Qualitative Data and Design Thinking
At Kai Analytics, our core specialty is analyzing qualitative data. Qualitative data is descriptive, language-based data that usually represents attributes, and is often more nuanced than its relative, quantitative data, which involves numbers and has data sets with unique values. In market research, qualitative data can be collected for many different reasons and often helps to improve products and services.
The mark of an ideal tool or service is a design so intuitive it becomes invisible. This transparency can require qualitative data as part of the design thinking process. Design thinking is a component of market research that is essential to creating new services and products or improving old ones, and in almost every step of the process is the opportunity to leverage qualitative data.
Good Design Requires Feedback
Imagine it’s the year 2200, and you’ve just started a safe and affordable jetpack rental service. You run an extensive marketing campaign, and initially potential customers seem genuinely excited. You must make several decisions related to this service: you locate your stores at the tops of buildings with a launchpad so it will be easy for your users to test out their jetpacks, and you include other recreational add-on equipment, because you believe most individuals will be using the service for fun. When it comes time for launch, only a few customers show up and your new business is operating at severe losses. What went wrong?
If your business involves creating something for wide consumption or serving people, it’s natural to want to ensure your services are perceived in the best possible light, and it’s a lot easier to do that if the service is fantastic to begin with rather than doing damage control with additional marketing because the service had serious flaws that were only discovered after it went to market. In keeping with our futuristic example, let’s imagine that before launching your jetpack service, you first go through the design process to ensure you’re on the right track:
1. Discovery and Problem Framing: Market researchers can collect feedback from customers or the intended audience of a product or service. This step is incredibly important because it can become very easy to make assumptions about what the problem really is or how users will benefit from a product, when the reality can be starkly different. Even customers may not realize what the problem is, but well-designed interview questions that produce data that can be examined on a large scale using natural language processing (NLP) can get to the heart of the matter.
In our example, you hold a series of interviews and focus groups with potential customers. You aggregate the qualitative data for analysis. If Kai Analytics were assisting in this project, our system using Unigrams would include tagging sentences for one or more themes: For example, someone mentioned that they’re worried about the cost of the service, so you could tag that as “pricing.” Each interviewee is also assigned a personality trait, such as their age range or experience with jetpacks. The data is in a table that compares the results of all respondents as well as each specific respondent, displaying both each person’s personality attributes and the themes they mentioned.
From the generated data table, the comparison reveals similarities and differences between certain personality traits which we use to create corresponding user personas. In this example, the analysis reveals three user personas: customers who want to use the jetpack purely for entertainment, customers who are concerned with safety and practicality, and customers who hope jetpacks will improve accessibility.
2. Ideation: Now you have feedback from customers, you need to come up with ideas, which could include forming a design team. If a group of your employees are tasked with brainstorming and other creative processes to come up with potential solutions, the result of their discussions becomes qualitative data. If only a couple people are coming up with ideas, there might not be much qualitative data for an analysis, but there is strength in numbers, and often the more ideas are generated the more options there will be to choose from.
Your jetpack service design team comes up with a wide range of ideas from safety levels to additional instructional classes. Not all the ideas are feasible, but that’s alright, as the point of these sessions is to explore all options. You also run a smaller set of interviews with potential customers and record their reactions to the most promising ideas. Customers across all personas seem particularly drawn to the idea of creating a simple but intuitive system of safety levels.
3. Prototyping and testing: Prototypes shouldn’t be rushed to market — Testing groups can provide feedback that can help determine whether the prototype does what it was designed to do. Once again, this is important qualitative data that can be analyzed with NLP to find out what core ideas the testing groups are coming away with. While customers are the most obvious source of feedback, employee and other stakeholder experiences are also incredibly valuable, especially in situations where employees regularly interact with customers.
You hold small demonstrations of what your store will look like and what the process for renting a jetpack will be. Customers love the new safety guides, but now they have some concerns about the location of your store. Employees also communicate what supports and training they will need for the service to succeed. Both positive and negative feedback is equally important, and an analysis can balance out different factors to point to key solutions.
4. Implementation: Qualitative Data from all previous steps of the creative process can be used to tell a story that will communicate the changes, improvements, and innovations made. If customers don’t understand how the product is supposed to help or why the service had to change, they may not recognize how it was shaped based off their own experience.
In our example, you use the data from all the steps to make a presentation showing how you took customers’ safety and accessibility concerns into account. Your stores will always be on ground level for safer jetpack testing and accessibility, and customers will know beforehand how they will be matched with the right jetpack for their level of experience and needs. Because it was based on real data from real users and you communicated your changes to the public, your service launch is a glowing success.
Product Design vs Service Design
Design thinking often falls into two categories: product design and service design. As you may have guessed, one revolves around the development of products and the other around the development of services, however, this distinction causes a ripple effect of small differences to their otherwise similar processes. Product design may involve everyone in the company on some level, but for some employees their only involvement may be crafting the final product in an assembly line. For service design, companies need to give more consideration to how the different people delivering the service will be organized, which may result in greater consideration for their feedback as part of the design process. Despite these differences, ultimately the process needs to be human-centered.
A Human-Centered Experience
Research and development can be expensive, and the solutions created may clash with what might seem more beneficial for the company, but the reality is that when products are user-friendly, everyone benefits. Public-facing employees will spend less time having to explain why a product is useful, and the company will save on costs related to dealing with backlash from a poorly designed service. Users won’t be aware of how much work went into making the service, because it will be so easy and painless that they won’t even think about it. In our jetpack example, it might seem like jetpacks are probably already expensive enough without the added cost of doing studies with future users, but if those future users reject the service because of poor design, launching a service doomed for failure would be much more costly.
The concept of service design hinges on the idea that it is worth the time, energy, and even organizational change to provide better products and services that will solve the true heart of a problem. While quantitative data based on facts and figures is also important, qualitative data can provide more nuanced, human-centered feedback that can lead to solving the problem in more creative ways, both now and in the future.
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Originally published at https://www.kaianalytics.com on April 18, 2022, by Lorina MacLeod.
