The Ultimate Guide to Data Driven Design in 2022

We see the design process often as an art form, with intuition being the preferred method. Unfortunately, designers are unable to read the thoughts of their customers. As a result, using intuition may result in a design that is out of sync with the user’s requirements. Here is where data driven design comes in handy.

This data driven design method aids in the creation of a user-centric design and a better user experience. It allows you to make better design decisions based on actual data about the user’s behavior, attitude, requirements, etc.

However, there is still a lot of misunderstanding regarding data driven design and a lack of understanding about why it is essential.

To clarify things, we created this comprehensive guide to the data driven design process. This tutorial will explain what qualifies as data, what data driven design is, why it is essential, how you can utilize it, and the stages in adopting the data driven design process.

What Is Data?

When most people hear the term data, they immediately think of quantitative data in the form of numbers.

Data Is More Than Just Numbers

Data also includes qualitative data, which relates to emotions, opinions, and observations that numbers cannot represent.

To inform the design process, you may utilize both quantitative and qualitative data.

Numbers and answers queries (e.g. how many, how much, and how often) represent quantitative data. Quantitative data sources include A/B or multivariate testing, website analytics, heatmaps, and large-sample surveys.

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In contrast, qualitative data focuses on the why. It provides information on user motivation and purpose. Interviews, competition analyses, usability studies, focus groups, and diary studies help obtain qualitative data.

Both Kinds of Data Are Helpful Because They Complement One Another

Data serves as a tool to assist designers in producing the most incredible user experience possible. Designers may use facts to back up their intuitive decisions. They may also use qualitative data to better understand their consumers’ requirements and motivations in a better way and modify the design appropriately.

Designers do not need to start crunching statistics and mastering statistical analysis. They would continue to concentrate on creative work, collaborating with academics and data scientists to offer valuable data-backed input.

All data may be presented simply and even graphically to make it more understandable. As a result, designers do not need to be mathematically literate.

It’s a designer’s dream: less time spent on revisions and more time on creative work.

However, there is a downside to this data-driven design approach.

Nothing Is Perfect!

Don’t get caught up in the trap of maximizing the statistics while overlooking the bigger picture. It may result in a poor user experience and harm a brand’s image in the long run.

You must strike a good balance between intuition and scientific data. Knowing to depend on which one is a delicate skill. When the data doesn’t provide a clear answer, or you need to stitch together many disparate parts into a cohesive whole, you may rely on your knowledge and gut instinct.

Numbers may enlighten you, but they should not make you a slave to them.

After all, data may inform, but it is up to the designer to contribute the secret ingredient that will bring the design to life. The human element is what drives true innovation, while statistics can merely inspire or provide helpful insights.

What Is Data Driven Design?

DataData driven design is a design decision-making method that primarily depends on gathered data regarding consumer behavior and attitudes.

Information regarding how consumers engage with your design serves as feedback, informing you whether or not your design achieves its goal.

Is the CTA button visible enough on a landing page, and does it get enough clicks? Does the design pique the viewer’s interest without overshadowing the primary message? If it’s an e-commerce website, are all of the stages in the purchasing procedure evident to the user?

Designers often dislike the data driven design idea because they are concerned that facts and figures would restrict or even replace their creativity. Not to mention the mistaken belief that they would have to deal with statistics. That couldn’t be farther from the truth.

Furthermore, data-driven design approaches may help you save time and money. Proper user research and testing may reduce the number of revisions required to get the final design, and you could even get it right the first time.

Why Is Data Driven Design Important?

It is risky to rely only on your intuition and not consult facts for real-world input. It may result in an inefficient design, resulting in lost income, wasted time and effort for revamping, or even some brand image damage.

You may use data effectively to improve conversions and push your company to overall success. Many success stories demonstrate how data-driven UX approaches substantially contribute to company development.

 

What to Remember When Thinking of Data Driven Design?

Image courtesy of Learning Environments Lab

So far, we’ve discussed what data driven design is and how it may provide value. We also addressed some common misunderstandings regarding it.

The data driven design approach is supported by evidence about users, which is the foundation of developing a user-centric design. As we have shown, this customer-focused and data driven approach to design may add substantial value to your company.

Data Driven Design Is Often Misunderstood

Despite this, data-driven design is often misunderstood, and its execution may cause a lot of conflict in a business. You may encounter some opposition from stakeholders who have opposing views. Assume the designer can produce a good design without using any data, and money is flowing. Why bother with all of the research, analysis, data interpretation, and extra people?

