Data-Driven Design Process: What It Is and Benefits for Your Project

Creating a user-friendly product must be guided by information about what works and what doesn't. And that's where data-driven design comes in. Before this approach, designers made solutions based entirely on their experience, skills, sense of taste, ideas about design, and their design "handwriting." The common problem was that designers were exposed to the effect of false consensus and projected their reactions and behavior on the page onto potential users — they made decisions based only on their vision.

Thus, the emergence of a new paradigm was reasonable and expected — e-commerce sites, technology startups, social networks were the first to experience the need for DDD. The main reason is the user-centricity of these platforms. Today, the data-driven design approach covers the flaws of the traditional methodology — both on the contractor's side and on the client's one. It is about making informed design decisions. The most important thing is that now the design is always justified — no intuition and subjectivity, no corrections because of the difference in the tastes, no discrepancy between design solutions and user needs.

In this article, we will cover everything you need to know about DDD. After reading this overview of data-driven design, you will understand when to use this methodology, and most importantly — how to get the most out of it.

For some, design is an art, but it is actually more about an applied craft. Design is about solving problems. Data-driven design is by far one of the key trends in web design over the past few years. This product development methodology is based on data about end users' motivation, behavior, needs, attitude, and expectations. It relies on tests, analytical research, hypothesis testing, Big Data, and the achievement of certain metrics is the main goal.

In fact, implementing the data-driven design process is considered a truly serious approach to project design. It helps create a user-centric design and a better UX. DDD allows making better choices based on objective evidence. Such a design is closely associated with specific metrics: if they are achieved, the design is good, if not, something needs to be changed.

With this approach, the design decision is made based not on the taste or intuition of the designer but on the results of tests and hypothesis checks. You can't just make a button big and red. You must first compare it with the small blue button, and if the desired indicator for the red one is higher (for example, users click "order" more often), then such a change is accepted. And only so. All changes must be supported by numbers. Each design decision must be substantiated and reasoned. This is the basis of everything. And this is the main thing that makes the data-driven design more reasonable than the intuitive one.

The data-driven design techniques have fundamentally transformed the way we approach digital projects. It has changed the world of design and can change your project. Let's find out how.

Collecting and analyzing data is key to creating better designs and UXs. Its effective use can directly lead to better business results, while the absence of data accounting (or its ineffective use) may have serious consequences for the success of a project. So, how to use the data-driven design for your next project?

Here is our complete guide to the data-driven design process.

First, for the data-driven methodology to work, you must set up a process for ensuring access to information for all key people. The common opinion is that analysts work with numbers, while designers deal with the experience. Yes, but today these metes and bounds are erased. Therefore, it is crucial to establish a communication process within the team: how you will transmit the data and between whom, how you will arrange this process, and what instruments you will apply.

Providing access to data is not everything. The data and design departments must understand each other. Eliminate misunderstandings in the context of quantitative and qualitative data, agree on terminology, improve communication flow between specialists. And don't forget to make sure everyone is moving in the same direction and working towards a common destination.

To implement a data-driven approach effectively, you need to make your goals as clear as possible. For instance, depending on whether you want to release a new product or just change an existing one, the process of data collection will be otherwise. Also, make sure the goals you set are realistic and remember that beyond the data, factors such as cost, time, and feasibility must influence your solutions as well.

Once you realize why you are collecting data, you can focus on building a working hypothesis. It looks more like a predictive guess. And a data-driven approach will clearly show whether your solution satisfies end-users or not. A reasonable hypothesis should have a clear metric to evaluate and a visible path to that measurable outcome. The hypothesis is based on the principle: for (user group), if (change), then (effect), because (justification), which will affect (metric), where:

How do you choose what to check? Testing everything that can be tested is definitely not the best solution. However, the plus is that you already have some information about your users' behavior. This will make it much easier to incorporate a more complex DDD process into your decision-making. You can start applying data-driven methods with the data you have. Once you get used to this and take advantage of this data available to you, you can start thinking about other data-driven design techniques for gathering information.

Okay, let's suppose you don't know your customers well enough. Use the data to "get to know" your users: check page analytics, analyze actions, explore demographics and audience analytics, and gain a deeper insight into your customers.

Then you can move on to checking your hypotheses. Find out who uses your product, how they do this, and why. Study this information and build your ideal audience portraits. Finally, when you have your users' images, you can conduct tests with users that correspond to your ideal customer profiles. Engage them in the early stages of the development to find all possible errors and flaws before release. Refer back to these users throughout the development for valuable pieces of advice.

