Data-Driven Design: Step-by-Step Guide
User’s needs and expectations change constantly as we witness the rapid growth of the digital era. The data-driven design has opened up many avenues for research to accurately understand audience demands, pain points, and interactions with a product.
What is data-driven design? How do you define a step-by-step strategy and its role and value for the business? We have been asking these questions the most, so Limeup experts leveraged their experience to create this comprehensive guide. Besides theory, you will also find helpful practical tips on collaboration, planning, and analyzing.
The core goal for a design team is to evaluate end-users’ expectations before they start creating a solution. Otherwise, there’s a risk in delivering a product that may be rich-in-feature, high-quality but still unnecessary for users, and unprofitable.
Keep reading this blog post to learn the point of design and techniques to collect information based on our 10+ years of experience in this industry.
What is data-driven design?
It is an approach designers utilize to achieve user-centered design by collecting and analyzing user details. There are a lot of various tools for a data-driven website design, such as surveys and research methods, A/B testing, dairy studies, analytics, customer interviews, etc.
Reviewing behavioral patterns, performance metrics, and user flow answers what people need, why they act in a certain way, and what drives them to do so. Making informed decisions instead of solely relying on inspiration or aesthetic vision helps to make profitable solutions and achieve business goals.
Moreover, processing the collected data will enable specialists to prioritize development tasks and increase the speed of product delivery on the market.
It demonstrates what analytics tools work and don’t bring any results so you can fix it while reducing potential waste and risks. You can establish such a practice in your company. Still, if you need such specialists and resources, you can use the services of UX design agencies in London.
Types of data to collect in data-driven design
Types of qualitative and quantitative data.
There are two main types to use: quantitative and qualitative. Each has its perks and benefits, but the best middle ground is gathering insights from qualitative data and reinforcing them with quantitative.
Qualitative data
This kind of statistic answers why users act in specific ways. It enables us to thoroughly understand potential product users’ motives, intentions, and attitudes. You can’t measure it in numbers as it’s subjective and collected through conversational user interviews. You explore their feelings and motivations from the perspective of the social sciences of sociology, anthropology, and psychology.
Example: When trying to explore a pain point that stops the whole user journey, analyzing quantitative data shows how many users ceased interacting with the interface, while qualitative explains why it happened, therefore allowing us to find informed solutions.
Pros:
- Better understanding of the product perception by users.
- More descriptive insights help to draw inferences.
- A clear view of the audience’s personal experience.
Cons:
- Limited generability and low representation of a broad audience, as they are sometimes conducted in terms of small selections.
- Implying subjective interpretation can be shaped by the interviewer’s biased point of view.
For now, let’s refer to the leading methods of qualitative data-driven design:
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User interviews are conducted with a target audience representative, where specialists discover their pain points, expectations, motivations, how they cope with their workflow, and what they want to achieve.
You must ask open-ended questions and can add follow-up questions to get a complete picture. Also, you can read their body language to see how it aligns with their responses.
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Customer interview stands for the opportunity to overview your product from the consumers’ point of view. Otherwise, a nuance differs from the user interviews—communication with those using the product.
You may think that customers and users are the same. Still, separate groups have different intentions and purposes for using your product. Buyers are those who purchase the product and pay its cost. For example, when a consumer product is targeted at children, parents are buyers, and children are customers.
- Ethnographic research is used when the specialists can observe how focus groups of potential users interact with products in the natural environment. The researcher gets to see customers in their context and identify new issues in real-life conditions.
- A dairy study is a type of survey in which participants must keep a diary to сapture their thoughts and describe experiences when using a product. Usually, such user research goes in terms of the set period. Therefore, researchers can see how interaction evolves with time.
- Usability testing is reliable if specialists want to gather qualitative data and assess interface usability and effectiveness. During the testing, the user tries to complete average tasks on the site page or app. The researcher monitors the whole process and can ask additional questions. It can improve some existing features or compare them with competitors.
Who should conduct these tests? It could be a UX designer, a developer, or a CEO. The main thing you need is a screen recorder, a microphone, and a plan for the interview.
Numerous businesses prefer to partner and devote analyzing data to experienced product design companies in the UK. This option is convenient because they provide competent specialists throughout the full-cycle design process.
Quantitative data
This data type answers the question of how much and is based on gathered numeric or statistics. As a rule, it’s collected on extensive samples and represents the whole population.
Quantitative data is always objective, and its format includes only closed-ended questions to turn one-word answers into numbers. It’s best to check our suggestions based on qualitative research methods.
Pros:
- Statistically accurate results that can be summarized for a broad population.
- Reduced risk of biased and subjective opinion through the design process.
- Standardized methods of measurement allow researchers to repeat the study.
- Better detection of cause-and-effect relationships and hypotheses estimating.
Cons:
- The lack of depth captures participants’ specific experiences or points of view.
- Limited context and no subjective interpretation of numerical data.
- Inflexible as conducted in terms of a particular methodology and measurements.
The main methods for quantitative data include the following:
- A/B testing is an experiment in which experts test two (or more) similar versions of the design with slight differences to see which one works better. The main point is to decide what you want to track first and what will indicate the most suitable variant. For example, creating a strong CTA form can increase the conversion rate, and banners can grow the CTR using personalized customization.
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Cards sorting is a kind of UX research that allows experts to see how people classify information. Participants have cards with different materials or topics, and their task is to divide them into groups.
After comparing results, you can conveniently sort out content for users on the website or app. It provides reliable information architecture for creating navigation, site maps, and more.
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Analytics stands for measuring metrics with Google Analytics, a popular method among web design companies in the UK. The tool enables tracking the product’s performance and evaluates how and when people interact with your solution. With an intuitive interface and robust functionality, it’s easy to observe analytics data like page visits, number of clicks, time spent on interaction, etc.
