From customer’s needs to concrete targets

Classifying customers’ needs and translating them into measurable targets with the Kano Model plus Quality Function Deployment followed by a Help Desk example with these tools.

“Everything starts with the customer “ - Louis V. Gerstner Jr.

Abstract:

• The five categories of customer preferences from the Kano Model were addressed: must-be, one-dimensional, delighter, indifferent and reverse quality;

• A Kano Diagram and a House of Quality were illustrated for the case study of a fictional Help Desk;

• With the help of the Kano Model we can understand how customers are affected by different attributes. These can then be further processed into the House of Quality and translated to measurable targets that, if met, should lead us to fulfill customer’s expectations.

Good changes that become improvements are usually the ones that costs are reduced while quality is secured or, quality is improved with no cost added or, where the changed product increases customers’ expectation. The best changes are the ones that address all these three dimensions. Of course, excelling customers' experience with your product or service, which is usually reflected in the price they are willing to pay, is a bit challenging. First step to start working with this third dimension is to identify it.

Classifying Voice of Customer:

The process of organizing customers’ needs and their relationships was already addressed in the previous text with the help of Affinity Diagrams. We can use the same tool to rank these “voices” (e.g. by voting) or get such prioritization directly from interviews/workshops with customers. Beyond the importance given by us or customers on each of these qualitative items, we can use other frameworks to classify them and then translate these into measurable requirements.

Although there are several metrics and classification practices for customers’ preferences, a simple and effective way to start such analysis is by using the Kano Diagram. This model for processing and classifying customer “voices” was developed in the 1980s by Professor Noriaki Kano. There are five categories of customer preference:

Basic / Must-be Quality – something that is expected. That’s when someone says that you did no more than your obligation when you meet this requirement. If this aspect is covered, no reaction should be expected from the customer. In contrast, if we don’t meet this demand, a relevant negative impact should be expected. It is like expecting bread and butter for breakfast in a hotel.

One-dimensional Quality – the more, the better. Customer satisfaction increases or decreases (proportionally) as this attribute is met or not. Several dimensions in service and industry follow such patterns such as productivity, costs and efficiency. To follow our previous example in a hotel: this would happen with the variety or quantity of breakfast items offered.

Delighters - the competitive edges. Customer satisfaction will usually not be affected if you don’t meet such attributes since they were not expected but, if you add these features to your product or service, the customer will be delighted. This would be a deluxe but not expensive breakfast.

Indifferent Quality – neither good nor bad. Customers' satisfaction is not relevantly affected whether this attribute is met or not. In our hotel breakfast, this could happen with highly polite / formal communication from the personnel.

Reverse Quality – it depends on the customer. When excelling the requirement may result in dissatisfaction or some customers perceive it differently. It would be like finding out there is live music when you get into the breakfast room.

Considering the fictitious example of customers’ needs processed with Affinity Diagrams in the last text, you can find in the next picture how we could classify the voices in the Kano Diagram. One can use actual detailed voices in this framework as well.

It is interesting to notice how the attributes change in time and location. For example, free Wi-Fi was a delighter in the past but is expected today in restaurants and coffee shops, the automatic gear shift is still a delighter in Brazil but it is a Must-be quality in the USA, and traditional dishes are must-be in their home countries but delighters elsewhere.

Once we understand these classes, we can process the result of customers’ surveys and/or their related Affinity Diagrams, into the Kano Model, to understand how the requirements are affected and then how we should address them.

Translating voices into measurable requirements:

The next challenge is translating these subjective items to concrete requirements. A powerful tool to help us on this is the Quality Function Deployment, also known as the “House of Quality”. This brings a visual format to link the project team’s effort to Voice of Customer. It results in literal translation of these voices and their given importance into critical functional requirements. It can be used for other “translation” processes such as turning these requirements into product specifications and later into process specifications.

After listing customers’ needs, we must determine their prioritization ideally directly from customers or from any scale (e.g. ranking from Affinity Diagrams). Later, we complete the Planning Matrix, where we benchmark ourselves with competitors and the voice of customer. Then we identify functional product requirements (usually our experts know the typical dimensions that drive our product). Next step is determining relationships between customers’ needs and functional product requirements. We can, at this point, calculate priorities for these requirements. Later, we must identify technical correlations (relationships between the requirements). After determining competitive benchmarks (gathering from the market how our competitors are performing in requirements we listed), we can set our targets for these requisites.

A fictitious example of the Quality Function Deployment applied to our Help Desk case:

Conclusion

With the help of the Kano Model we can understand how customers are affected by different attributes. These can then be further processed into the House of Quality and translated to measurable targets that, if met, should lead us to fulfill customer’s expectations.

In our example, we would probably conclude that quality is not our big challenge but flexibility is. Besides competitors being better at this dimension, the customer feels this as a delighter. Priorities would probably be taken in optimizing First Time Response and minimizing the difference of resolution rate among analysts’ cells (e.g. with best practices analyses and standard work tools).