advantages; 3) when you need data support for prioritizing product feature development; 4) when analyzing changes in customer expectations and feature maturity; 5) when it is used for user experience research in software products, mobile apps, website design, service processes, and similar areas; 6) when you need to balance the maintenance of basic features with the development of exciting features; 7) when developing a long-term product roadmap and feature planning strategy.

How to interpret Kano model results? How to apply them?

Interpretation and application of the results: 1) KANO attribute: determine the function type based on the highest score to guide feature classification; 2) Better coefficient: measures the impact of attribute performance improvement on satisfaction, calculated as (Performance% + Excitement%)/(Basic% + Performance% + Excitement% + Indifferent%); 3) Worse coefficient: measures the impact of attribute performance decline on satisfaction, calculated as [(Basic% + Performance%)/(Basic% + Performance% + Excitement% + Indifferent%)]×(-1). Application recommendations: basic features must be guaranteed for quality, performance features need continuous improvement, excitement features are the key to differentiation, indifferent features can be considered for simplification or removal, and reverse features should be avoided. The results should be used in combination with qualitative research such as user interviews and competitor analysis.