And now, let's suppose that you know your users very well, but something seems to be wrong. By analyzing quantitative data, you may find weird customer behavior patterns that can be both unusually low or too high. The numbers can tell you what's going on, but they don't tell you why this is so. This is where you need to delve deeper into qualitative data. Collect more data through data-driven investigations, surveys, and other data sources for designers, look at a product from the position of your end users, observe the person who corresponds to your ideal client in real-time. All this will give you very valuable information.

You must not only make the most of the data but also make sure you do it right. Here are our life hacks:

Patience is critical and can be one of the hardest parts of building a data-driven solution. After you analyze your data and make the appropriate adjustments, you will have to wait. Track the impact of design changes on customers, remember about the onboarding period, and don't jump to conclusions until you see the full picture.

The DDD is beneficial for all sides of the process: both for the customer and the contractor. Let's find out the benefits of data-driven design for businesses and for those who implement it.

With concrete data supporting decisions, clients can't argue that the button is bright blue and located bottom left. This design decision is backed up by research, data, and numbers, which means it is correct a priori. Even if the customer doesn't like blue.

DDD helps UX designers go beyond their own views and traditional approaches. Each industry, each vertical, and each business are unique. Designers need to gain knowledge that is specific to their target audience so that they have the opportunity to improve the UX when working on a product.

Even the best designers can't predict exactly what users want. Data is the link to customers, it provides insight into what content they find valuable and what problems they want to solve. Users want intuitive digital products. It's all based on an understanding of user data.

While striving to improve conversion rates can prevent designers from innovating, the issue is not using the data itself but how it is used. Designers can and must come up with radical and bold solutions. Nonetheless, if they want stakeholders to agree to implement these solutions, they need to back those hypotheses with data — do usability testing, use website analytics, run a customer survey.

DDD leads to permanent improvement. It involves implementing incremental changes, tracking vital signs, and making further changes based on data-driven decision methodology. This increases the overall productivity and efficiency of the design process and improves the product and you as a specialist as well.

Many companies suppose it is difficult to find a balance between user needs and business goals. Lack of data analysis can have serious negative consequences for the success of a project. Leveraging data effectively, in turn, can lead to improvements in business performance. After researching and analyzing the results, many unexpected insights and ideas emerge that make the design solution even more effective.

The best and most high-converting projects are always focused on the needs of the users. Designers cannot predict what customers want and need — your designers are not your users. They have different experiences and perspectives, various needs and expectations. Fortunately, designers can fill this gap through UX research — for example, by conducting usability testing. This is where the data-driven approach comes in handy.

Data-driven design thinking is an investment that drives sustainable growth. This process provides a high return on investment. With the help of user data, the website can evolve based on the user's preferences. This translates into more conversions and a better ROI. The investment pays for itself in full.

DDD enables businesses to meet customer needs both now and for the future. It's also a far-reaching decision because the data-driven design is typically applied across the entire product fleet, allowing detecting trends within product data that can positively impact future projects — and profit margins.

The data-driven design also helps companies elicit product failure trends. By keeping an eye on the failure tendencies in your current product fleet, you can work to fix issues in future releases, thereby reducing outages and customer downtime.

They are there both for the person who pays and for the person who makes. For a customer, continuous improvement means budget reserves. Not every business is ready to incur additional costs at the stage of launching and running a project.

For a contractor, there is a risk of getting bogged down in data, demotivating designers. Creativity, innovation, and bold ideas don't live where analytics reigns and continuous changes in the finished product demoralize the design team.

DDD often leads to kinks — a "machine" approach to product development for people. That is why some may criticize this approach.

However, if not data-driven, then what? "Humanized" data-driven! To neutralize the disadvantages of this approach, be flexible. The data brings invaluable benefits to the product; it is indisputable. Nonetheless, the most successful services adhere to such a philosophy: design with reliance on data, but not blind submission.

Data-driven design principles should not fully predetermine all design solutions. The design that relies on the data and quantitative research must consider the context and not overlook the qualitative data. That is why it is so important to combine Big Data and traditional approaches: surveys and observations. For the same reason, it is not necessary to leave in the direction of data centers: people, not technologies, generate innovations.

Look for the proportion of data and creativity. Such an approach will maximize the benefits of data-driven design and minimize its disadvantages.

It is easy to get started using data-driven design methodology for your startup or another project, but it is quite difficult to imagine all the amazing possibilities this approach brings. Specific numbers and hypotheses based on qualitative and quantitative research are useful for incremental and tactical changes.

Regardless of your goals, a data-driven design process can help improve product performance and increase conversions. Strength in numbers. Interested in creating a data-driven design solution? Asynchon Labs will help you experience the full benefits of this approach and get the most out of the process.

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