As a result, getting stakeholders on board with the idea of data driven design may be difficult. However, if you follow our advice, this job may become much more straightforward.

First and foremost, if you want stakeholders to embrace your vision of a data driven design approach, you must ensure that they understand what a data driven strategy entails. One of the most critical issues to solve is what constitutes data.

Most people associate data with numbers when they think about data. This is particularly true if they are not well-versed in UX research. However, we cannot express specific facts numerically. Opinions, views, or emotions are sometimes depicted.

We may compare data to evidence. A large portion of it is numerical data that answers what, when, where, and how often. Although this quantitative data is fundamental, it cannot explain why individuals act in this manner.

To get insights into the user’s motives, we must do qualitative research. We can bring that missing piece of knowledge to the table by watching people, listening to their views, and empathizing with them.

As a result, we should treat this less physical and less organized information with the same regard that we do numerical data. Take the time to ensure that you and your stakeholders are on the same page on this.

Data Is Not Fact!

Furthermore, data cannot be treated as absolute fact. Even though data offers proof regarding consumer behavior, it is essential to remember that it only represents a portion of their experience. No matter how large our data set is, we’re just interested in one or a few elements of the overall narrative. Moreover, to get the whole picture, we must expand and diversify our data. Consider incorporating as many diverse data sources as possible, but remember that this approximates user experience rather than conveying the actual truth.

This discussion with stakeholders about what we can and cannot do with data is required to effectively implement a data driven design strategy.

While many businesses still do not prioritize design, statistics indicate that they should. If a design can make such a difference, be sure it is supported by facts rather than simply conjecture and intuition. Research supports this idea that a data-driven strategy may provide a competitive edge.

“Companies in the top third of their industry in the use of data driven decision making were, on average, 5% more productive and 6% more profitable than their competitors,” said Andrew McAfee and Erik Brynjolfsson from MIT in Harvard Business Review.

Furthermore, data driven design concepts may assist designers in improving their efficiency. User research and testing may help to reduce the number of iterations and revisions required to complete the design.

A Designer May Do More in the Same Amount of Time

There’s a lot to gain in the long run by recognizing the significance of design and incorporating data-driven thinking as the fundamental concept of your company’s work.

When persuading stakeholders to embrace data driven design, utilize success stories to show the return on investment (ROI) of data driven UX research techniques. When you demonstrate how evidence-based methods have benefited others, it is simpler to sell the concept.

Allow the data to speak for themselves. It’s challenging to remain uninformed about data driven design when you realize the value it can provide.

“Creating visual explanations had greater benefits than those accruing from creating verbal ones,” concluded a 2016 study. According to this research, people prefer to absorb information better when presented in various formats—using pictures while giving new information aids in knowledge absorption.

So, while creating your presentation for stakeholders, consider adding some visuals and diagrams. A few persuasive graphics may go a long way.

How Do You Use Data Driven Design Decisions?

We defined data driven design and why it is vital in today’s business environment. We offered you some pointers on how to get stakeholder support for adopting it in your organization. However, we haven’t yet discussed how to get started with this data driven design approach.

Adding a data driven design model to your current working framework may seem to be a difficult job. With so many moving pieces, starting this process is much more difficult. This section will help you understand where to begin with this data-driven strategy and the critical components for adopting this model in your company.

For this data driven design approach to function, influential individuals must first have access to the data. There is often a lack of information flow across departments in more prominent companies, which may be a significant impediment to adopting this new procedure.

It’s a long-held belief that analytics experts deal with quantitative data while UX researchers and designers focus on the user experience. These lines are gradually becoming blurred.

Designers Must Also Have Access to Quantitative Data, Particularly Data That defines User Behavior

Of course, they need to represent this data intelligibly, rather than just raw, which may seem to non-data people to be a foreign language.

Discuss with your team how you want to distribute this information inside your company between data workers and designers. Think about who needs to know what, how you’ll organize the whole process, and what tools you’ll utilize. Make sure that gathering all of the required information is as simple as possible for everyone.

Ensuring an efficient flow of information is not enough. Colleagues must also understand one another. That brings us to another issue you’re likely to encounter right away: a lack of consistent language between the data and design teams. Designers may not need to master all of the technical jargon data professionals use, but they need to understand each other.

Defining the fundamentals is a great place to start. Discuss quantitative and qualitative data by discussing how they are related and why it is critical to utilize both kinds of data in the design process. We have previously addressed this.

