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Data-Driven Design: An Integral Part of UX Design

October 5, 2020

“What’s measured gets managed. Numbers have an important story to tell.”—Peter Drucker.

What is data-driven design (DDD) and why should we care about it? UX design uses research data of various kinds to determine how to provide an optimal user experience. Forbes has described some key customer analytics, including customer satisfaction, lifetime-value, segmentation, sales-channels, Web, social-media, engagement, churn, and acquisition analytics. This data helps product teams understand their target users, reveals information about users’ painpoints, unearths new trends, supports data-driven design, and assures teams that their work is on track. User data can lead directly to improved business outcomes. UX methods incorporate data-driven design, which has proven, tangible results.

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The Impact of Data on UX Design

We need to approach data collection with immense care. Two broad classes of data factor into data-driven design and help us make design decisions:

  1. Quantitative—Numerical data that shows who, what, when, and where. Quantitative data shows scale. What quantitative data doesn’t tell you is why. We obtain much quantitative data regarding the usage of a Web site or application from Google Analytics.
  2. Qualitative—Data that demonstrates why and how. Why does a certain group of visitors take one action, while a different group chooses another? Why does one piece of content keep visitors on your Web site longer than another? Qualitative data offers perspective and helps us understand not just what happened, but why and how it happened. We often communicate qualitative data through personas or journey maps.

“The qualitative / quantitative issue is really a misunderstood area in research, especially to people who haven’t been exposed to broad-based training.”—Dave Yeats, Senior UX Designer at Bazaarvoice

Yeats says he has encountered many instances of “people dismissing qualitative research as anecdotal because they don’t understand [that] non-numerical data is still data.”

The best qualitative and quantitative data are always empirical data.

The Importance of DDD Thinking to UX Design

Design analytics (DA) can be the richest source of data for UX designers. This data includes everything from Web analytics to A/B testing results.

You should consider design an investment, not an expense. It is not enough to design an aesthetically pleasing product. It must also be usable. Usability—which refers to ease of use—is of vital importance.

Data-driven design is important because it helps you

  1. Understand your users and their needs
  2. Move beyond best practices
  3. Create effective designs
  4. Leverage data to drive innovation

Understanding Your Users

In the IT services and product industry, there widespread belief that usability testing is unnecessary when a brilliant UX designer is working on application or product. But, all too often, recruiting a world-class designer proves insufficient to guarantee the success of a product. Designers cannot predict what users want and need.

Your designers are not your users. They come from different demographics and perspectives. Both navigate technology differently and have different expectations. Fortunately, designers can bridge the gap with UX research—especially generative user research and usability testing.

Moving Beyond Best Practices

Data-driven design helps UX designers move beyond their assumptions and reliance on best practices. Every industry, vertical, and business is unique. Following design guidelines to the letter or adopting the latest digital-design trends doesn’t enable you to develop empathy for your target users. Designers must gain insights that are specific to their target audience to improve the user experience for a product. Data that can help designers make design decisions that enhance the user experience include findings from user research, usability test results, Web-site analytics, and survey results.

Creating Effective Designs

Many organizations struggle to balance their users’ needs with their business objectives. Failing to consider data analytics can have serious negative implications for the success of a project. Using data effectively can lead directly to improved business outcomes. Research by MIT’s Center for Digital Business found that “companies in the top third of their industry in the use of data-driven decision making were, on average, 5 percent more productive and 6 percent more profitable than their competitors.”

UX analytics can inform UX design. For example, high exit and bounce rates can indicate that a page doesn’t contain the information a site visitor was seeking. Or, if the information is actually there, it might indicate that it’s not easy to find or understand. Paying attention to UX analytics as you make changes to a design is also key. If a page is performing well before a redesign, pay attention to how it performs afterward. An improvement in positive indicators means the redesign is on the right track and vice versa.

However, such decisions often win the battle, but lose the war. The most effective, high-converting Web sites and applications serve users’ needs first. If your product’s holistic user experience is engaging, intentional, and easy to navigate, users are more likely to convert. Even if your conversion rates for individual pages decrease, your core conversion rates could be higher.

Leveraging Data to Drive Innovation

Many accuse the data-driven approach to design of inhibiting innovation, but these goals need not be at odds. While it is true that striving to grow conversion rates just by small percentages can prevent UX designers from innovating, the problem isn’t using data, but how you’re using it. Designers can—and should—be able to propose drastic, daring changes. If designers want their clients or other stakeholders to agree to implementing their designs, they should back up their design hypotheses with data.

If you want to completely redesign the checkout flow for an ecommerce site, run usability tests to identify the core stages of the checkout process at which consumers struggle. Use Web-site analytics—for example, to show how many people abandon their cart after seeing the shipping rates. Survey your current customers about their shopping behaviors to better understand your up-sell opportunities.

Getting Started with Data-Driven Design

Designers don’t usually have backgrounds in statistics or information analytics. However, incorporating data into our design process means we’ll have to brave the use of numbers and learn to understand how analytics operate.

“As designers, we need to accept and embrace the world of metrics and use their amazing powers to change the way we’re doing things.”—Jared Spool

While every organization differs, getting the data you need could be the single hardest part of your journey on the path to data-driven design. Especially in larger organizations, departments often have very distinct functions and crossing over their boundaries can be nearly impossible.

To truly have a successful data-driven process, you must have access to the data. So you may need to put up a bit of a fight—in a business appropriate, respectful way, of course—to break down some of the barriers to information access.

