Data assists product design teams in understanding their target users’ pain points, uncovering new patterns, supporting data-driven design, and assuring teams that their work is on track. UX design makes decisions on delivering the best user experience using many types of research data. User information can immediately result in better business outcomes. The data-driven approach in design is a UX technique component that produces measurable, tested results. User experience, data and design, thus, go hand-in-hand.

Designing a website or app requires vision and adaptation that responds to how visitors utilize the site and any evolving organisational goals. The data design shows how a customer is using the product while simultaneously engaging product designers in an ongoing conversation that helps to hone and refine instincts about customers over time.

What is Data-Driven UX?

Data-driven user experience involves data collection from UX tracking, market trends, and customer feedback used to create, test, and improve the UX. Data visualisation UX tools give an accessible approach to examining and comprehending trends, outliers, and patterns in data by utilising visual components, including charts, graphs, and maps.

If the user experience data visualisation is adequately examined, the data can serve as an objective lens to reveal whether aspects of the user experience need only minor tweaks or a complete revamp.

The book “Designing with Data” states that capturing data, management, and data analysis are the best ways to bridge the design, UX and business relevance gap. Three different types of data usage are classified for UX design enhancement.

• Data-Driven Design (when UX is used for optimising performance): Making design choices for improvement that are exclusively based on quantitative data. To do this, some data collecting should be kept in place.
• Data-Informed Design (used when creating a new UX): Combines qualitative information, such as instinct, details, prior experience, or talks with clients, with the objective nature of quantitative data.
• Aware Data Design (fine-tuning a fully functioning UX): Evaluates data insights in the same way that marketing information, first-hand knowledge, and other market trends are evaluated.

When defining the UX, these three design patterns should be considered as a whole because they are not mutually exclusive. The graphic display of information and data is known as data visualization.

This means that in some aspects of the UX, a Data-Driven strategy may be sufficient for understanding how to enhance the design. Other areas may require a Data-Aware or Data-Informed approach because gathering user feedback or behaviour may take too long.

Impact of Data on UX Design

Data collection must be approached carefully to gain user experience data effectively. The data-driven design process has two broad classifications.

• Quantitative: Quantitative data shows a scale of where, when, what and who but doesn’t explain the way.
• Qualitative: The how and why are approached here. For instance, “why does this specific content piece attract more visitors than others?” Qualitative data is often communicated through journey maps and user personas.

Importance of the Relationship Between Data and Design UX

UX designers have a great aptitude for creating distinctive experiences, but balancing this talent with data insights is crucial. Data will let designers know what the users want and expect, and success will follow if those expectations are realised.

The importance of data-driven design is mentioned below.

• Helps to know and understand your user’s needs
• Progressive and doesn’t use traditional practices
• Effective design creation
• Drives innovation by leveraging data

UX Research Methods to Deliver Useful Data

Research methods such as behaviour flows, usability testing, surveys, analytics tracking on apps or websites, and A/B testing are used by data-driven design.

Behaviour Flows

This research method shows how users navigate an app or website, from when they view the first landing page to the last before exiting. A certain path is preferred for users to take in a website. If the actual behaviour flows vary greatly from the preferred path, the user experience gets affected.

Google Analytics has stools built-in to explore use behaviour flows. Analysing this data compared to the ideal behaviour flow created by the UX designer offers useful knowledge about whether the design accomplishes behaviour goals and UX.

Usability Testing

This type of testing allows designers to evaluate the ease of usage of a design solution. Usability testing can be conducted either remotely or in a lab at different stages of a software-development process. Qualitative data is typically gathered about user experiences with a product.

A/B Testing

This type of testing shows you how different websites or app versions perform against each other. These approaches can be used to improve the UX and push user behaviours.

For example, Spotify customers claimed that the lighter interfaces induced headaches, while others thought the dark interfaces felt like Darth Vader. The designers decided to do some explorations forming a key hypothesis.

Rather than the existing experience, which was mostly light, a darker experience focused more. It showcased the content, i.e, the visuals such as the artists, albums, playlists, etc. they concluded that a darker UI would be more engaging and make it easier to find the music people were looking for.

But, it would be a big change for the users and risky to implement because no one likes change. So, the team used standard best practices and user research usability surveys.

Then the team ran an A/B test where 1-5% of the user base was chosen to use the dark interface while the remaining customers stayed with the light interface experience. After conducting a survey, those using the dark interface claimed to have a better experience.

Therefore, constantly running A/B tests improves design, collection of data and UX and results in massive conversion increases.

Conclusion

With the right research and commitment, creating a top-notch UX is simple. Investing in a data-driven strategy with the help of analytics is necessary to maintain the distinctive UX because it keeps the experience interesting. To learn more about data and design, enrol in the 2-year post-graduate program at JD School of Design to receive a well-structured curriculum to thrive in the industry!