How we solve your business needs and end-user problems.
Design outcomes that are based on insights from data and real user feedback are the ones that look good and perform brilliantly. But how do I get there?
Ask lots of questions
What is the current state of your project?
Where do users encounter problems or challenges with a product or service?
What data is needed to understand these problems and challenges?
What design approach yields the most useful insights and impactful results?
How do organizations develop comprehensive solutions to their users’ problems?
Identify the best design approach based on business and team needs
Design approaches to be considered are: Genius Design, Standards-based Design, Data-driven Design, and Data-informed Design.
An individual design approach may be distinguished along two continuums. The first is the type of input that users provide: 1) user input based on degree of behavioral actions "what people do" versus 2) attitudinal feedback "what people say". The second is how user input data is obtained by: 1) qualitative, direct observation such as a usability test or user research, versus 2) quantitative, indirect observation such as a survey or an analytics tool. Qualitative data better addresses why or how to fix a problem; quantitative data better answer “how many” and “how much” questions.
Which approach nets the strongest results?
Genius design works best in new, unknown market segments. Over time, research-driven products are the most successful, but the design starts from a well-founded concept.
Standards-based design can provide a security blanket for built-in consistency and interface conventions, all at a lower cost outlay. But design is not paint by numbers; it still requires a designer's skill to avoid the conundrum that standards institutionalization also threatens a product's market differentiation.
Data-driven design creates a reliance on user data but can still miss the deeper “why” behind these users’ motivations. When iterative design and sprint cycles are coupled with this approach, the impact is compounded, increasing the risk of tunnel vision that creates blindness to fresh and divergent thinking.
Data-informed design yields the strongest results for design validation because it seeks more rounded input. Since designers are not users, designers need to understand the motivations of their users to ensure that they’re creating holistic experiences that solve specific user challenges. By collecting data about user needs, challenges, and opportunities, a designer can use their knowledge to determine the most impactful solution.
Of the design approaches above, data-informed design yields the most impactful results.
Quantitative, Qualitative, and Great Design
The strongest data-informed design approach has three major components: Great design practices, quantitative data and qualitative data.
Great design practices are the deliberate efforts to seek out the best inputs of creative design. It includes design thinking to create delightful experiences, the process of diverging and converging iteration as a method of continual improvement, and the maximizing user performance by accommodating strengths and weakness of humans.
To validate great design practices, use data inputs. It is important to consider all types of data (e.g. attitudinal, behavioral, qualitative, quantitative) when shaping a design solution, regardless of source. This data informs the designer, helping them paint a clearer picture of the user’s journey, and provides insights into the "why" behind the problem.
Among the sources for qualitative data are ethnographic field studies, usability tests, customer forums, and card sorting. Quantitative data includes web/app analytics, A/B or multivariate testing, surveys, and support tickets. Using both types of data better allows the designer to best understand the day-to-day experience of the user.
One critique of data-informed design is that using data and being innovative are contrasting perspectives. This challenge can be avoided by refraining from taking quantitative data (the "how many" and "how much" information) as the only inputs to determining a solution or course of action. By bringing in qualitative data (the "why" and "how to fix" insights) and looking across all user touchpoints, can the designer better understand the user’s actions and motivations. As a result, the designer has access to the "why" data necessary for solving the challenges the user is facing and is now able to move beyond best practices to find unique and valuable solutions.
Data-informed Design in Action
Any individual, team, or organization can develop a specific, tactical plan to implement data-informed design. The data-informed design process is a scalable cycle based on available time, skills, and resources.
Let's look at the repeatable steps of the process:
1. Define the problem
Data and user feedback more often describe symptoms rather than causes of underlying problems. Identifying the real problem requires open investigation and solutions may not be trivial or popular. Without clearly defining the problem or challenge, it is very difficult to validate the problem's root cause and to propose a solution to resolve the issue. Identify the users who are experiencing this problem. Establish a clear problem statement so that all stakeholders can be aligned on what is being addressed.
2. Identify a hypothesis
Propose the potential reasons for why the problem is occurring. Conduct a brainstorming session with team members who have diverse backgrounds, perspectives, and roles. Multiple perspectives will help the team consider all facets of the problem and increase the probability that a holistic solution will be discovered.
3. Collect data
The first step in data collection is to identify what data points will potentially provide insights into the root cause of the problem. Next, conduct a gap analysis to determine what data is currently being captured and what data needs to be captured. After this, select the data collection methods that will be used to obtain needed, relevant research information. Finally, the available resources (people, time, money, etc.) should be checked against the ideal data collection scenario. If there are conflicts, adjustments should be made to the plan to identify a best-fit solution.
4. Develop findings and insights
Once the desired data is collected, analyze it to determine the key findings and discover applicable insights. Begin the process by organizing and coding the data. Once coded, trends and themes will emerge; this will lead to the key insights that shed light onto the "why" behind user behavior and problems.
5. Construct an implementation plan
During the final step, compare the findings against the problem statement completed in step 1, then work with the team to develop an implementation plan. A good plan uses the findings to identify clear, actionable steps that will solve the problem or will improve the user experience. Evaluate each step of your implementation plan, ranking the impact and level of effort required for each action.
For any new problem identified, repeat this process.
Adaptable and moldable view of Activities and deliverables timeline across a multi-functional team consisting of UX and UI, Project managers and engineering teams
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