Collecting Data

First and foremost, be sure to collect information over an extended period of time—or at least at multiple intervals. For example, if a statistic says you had 1,000 users on a given day, unless you know how many users you usually get, you won’t have any idea whether you’ve improved your numbers, they diminished, or that was a completely normal day. Try comparing your numbers across the industry domain in which you’re working to get a sense of where you stand.

Once you’ve gathered a decent amount of data, you’ll need to learn how to interpret and understand the information. Some of this is straightforward enough that a little intuition and a brief explanation from an experienced colleague would give you all you need to get started working with data. However, working with data takes practice and continued effort.

“Data science is now an essential skill for every UX team. If you don’t have people who understand how to do data science, you cannot create great designs.”—Jared Spool

Choosing UX Research Methods That Deliver Useful Data

Data-driven design uses UX research methods such as surveys, usability testing, behavior flows, tracking analytics on Web sites or in mobile apps, competitor analysis, and heuristic evaluations. Google Analytics has built-in benchmarking tools that make it simple to see how a Web site is doing in comparison to the industry average.

Usability Testing

Usability testing lets you evaluate how easy a design solution is to use. You can conduct usability testing either in a lab or remotely at various stages of the software-development process. Generally, you’ll gather qualitative data about participants’ experience with a product, but you could also collect some quantitative data.

A/B Testing

A/B and multivariate testing let you see how different versions of a Web site or app perform against one another. You can use these approaches to make big improvements to your user experience and drive user behaviors as well. Continuously running A/B tests to improve a design can result in huge increases in conversions. Learning hub provides 25 statistics about A/B testing.

Behavior Flows

Behavior flows show how users traverse a Web site or application—from the first landing page to the last page they view before exiting the site. In most cases, there is a certain path that UX designers would prefer that users take through a site. If the actual behavior flows differ greatly from that path, there may be a problem with the user experience.

Google Analytics has built-in tools for exploring user behavior flows. Exploring this data in comparison to the ideal behavior flow the UX designer has created offers valuable insights into whether the design accomplishes the designer’s user experience and behavior goals.

Qualitative User Research

Designers can employ a variety of user-research methods to collect data that can assist them during the design process. With good data, you can create a better user experience and influence user behavior more effectively. User interviews are an important way of collecting information for data-driven design. Surveys are also powerful tools for collecting data.

UX research methods also include card sorting, contextual interviews with actual users, focus groups, surveys, and heuristic analyses. User research provides the basis for creating user-centered design artifacts such as personas, task analyses, and use cases. While user research is one of the more resource-intensive methods of data collection, it can also be the most valuable—especially for new projects and products for which there are no existing, primary data sources.

Analyzing Usage Data to Learn What Matters to Users

Figuring out your digital product’s key areas for improvement can be difficult. Having a glut of data can feel overwhelming, but data can also be the key to figuring out how to attack a problem.

When you examine the data, you’ll often see specific patterns emerge—for example, people abandoning a site on a particular page, extremely high or low bounce rates or time-on-page indicators, a usage drop off after roughly three weeks of using your app. Whatever you’re seeing, dig deeper into the data you’ve been gathering.

However, figuring out surface-level patterns isn’t always indicative of an issue. Perhaps people abandon the site on one particular page because they’ve completed their task, but you won’t know for sure until you look at the data more deeply—especially where there are anomalies in the data. Balancing the quantitative and the qualitative data is where your intuition comes into play.

Representing Data Visually

Data visualization is the graphic representation of information or data. A study about the distribution of the various types of learners shows that 65% of people are visual learners. Using data-visualization tools such as charts, graphs, timelines, and maps lets you provide an accessible way for people to absorb visual information and understand trends, outliers, and patterns in data. By presenting visual data to your stakeholders, you can captivate your audience and clearly convey your message. You can thus help the business make go/no-go decisions about product or application features.

Conclusion

When getting the data you need isn’t the hardest part of implementing a data-driven design process, having patience in working with the data probably will be. This is perhaps the most important requirement for getting started with data-driven design: being patient is absolutely crucial to the success of your work. It’s important to monitor the effects of your design changes and critically evaluate the market response to your changes. Keep in mind that, no matter what changes you’ve made, there will be a period of adjustment for your users. But, armed with quantitative and qualitative data, you can better influence business decisions and convince decision-makers that optimization is necessary.

Today, with mobile user experiences and augmented-reality (AR) and virtual-reality (VR) technologies gradually capturing the marketplace, there are new business opportunities that UX designers can leverage through data-driven design. Your product owner should use competitive product information, as well as look at competitors’ current products to guide his product decisions. Data-driven design plays a vital role in the UX design process—especially in organizations where heuristic evaluation is the most commonly used tool for helping product owners decide whether to redesign an application.

UX designers are now moving toward data-informed design and data-aware design methodologies. Data-informed design ideally means that qualitative data informs the product owner’s decisions, along with experience and intuition. A data-aware design team would put quantitative data on an equal footing with other decision-making factors. Such a team views data from UX research as one of many essential sources of valuable information. 

Experience Specialist at the Experience Design and Engineering (EDGE) Centre of Excellence at HCL Technologies Ltd.

Chennai, Tamil Nadu, India

Anusha PichumaniAnusha began her information-technology (IT) career as a developer and gained her knowledge of User Experience through formal education, by conducting user research, and by being responsible for user-interface design on her projects. She has expertise in diverse domains, including insurance, retail, banking, healthcare, and engineering. She has over 11 years of work experience in sales, research, ecommerce design, UX consulting, Lean and agile methodologies, UX design processes; and interaction design for Web, mobile, and enterprise applications. She enjoys singing and reading about human psychology and design.  Read More

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