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Guide to Data-driven Design | GoDaddy Pro

Guide to Data-driven Design | GoDaddy Pro

Crunch those numbers Data is crucial in driving business decisions in today’s digital world

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Crunch those numbers

Data is crucial in driving business decisions in today’s digital world. While the design is often seen as an art based on intuition, sometimes it is not easy for designers to read the thoughts of their prospects and customers. Using intuition when designing a website may often lead to a design that does not meet users’ requirements. This is where data-driven design comes into play.

A good website design is user-centric and aims at providing a better user experience. Data helps understand users’ behavior and their requirements to create a better design that is easy to use. This article elaborates on data-driven design, why it matters, and how to adopt a data-driven design in your organization.

Understanding data

Data refers to information collected and stored in a form that is efficient for processing. That said, data does not just involve numbers. It includes qualitative data relating to opinions, emotions, observations and other things that are not presented in numbers.

As such, both quantitative and qualitative data are essential for your design process. Numbers provide answers to questions like how much or how many, which represent quantitative data.

Great sources of qualitative data include website analytics, A/B testing, heatmaps, and surveys.

On the other hand, qualitative data cannot be represented in numbers. It explains why users perform specific actions — user motivation and purpose. Great sources of qualitative data include competitive analysis, usability studies, interviews, focus groups, and much more.

When used alongside intuitions, data plays a crucial role in helping designers provide a better experience to their users. Among other things, qualitative data will help you better understand your customer’s needs and what motivates them to take specific actions.

This will help inform your decisions when designing and modifying your website design.

Wait… data-driven design isn’t perfect?

Like anything else good, data-driven design is not perfect. As such, you should not overly rely on data while ignoring the bigger picture. This mistake can lead to a bad user experience and hurt your conversions in the long run.

The best approach is to take advantage of intuition and scientific data and find a balance between the two. Combining the two provides a clear picture of what you should do to have an attractive and effective website design.

In simple terms, data-driven design refers to a design process that relies largely on data about customer behavior and attitudes.

This includes how your users engage with your design, whether your CTA on your landing page gets enough clicks, whether the buying process steps are clear enough, and whether your design achieves its goal.

Not all designers like data-driven design. Some argue that scientific data interfere with their creativity. However, this is not always the case. When done right, the data-driven design will save you time and money as it will limit the number of revisions required to get your design right.

Why all this even matters

You are unlikely to have an impactful website design if you only rely on intuitions and ignore data. Ineffective designs mean wasted time and money and lost opportunities to drive traffic and generate revenue. In most cases, a bad website design will hurt your SEO and brand image.

The need to use data in your website design cannot be overstated. Effective data use will lead to a better user experience, ultimately increasing conversions and improving your business’s overall performance.

Data provides an effective way for designers to validate their intuitions with data. Qualitative data helps designers better understand their customer behavior, what they need, and how they can adjust their design to give them a better experience.

This does not mean that designers need to learn statistical analysis. Instead, they need to work with researchers who provide beneficial feedback to enhance their creative work.

The data is presented in a clear and simple way, meaning you do not need mathematical skills to understand and apply these data.

How to create data-driven designs

Like anything new, introducing a data-driven design process in your organization can be daunting. In this section, we provide tips on how to get this process started in your company.

Define clear goals

Before you do anything else, make your goals clear and realistic. After all, defining your goals clearly is crucial for this data-driven process.

Whether you are introducing a new product or creating a new iteration for your existing product, it is important to start right.

Setting your goals is not enough. You need to ensure that your goals are realistic.

If you want to apply data-centered techniques from the start, you may want to make products from scratch. Besides data, cost, time, and feasibility factors will influence your decisions. Sometimes you may realize that you need to modify your product rather than redesign it.

Ensure key people have access to data

Data-driven processes will not work if key people do not access data. Implementing a new process can be daunting if communication between departments is limited.

Typically, analytics specialists handle quantitative data while designers deal with the experience. However, to actualize data-driven design processes, designers should access quantitative data, especially on user behavior. The catch is to ensure that the data is presented in an understandable manner.

Talk to your team about the best way to share this data between the analytics team and designers in your organization. Think about the people who need access to the data and the right tools to ensure that all key team members have easy access to the data.

Ensure people understand each other

The flow of information is one thing; you need to ensure that people are on the same page. While designers do not necessarily need to understand all the technical stuff in data space, they need to be on the same page with other players.

The best approach in this is to define the basics.

Talk about quantitative and qualitative data and how they relate and the need to use both types of data in the design processes. Besides that, you need to define the terms you will use while communicating between team members.

You may also want to revisit your goals to ensure that every team member agrees to them. Remember, you cannot achieve all your goals at once, so ensure to specify what goals you will prioritize and that every team player is working towards the same goal.