Clear up any misconceptions about this quantitative/qualitative problem early on to prevent needless conflict caused by concerns about the reliability of qualitative data and to enhance communication among team members. Define the vocabulary you will use in communication and data extraction tools.

It’s also critical to have a clear vision of success and ensure that everyone in the team agrees on it. You may have several distinct objectives that you want to accomplish, but you can achieve them all at once. Check to see whether everyone engaged in a given activity has the same aim in mind. This brings us to the goal-setting process.

Your Decision Matters!

Before you begin collecting data, you must first decide what you want to do with it. Making your objectives as straightforward as possible is essential for successfully adopting this data-centric strategy.

Do you want to launch a new product or just tweak an existing one to produce a new iteration? In these two situations, the data collection procedure will be different.

Also, be sure that your objectives are attainable. Preferably, we should use data driven methods from the start. As a result, it may be tempting to start again using data driven design ideas. Aside from the facts, other significant considerations such as cost, time, and practicality should impact your choices.

You may examine these extra factors and ask yourself if a redesign is a viable option in that specific scenario. It is sometimes preferable to just alter the product.

If you’re learning how to harness the power of data, be sure you do it properly and prevent errors that may result in lower-quality data or erroneous conclusions.

Here are a few pointers to keep in mind if you want to make the most of your data:

Collect Adequate Information

Make sure that the data sample from which you draw inferences is large enough to provide statistically significant findings. If you gather insufficient data, you may get a distorted picture of reality. Such information will not be of much use to you. Also, consider collecting data over long periods of time to reduce the impact of transitory effects and random oscillations.

For Your data, Use reference Points

Assume you’re looking at your page statistics and see that 500 people visited your site in a single day. Is it a good or terrible outcome? You’d have no idea until you compared it to your weekly or monthly average. It may also be beneficial to compare such statistics among rivals in your niche.

One at a Time

During A/B testing, we only test one variable at a time. For example, if you’re testing how the contrast of your CTA button impacts conversions, changing its location isn’t a good idea since you won’t know which of them made a difference.

Use Both Kinds of Data

When possible, use both kinds of data – quantitative and qualitative. These two kinds of data disclose specific information about your consumers. While quantitative data demonstrates consumer behavior, only qualitative data reveals the reasons behind that behavior. Despite whatever negative assumptions you or your coworkers may have, try incorporating qualitative data in your analysis.

Keep Context in Mind

When optimizing, keep the context in mind. For various kinds of sites, content, and users, successful optimization may appear different. Some websites are only concerned with conversions, while others educate visitors or fulfill another function. Keep in mind that the demands and objectives of new visitors may vary from those of regular visitors. Consider the variations in users from various sources, such as organic search, email marketing, and advertising.

Some Additional Tips:

It is not enough to just collect data and understand how to evaluate it. Patience is essential, and it may be one of the most challenging parts of implementing a fully functioning data driven design process in your company.

You must wait and observe after analyzing your data and making any changes. That’s when another obstacle appears. People tend to attempt to comprehend and classify what they see as quickly as they can, and in this instance, that inclination works against them.

It is critical to track the impact of your design modifications on consumers. However, keep in mind that there will always be some time for adjustment. If the design of a website or the interface of an app is changed, it will take some time for people to retrain how to use it and get used to it.

So, whether you see good or bad impacts early on, don’t jump to conclusions since you may not have seen the whole picture. After the early excitement of a new design wears off, consumers may discover that it isn’t beneficial, impacting their usage. Customers may perceive more advantages and find it helpful after an unpopular change and vice versa.

When you make design modifications in the future, allow your customers some time to adjust before evaluating the new data.

What Is the Procedure of Data Driven Design?

Once you’ve determined why you’re collecting data, you may concentrate on developing a hypothesis.

Even though we employ hypotheses in scientific experiments, the same concepts apply when designing a UX experiment.

Create Hypotheses

A hypothesis should not be confused with a theory. A hypothesis is more of a prediction, while a theory is a body of knowledge supported by evidence that explains occurrences. The biology department at California State University, Bakersfield, defines a hypothesis as “a tentative statement that proposes a possible explanation for some phenomenon or event. A useful hypothesis is a testable statement, which may include a prediction.”

Given that you’ve established criteria for developing a hypothesis, the critical issue is: how do you decide what to test? Where do you begin your research?

It is not a good idea to test everything that can be tested. Also, avoid stabbing in the dark in the hopes of hitting the target. In both instances, the findings are unlikely to indicate any significant improvements. Worse, you may lose credibility in the eyes of decision-makers, making it difficult to get approval for another test.