Сollaborating with the optimization specialist, a design team can receive regular reports and use them to set goals and define ways to achieve them.
- Multivariate testing has a core concept similar to A/B testing, but the main difference is that you can test more than two variations of one website page and multiple elements simultaneously.
If you want to test different styles of icons, headers, and progress bars, you can launch all of them and try all possible combinations at once.
4 steps to create a process of data-driven design
1 step: Define project requirements
Defining project requirements on different levels.
You have to set clear goals and assess the current situation to build your strategy for the design process. Reconsider your strengths and weaknesses to figure out how statistics can help you. Don’t set abstract goals; be specific and indicate exact numbers.
It’s essential to evaluate the required time for collecting data. Set the deadlines, which will help you assist in developing a plan. Ask yourself whether these goals are realistic and what kind of specialist should be involved.
When considering hiring one of the UX design companies for gathering data, write down your priorities and desired results before discussing your project. Here is an example of a well-defined goal that may start with “I want to”:
- Increase the landing page conversion rate to 15% with web analytics.
- Extend the time spent on the page to one minute.
- Reach a click-through rate of 4.23% on the CTA button.
- Boost newsletter signups by 10%.
2 step: Indicate the right area
Improvements to the navigation of the log in process.
Data-driven design requires resources and time, so choose the most appropriate field for the test before you start preparation. According to your business goals, prioritize segments that need to be improved.
This can be a customer registration form, onboarding, or purchasing process.
For example, if you need to reduce the number of clicks a user has to make to achieve his goal, work with the navigation section. Take into account the user’s needs and pain points. What are their areas of difficulty? What do they lack?
Gather quantitative and qualitative data you already have, it can be users’ feedback, analytics tools, ratings, or reviews. That will come in handy when comparing with later results.
3 step: Define the hypothesis
Steps to consider when defining an effective hypothesis.
A hypothesis is an assumption based on an observation that can become true in certain conditions. For example, the data collected concludes, “If I do this, that will happen.” You can adjust it to your needs in any possible way, there is no standard form.
Hypothesis building structures our thoughts and works similarly in the UX research process. You develop ideas or identify an issue, then, after data collection, start building a hypothesis to find the solution and prove it with insights in the second part to validate it.
A correct hypothesis should be:
- Concretized,
- Clearly defined,
- Testable,
- Matches with the purpose of research,
- Determines further actions.
For a quick example, if you add a search bar, users will be able to find products more efficiently. The content will be shared more often by adding social media share buttons.
4 step: Get started with testing
How to gather feedback from real or target users.
Each enterprise may have different politics due to data access, but collaboration between the design team and the analytics department is standard practice. It’s crucial to convey what form it should be transferred, how often, and what information type should be collected.
You will also need additional specifics for comparison. For example, when the user feedback states that there are 100 app visitors per day, to understand if this is a high or poor metric, compare it with your previous analytics and check your competitors or market standards.
Making such regular checkouts is crucial to achieving sustainable growth. Remember that data is just an indicator that should be used correctly. Avoid falling into the trap of endlessly checking performance.
Top 4 data-driven design tips
Test at least 5 users
Testing 5 users is a necessary minimum amount to start the usability test.
Nielsen Norman Group debulked the myth that usability testing can only be carried out on projects with significant budgets and audiences. In reality, the opposite is true: the best results were shown by user testing with no more than 5 participants.
If you can test more than 5, then make several surveys with 5 people each. Sounds contradictory, doesn’t it? Zero users give no outputs, but one provides a third of all the information you can learn about usability, and so on.
The truth is that the subsequent visitors will have a tiny percentage of non-repeatable actions. Therefore, the more people you add, the less new qualitative data you earn. Five users are enough for one usability test to draw informative conclusions.
You have to involve additional people to collect data if your website or an app has several different target audience categories. For instance, a flower delivery online shop has various target audiences. Testing 3-4 users from each type is recommended.
Investigate unusual patterns
High bounce rate that needs closer investigation.
This particularly applies to analyzing quantitative data when you use analytics tools and notice rapid drops or bugs in interaction. Such factors should be addressed because they may have different reasons, for example, poor optimization.
On the contrary, this signals that something is going wrong at this stage. The page may be too long to load, or the buttons need to be fixed. It’s essential to check and find the root of the problem. For example, using qualitative research would demonstrate user behavior in real-time so that you will see any possible bugs within the user interface.
Choose a strategy
Essential characteristics of a design strategy to create the most effective products and services.
Based on our experience as London design agencies with robust expertise, we can tell there is no standard solution for perfect data-driven design. Still, evaluating the collected information, you can choose among various creative strategies.
This tip will help you to define the touch points with the audience, predict the level of engagement and satisfaction, and more.
Represent the data
Variants of chart that may be used to visually represent the data.
There is no doubt that a data-driven design process brings valuable resources, and as a UX designer, you need to communicate this importance to others. 65% of people perceive visual information better, so putting it in graphs, diagrams, and maps is worth it.
This will make it easy for everyone and assist your team in making the right decisions. You can always show successful cases of companies with such an approach or add proven statistics.
- Companies with a data-driven approach are 23 times more likely to attract customers and 19 times more profitable.
- Organizations that actively use quantitative and qualitative data are 3 times better at decision-making.
You may also have noticed that many companies now display user information to make interesting personalized selections and recaps. This has become one of the UX design trends used by Spotify, YouTube Music, and many other platforms.
Conclusion
After reading this blog post, you can evaluate and implement the gathered data for more well-thought-out decisions. Although it cannot give you a creative approach or an aesthetic vision of future products, it shows how the user perceives it and what they truly need.
Looking back at the research, you can draw valuable conclusions that will drive further development. Furthermore, presenting data facilitates the conceptualization process, which is crucial for further development.