In the current business climate, data is the lynchpin that enables companies and project teams to make smarter decisions, and create validated solutions that lead to better business outcomes. 

But contrary to the evidence, it’s not simply a case of the more information, the better. While a data-driven approach sounds robust in theory, if businesses rely solely on data this becomes their single source of truth. The issue with adopting a purely data-driven approach is that it can often become a crutch. 

There’s no denying that data is essential for decision-making optimisation in business, however a dogged reliance in numbers that disregards other sources of insight or research methods is akin to seeing the world in black and white. 

This data-driven focus becomes particularly problematic when creating user experiences. A blinkered reliance on data alone provides an incomplete picture, meaning that the UX designers aren’t making decisions based on all of the available information. 

This inevitably results in a sub-optimal user experience—the opposite of what the organisation is aiming for.  

Having an approach that’s informed by data, not dictated by it, empowers businesses to form a more holistic understanding of their users’ needs and motivations, thereby better equipping them to design more customer-centric solutions. 

By undertaking activities like customer interviews and user testing, businesses are able to test their data-driven assumptions, and achieve more robust solutions that more accurately respond to the needs of their clients and their customers.

Differentiating between data-only and data-informed decision making

Data is one of a number of key inputs that help create validated solutions when optimising or building digital assets from scratch. The human factor can never be ignored.

Data-only design makes decisions based solely on quantitative research—decisions that are based purely on data. Quantitative research methods provide numerical data that show the who, what, when, and where. Some examples include analytics, A/B testing, multiple-choice surveys and heat maps. This is factual, incontrovertible evidence—”hard data”.

 

Data-informed design makes decisions based on multiple factors. It takes both quantitative data and qualitative insights into account, along with the use of design conventions, experience, and designer instinct. Qualitative research methods are focused on non-numerical information that informs the why or how. Some examples of this include competitor analyses, customer interviews, and user journey or user flows. Data-informed design provides the colour, the personality.

Businesses can avoid falling into the trap of data-only decision making by creating a design process that incorporates both quantitative and qualitative research methods, and employ design thinking principles, first-hand experience, and the long-trusted ‘gut feeling’ to guide them toward better outcomes.  

Data is one part of the story, not the whole thing.

Data should not be the only deciding factor

When data becomes the only insight on which to base design decisions, it ignores why users engage with a brand’s digital content. This myopic view can have a great impact on customer strategy, as it doesn’t allow for any direct input from humans—the very focus for which improving engagement is designed.

A data-only approach may result in the chasing of “optimisation” to generate more user engagement, at the expense of the overall user experience.

Let’s look at the example of a business that has determined the need for on-site optimisation. Their research data directed them to “optimise”, to improve their customer engagement by making their on-site elements stand out more. 

But without a clear customer strategy there is no specific focus for their optimisation. So, everything gets optimised. Buttons are made bigger, more design elements are added, and more calls to action are included. 

In the end, the weight of the buttons and other on-site elements only serves to confuse the hierarchy of content—and the end-user. So while the data implies that optimising the on-screen elements is necessary, it didn’t take into account the user experience. The hierarchy of content gets lost, and the end result is incoherent and imbalanced.

When everything shouts, nothing stands out. 

Another common example of how a data-only approach can be taken too far, and end up annoying your customers, is through the use of promotional or signup pop-ups. Your data may indicate that these pop-ups drive conversion, so your business implements them. 

But as Gill Andrews points out in her data-backed ‘Reason To Get Rid Of Pop-Up Forms’:

“If you are a large business owner, on average you’ll need roughly 271 people to see your pop-up before you get 1 subscriber who also clicks on a link in your email.” 

This number increases to 1319 people for small-to-medium businesses. 

Yes, it’s true that pop-ups do direct your user to act. But it’s important to consider whether the user actually enjoys this experience or not. While the data indicates that having 1319 people see your pop-up will work to increase conversions, in practice, is the major disruption to the experience of the remaining 1318 users worth it?

The numbers don’t lie—but they don’t tell the whole story. It’s important to consider the user’s fragmented experience, and how this can work to sully your brand’s reputation in the process.

There are other ways to promote your services that are less intrusive, like integrating opt-ins into quality, brand-enhancing content that your customers are willingly seeking out. It’s a more organic experience that customers can choose to engage with. 

If businesses were to use data-only decision making as their guiding light, they would ignore the impact on the vast majority of users who engage with their brand online, and the subsequent negative flow-on effect for their reputation.

Making the case for data-informed decision making

A data-informed approach provides businesses with a holistic understanding of the problems to be solved, as both data and qualitative insights can be used together to form a more accurate point of view. It helps inform not just user performance, but also how the user feels about their experience, and delivers a more in-depth view of what they actually want from an experience.

This holistic view can be weighed up against the business’ CX strategy to determine relevancy and alignment, then fed into the interaction design stage of the project. Prototypes can be tested through qualitative research, then refined and tested again if deemed necessary.

Having a customer experience strategy and a data-informed approach puts the customer at the centre of the design process—it focuses on their needs, and doesn’t leave decisions to be dictated by the almighty data gods.

This provides businesses with increased confidence in their decisions, and results in better outcomes for both the business and their customers.

Yes, data is a crucial element of user experience design, but it should be one element of the design process. When used correctly, data can be a powerful tool to help inform design decisions, but not something to solely base them on.

At Equilibrium, our Experience and DDM teams work closely together to surface and understand problems, and create validated design solutions by incorporating a range of quantitative and qualitative research methods. We utilise the power of data in conjunction with a breadth of research methods to ensure the best possible understanding of problems we’re trying to solve. 

This approach enables us to achieve optimum outcomes for our clients, and deliver improved experiences for their customers.

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