A Research Method to Prioritize Features
In the process of refining the primary functionality of our product, the Marker Editor, a myriad of user feedback requesting numerous features surfaced. I wrestled with the reality that integrating these additional features would significantly amplify the cost, casting doubt on our ability to deliver the completed product on budget and within the projected timeline. This predicament underscored the utility of the Kano Model, a potent and flexible methodology that facilitates the decision-making process about which features a product or service should incorporate, grounded in the degree of customer satisfaction they generate.
Researcher, UI Designer
So, What is the Kano Model?
Kano Model is based upon the following premises:
Customers’ Satisfaction with our product’s features depends on the level of Functionality that is provided (how much or how well they’re implemented);
Features can be classified into four categories (Attractive, Performance, Must-be, and Indifferent);
You can determine how customers feel about a feature through a questionnaire.
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Now, let's dissect the four categories:
Performance features: These are features where having more is better. Customers appreciate their presence and are displeased when they're absent. These are often features that customers might imagine themselves based on prior experiences with other products or services.
Must-be features: If these are absent from our product, customers may express dissatisfaction. Over time, customers shift from merely tolerating these features to expecting them.
Attractive features: These are unexpected features that pleasantly surprise customers. They elicit a "wow" reaction, making our product seem both innovative and appealing.
Indifferent features: Customers react neutrally or with indifference to these features, rendering their development a lower priority.
Lastly, there are Reverse features: these provoke positive reactions when absent and negative ones when present. A flip of the Functional/Dysfunctional values helps identify the original category they belong to - be it Performance, Attractive, or Must-be.
Please note, contradictory responses indicate questionable features, which are usually excluded from the data analysis.
Kano-1 / Questionaire and the four functions
Kano-2 / Data board
After gathering enough responses, I could then proceed to the analysis step.
From each response (functional, dysfunctional and importance), calculates the discrete category, functional and dysfunctional scores;
Calculates each feature’s discrete and continuous Kano categorization;
Automatically stack ranks features based on potential dissatisfaction, satisfaction and importance;
Draws a scatter plot graph that shows each feature’s positioning, relative importance as well as data variance through error bars.
Which feature should be prioritized? Let's delve into the results from the Kano analysis. The Kano reaction graph, created based on responses from the questionnaire, plots features according to their satisfaction and functionality levels. From the graph, we can discern that features 1, 2, and 3 fall into the 'Performance Feature' category (P). These are desired features that enhance user enjoyment of the product. Meanwhile, feature 4 is classified as an 'Attractive Feature' (A), known to elicit excitement or delight in customers and thereby giving our product or service a competitive edge. Consequently, the optimization priority for these features should be in the following order: F3 > F2 > F1 > F4.
1. Always use data-driven customer advices
The comprehensive process of conducting a Kano Analysis has granted me a more profound understanding of customer feedback. Previously, we prioritized features based solely on the frequency of direct feedback or complaints from a handful of customers. While this method served its purpose, in developing a SAAS product, it's crucial to approach feedback from a more expansive and objective data standpoint. This ensures our optimization efforts are more precisely attuned to users' needs, making our work not only more effective but also worth the effort.
2. The importance of not-taking-it-all
Beyond incorporating user suggestions, it was essential to consider the perspectives of our stakeholders and development teams. I evaluated each feature based on our stakeholders' commercial strategy and discussed the feasibility and phases of development with our front-end engineers, given our limited resources. This led to the selection of the four features with the highest potential. Had I insisted on incorporating all user requests without these considerations, it could have led to an inability to complete any of them, or diverging from our product roadmap. This underlines the importance of factoring in not only user input but also stakeholder viewpoints and the overarching future of the product.