The foundation of marketing strategies, initiatives, and launches is consumer behavior. Before beginning design and manufacturing, marketing teams in businesses concentrate on the customer’s preferences and the aspects that attract them.
Let’s examine the Kano model, its background, the characteristics that make it effective, and the instances in which it may be used in this article.
Together, the teams for marketing and products examine the data gathered in the form of models called priority frameworks. There are several models that organizations have adopted. The Kano model is one such example.
What is KANO model?
The Kano Model, pronounced “Kah-no,” is a method for prioritizing features on a product roadmap based on how likely they are to please customers. To assess if adding a highly-rated item to the roadmap is a wise strategic move, product teams might compare its implementation costs with its use costs.
The Kano model may give a detailed grasp of a customer’s demands. The voice of the customer table may be used to translate and modify the generated verbatims, which becomes an excellent input for a quality function deployment (QFD) House of Quality.
Specifically, the model has two dimensions:
- Achievement (the horizontal axis) ranges from the provider not doing it at all to the supplier performing it well.
- Satisfaction (the vertical axis) ranges from complete unhappiness with the product or service to complete satisfaction with the product or service.
How KANO model works?
It’s time to discuss what it means to utilize the Kano model with numerous users and features now that we have a fundamental knowledge of how it works.
This section is based on several stories of practitioners and researchers using the Kano model, who have shared their experiences and key takeaways at each stage of the process:
- Selecting attributes and users for analysis;
- Obtaining the best information from clients;
- Analyze the outcomes.
Selecting attributes and users for analysis
The first thing to consider is the breadth of your investigation in terms of features and users.
Choosing features
Choose features that provide substantial benefits to the user. Your backlog may comprise technical debt payment, a sales/marketing item, a reporting system, or a design refresh. Kano doesn’t cover these.
Although products are greater, we can gauge customer contentment through external factors. Research conducted using Kano will be detrimental to your team, customers, and yourself if you need numbers to defend against not complying with a request from an internal stakeholder.
If you’re using volunteer participants, restrict the number of characteristics in your survey. This should boost participation and focus.
Selecting customers
You must consider some demographic, logical cohort, or persona to which the consumers (or prospects) you choose to participate in your survey belong. If not, your data will probably be dispersed widely.
Your client or prospect base is probably not uniform, and neither will their opinions on your feature. But you may significantly lower the noise in your research if you consider a category to which they belong.
Obtaining the best information from clients
The sole approach you used to contribute to the Kano research was the questionnaire and how you presented it. Therefore, you should make sure that this phase is as successful as you can make it.
Clarify questions
You should ask straightforward, concise questions. Each should represent one trait. If the feature is complicated, break the query down.
Your inquiries should focus on user advantages, not product capabilities. How would you feel if you could automatically enhance your photo?
Avoid polar question pairings. The dysfunctional question isn’t the reverse of the functional one; it lacks functionality.
Instead of describing features, demonstrate them.
Better than asking direct questions is showing the consumer the functionality and asking how they feel about it.
Instead of a written query, you may offer a prototype, interactive wireframes, or mockups. The consumer may better grasp what’s being suggested with this visual and dynamic “explanation.”
If you inquire this way, ask for conventional answers after the user interacts with the feature prototype, like a detailed text query. This will help them remember your survey’s elements without confusing them.
Pay attention to phrases and comprehension.
Some individuals are puzzled by Kano’s response order. “I like it that way” seems gentler than “It must be that way.”
The responses are ordered from pleasure to displeasure avoidance. Alternative word choices include:
- I like it that way.
- I anticipate that it will be a fundamental requirement.
- I’m unbiased.
- I don’t like it, but I can deal with it.
- I dislike it and cannot tolerate it.
You need to be cautious of how these alternatives are perceived and ensure respondents grasp the purpose of the questionnaire. Selecting the best responses and conveying them to participants should improve outcomes.
Ask the customer about the feature’s significance.
Multiple teams have recommended adding additional questions following the functional/dysfunctional pair. Customers are asked how significant a feature is.
This information helps differentiate features and determine which are most important to customers. It lets you distinguish between primary and minor characteristics and how they affect consumer choices.
Examine your questionnaire.
If feasible, review the questionnaire with a few of your team members before distributing it to your clients. Speaking with individuals from the outside would undoubtedly cause any internal uncertainty if there is any.
Analyze the outcomes
Now you reach the study’s motivation. After tabulating and analyzing data, you may classify characteristics and prioritize them. You may explore 2 types of analysis: – discrete and continuous. Both are mathematical notions that link participant answers to the Kano categories. Each method depends on the sort of insight you want.
Discrete Analysis
The most straightforward approach to analyzing the Kano results is to:
- Sort respondents based on the demographic and persona traits that best describe them.
- Using the Evaluation table, classify each respondent’s responses.
- Add all the replies for each category’s characteristic (and demographic).
- The most common answer (i.e., the mode) will be for each feature’s category.
- Use the leftmost wins rule when there are close outcomes among categories: Must-have > Performance > Beautiful > Uninteresting.
- If you asked respondents to rate the significance of certain features, you should average their responses if you did.
This kind of analysis provides you with a basic level of knowledge. It is helpful in many situations when a more thorough approach is unnecessary (e.g., testing design ideas or making a rough draft of your roadmap).
Continuous Analysis
The discrete analysis has a few problems, but it’s a fantastic place to start and gives a general understanding of the outcomes. Namely:
- In this process, we lose a lot of information. The first step was to assign each respondent’s 25 possible answers to one of six groups. The responses from each responder are then combined into a single category for each characteristic.
- The variation in data is entirely unknown; softer responses are given the same weight as tougher ones. Consider an attractive person with a dysfunctional “expect it” vs. a “live with it” attitude.
Score answers
First, each response choice is given a potential satisfaction value between -2 and 4. The higher the number, the more the client wants the function. Importance is graded 1 to 9 like previously.
Functional: -2 (Dislike), -1 (Live with), 0, 2 (Must-be), 4 (Like);
Dysfunctional: -2 (Like), -1 (Must be), 0 (Neutral), 2 (Live With), 4 (Dislike);
Importance: -1 (Not Important), 9 (Extremely Important).
You may find the Dysfunctional scale backward. Don’t higher scores reflect more pleasure? In Dysfunctional responses, Disliking denotes strongly disagreeing with the feature’s absence. Inclusion would increase satisfaction. Hence, it receives a higher score.
The rationale for the asymmetrical scale (beginning at -2 instead of -4) is that the categories you obtain from negative replies (Reverse and Questionable) are weaker (Must-be and Performance).
These scores will categorize the characteristics of a 2-D plane. With this strategy, no evaluation table is needed.
Suppose a characteristic turns out to be Reverse. In that case, you may always define it as the opposite and swap the Functional and Dysfunctional scores to classify it into a different Kano category; alternatively, you can remove it from your research.
Conclusion
The KANO model is a structured prioritization methodology for product teams. The framework helps in prioritizing the features they believe will please the clients.
These approaches demonstrate their effectiveness in this challenging marketplace, where items compete for shelf space and consumers’ attention. Even before the product enters the development phase, the kano quality model strives to provide clarity on the investment in features, time frame, and resources needed.
The KANO model case study demonstrated how to launch a new product while working under limited resources and timing constraints.
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