Create a tentative statement

Once you have set your goals and have your team on the same page, you can create a tentative statement or hypothesis. A hypothesis is a predictive statement that proposes a possible outcome of an event.

A hypothesis contains two parts: A proposition and a prediction of expected results. The case is no different when creating a hypothesis for user experience research. Here’s an example of the hypothesis on UX experiment:

If more people are using mobile devices when stuffing the internet, then creating a mobile-responsive design will help drive more traffic because more people will access the web pages easily on mobile devices.

As you can see from the example above, a hypothesis forms a crucial part of a UX experiment. It forms the foundation to A/B test different elements to better understand what works best and what does not.

How to choose a data-driven strategy

The next step is to choose where to begin the research and select what to test. One mistake that people make is to test everything hoping to hit the target. Avoid this at all costs, as the results you get will not suggest impactful actions.

The best place to start is to gather data about your customers’ behaviors. This simplified data-driven approach forms the foundations for a more sophisticated data-driven design process. You can use the available data to apply data-driven techniques before moving to other data collecting methods.

Use data to know your customers

Using data to know your customers better is an excellent approach if you want to know your ideal customer profile (ICP). By reviewing page analytics and analyzing behavior flow, you will have a clear picture of what your customers are doing on your page.

Demographic data and audience analytics will also give a detailed view of your customers.

The information you get is beneficial in decision-making. Through interviews and customer surveys, you will get information on who buys your product or subscribes to a service and why and how they use it. Use the information you get to create ideal user personas.

Next, conduct tests with users matching your ICPs and ask for valuable feedback about what needs to be improved. This will help identify any issues in the early development stages.

Look for anomalies in data

You may want to use this approach if you have enough ICP data but no other things. The best way to spot anomalies in user behavior is to analyze quantitative data from web analytics.

Quantitative data will give insights into the average dwell time, bounce rate, and exit percentages on some subpages.

However, it is important to understand that these indications can mean different things, and numbers from quantitative data will only give insights into what is happening but will not inform you why. This is where qualitative data comes in handy.

A deep dive into qualitative data involves getting in touch with your customers and seeing things from their perspectives. Consider conducting surveys and user interviews to gather more data.

You can also observe an individual matching your ICP using your product in real-time and ask them to share feedback about their experience. Once you spot the problems, develop a few solutions, and A/B test them to determine what solution works best.

Of course, this process will cost you, but the insights you get are worth the price.

Analyze the data

Having data is only part of the equation. You have to transform raw data into valuable information and make it presentable.

Interpreting data, especially quantitative objectively, can be daunting, depending on whether you have a dedicated role or not. Either way, you may want to combine different methods to figure out how to go about it yourself.

For example, when working on your project, you may encounter many different ideas that you do not know how to prioritize. You may also have collected data from different sources, including surveys, user interviews, product analysis, etc.

As such, you need to work on the data and organize it in a presentable way.

Ideally, analyzing the data will help you understand the impact each feature has on the process and boost your confidence level. It provides a clear picture of what you should focus on next.

How to make the most out of your data

When starting on data-driven design, you want to ensure that you get it right. That said, you need to avoid mistakes that could lead to incorrect conclusions. These tips will help get the most out of data:

Collect enough data

If you want a complete view of reality, then ensure to collect enough data before making your conclusions. Drawing conclusions from insignificant data will only make your analysis less valuable and may lead to wrong conclusions.

Even better is to collect data over extended periods of time to ensure the results are statistically significant. It is also advisable to compare your data with stats from competitors in your niche.

Use quantitative and qualitative data

Whenever possible, use the two types of data to gain different insights about your users. As earlier stated, quantitative data reveals customer behavior while qualitative data shows why customers behave the way they do.

Test one feature at a time

Next, test each variable to determine which one has the greatest impact. For instance, when testing the position of your CTA button, it’s advisable not to change its contrast as you will not know which one brought the change.

Be patient

Developing a fully functional data-driven design takes time. As such, when you get the data and derive ways to analyze it, wait and watch. This is the only way to monitor the impacts your changes have on the design.

An important thing to note at this stage is that it will take time for your users to learn the new design of a website or app interface. That said, do not be quick to conclude when you see a positive or a negative change, as it might not reflect the bigger picture.

Allow your customers an adjustment period before you make conclusions and implement the new data.

Wrapping up

There you go… That is what you need to start adopting a data-driven design in your company. The data-driven design provides an excellent way to improve user experience and gives designers a competitive advantage.

A better user experience helps boost conversions and improve Return on Investment (ROI) over time. Of course, user research, analytics, and A/B testing require time but will give an edge as a design professional. The catch is to experiment with the insights you get from the data and be patient before making conclusions.


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