The good news is that you already have some information about your clients’ activities. You may either do basic user testing or rely on analytics data. When we use this data to make choices, it is a simplified form of a data driven strategy.

This is a solid start, and it will make incorporating a more complex data driven design approach into your decision-making much more straightforward. You may begin using data driven methods with the data you already have. After you get accustomed to it and exploited the data at your fingertips, you may consider alternative data collection techniques.

Get to Know Your Customers Using Data

You may try a couple of other methods. If you are unsure about your ideal client profile, the first method is perfect (ICP). You can utilize data to learn more about your consumers. Examine page analytics and behavior flow to figure out what they’re doing on your website. Analyze demographic data and audience analytics to get a more in-depth understanding of your customers.

Using this information, you may begin searching for confirmation or disproof of your hypotheses in the actual world with real people. Customer surveys and interviews may help you learn more about how and why people use your product. Analyze this new information and build your ideal user personas based on the recurrent themes and patterns discovered throughout your user research.

You may now conduct tests with people who match your ICPs since you have user personas. Collect input from them throughout the early stages of development. Involve these people in beta testing so that you may catch as many bugs and other problems as possible before the launch. Return to these people as you improve your product or website to get meaningful input.

The second method is ideal if you have a firm grip on your ICP, but something else seems to be amiss. You may detect specific abnormalities in user behavior by examining quantitative data from web analytics. Those unique behaviors of your consumers may appear as a measure that is abnormally high or low. It may be an extremely high bounce rate, a very short average stay time, or a higher-than-normal exit percentage on specific subpages. It’s difficult to decipher what such indicators imply since there may be a variety of causes.

Some Examples:

Assume you offer a SaaS platform or an app to your clients. Your landing page directs users to the trial registration form. While reviewing analytic data, you see that the conversion rate from visitors to trial users is very high. Nonetheless, you find that most of them quit utilizing your platform and become inactive users only a few hours after joining. What’s going on?

There may be many possibilities. Perhaps certain issues irritate people too much, or maybe an unpleasant user interface eventually catches up with them. Maybe something disoriented them throughout the lesson or your instructional animation froze at some point? Or is it a communication issue, and your consumers anticipate one thing, but when they register, they don’t receive what they wanted? This far, quantitative data will suffice.

Numbers can tell you what’s going on, but they can’t tell you why it’s going on.

You must now delve further into qualitative data. Get in contact with your consumers and consider things from their point of view. We may gather more information via surveys, user interviews, or session recordings.

What’s even better is seeing a person who resembles your target client profile use your product in real-time and asking him questions about what he thinks while doing so. It may be costly, but it may provide some valuable insights. When you’ve identified the issue, you may create a few possible remedies and conduct an A/B test to see which one works best.

What Is Data Driven Design in UX?

Data driven design employs UX research techniques such as surveys, usability testing, behavior flows, and website tracking analytics.

Usability testing allows you to assess how simple a design solution is to use. At different phases of the software development process, you may perform usability testing in a lab or remotely. You’ll generally collect qualitative data on participants’ experiences with a product, but you may also collect quantitative data.

A/B and multivariate testing allow you to compare the performance of various versions of a website or app. You may utilize these methods to enhance your user experience while also driving user activity significantly. Running A/B testing to improve a design regularly may result in significant improvements in conversions.

Behavior flows depict how people move through a website or application, from the initial landing page to the final page they see before leaving. In most instances, UX designers prefer that visitors follow a specific route across a site. If the actual behavior flows deviate much from the road, there may be an issue with the user experience.

Designers may utilize several user research techniques to gather the information that will help them throughout the design process. With excellent data, you can build a better user experience and more successfully impact user behavior. User interviews are an essential source of data for data driven design. Surveys are very effective data collection techniques.

Card sorting, contextual interviews with real users, focus groups, surveys, and heuristic assessments are additional UX research techniques. 

Personas, task analyses, and use cases are examples of user-centered design artifacts that we may create via user research. While user research is one of the most time-consuming ways of data gathering, it may also be the most useful, particularly for new projects and products with no previous primary data sources.

Conclusion

Here you have it. We’ve covered all you need to know to get started in adopting a data driven design process in your company effectively.

Understanding the data driven design process may offer the designer a competitive advantage in his career.

This strategy may also provide substantial value to the company by improving customer experience, boosting conversions, and optimizing ROI over time. It may take some time to learn how to use these methods, but the work is well worth it. It will reap substantial long-term benefits.