The Value Of Data Science In Improving Online User Experience

UX is a broad discipline which seeks to understand and optimize an experience- with the goal of delighting the user. It considers what they are trying to achieve, the steps they take to get there and the scenario in which they take their journey.

Calgar Araz brings together the three aspects of UX in his article which looks at the common misunderstandings of the discipline – let’s take a quick look at them:

1. Subjectivity – it’s about the user, not the object!

“Usability exists in the experience of the person, if the person experiences a system as usable, it is. A commitment to designing for people means that, at the base, we must accept their judgment as for the final criterion for usability […] The starting point for usability engineering must be the uncovering of user experience.”

2. Contributing agents – The user, the object …and the context.

Elements contributing to the user experience include:

The ‘Object’, which might be a website, portal or app or it could also be a service;

The ‘User’ and their needs, motivations, expectations, and predispositions;

The ‘Context’, this is the location, time, culture, device and scenario in which they are interacting.

These three agents not only contribute individually but also integrate with each other

to affect the user experience, and as such, they all need to be considered when thinking about UX.

3. Singular And Accumulated Experiences

The widely accepted definition of UX by Norman and Nielsen states that

“User Experience encompasses all aspects of the end user’s interaction with the company, its services, and its products.”

This means that it isn’t about a single, isolated event or interaction which affects the UX, but in fact, each experience builds upon one another.

Consider buying a new car, you may see an advert on TV, go to the website, meet the salesperson in the showroom, go for a test drive and then purchase the vehicle. All of the individual elements leading up to the purchase and even afterward are shaping the ‘user experience’. However, if the website is difficult to use, the salesperson is unhelpful or the test drive doesn’t meet your expectations set in the tv ad, you likely won’t buy the car.

In the context of website or app design, the objective is to create an experience which is intuitive and easy, without any frustration or confusion. For most organizations their aim to increase conversion – maybe an inquiry, a sale or content download. It may also be about cost saving, such as reducing calls to a contact center or just keeping their audience engaged and loyal.

So, if UX is about optimizing an experience for a user, and goes beyond just the design of an isolated interface, what does that have to do with data science?

In order to understand the user and optimize their experiences, UX is built upon research and insight (rather than assumptions and opinions) alongside industry best practice.

Typically, UX relies on a mixture of qualitative and quantitative research in order to inform experience design decisions. Qualitative UX research includes interviews, workshops, and observations which are often limited to relatively small scale insight.

This is where data science comes into play. There is such an extensive amount of data generated via platforms and devices which could be used for analysis, but for the data to provide real value it needs to deliver relevant insights and be viewed in context.

“Aggregated information about how users interact with UIs through clicking, tapping, typing, swiping, navigating, moving the mouse around and more, constitute what we call “big behavioral data” – and it can provide valuable clues about the usability of our interfaces”, says Tim Rotolo

So with this in mind, what are the benefits of big data for UX?

• It’s user-generated data- a vital component for an authentic user-centric approach

• It can show how users behave and interact with your product (with the large data set able to highlight trends)

• Common behavioral patterns can be identified and extrapolated for UX and usability issue

• Big data can be used for predictive analytics and anticipate how people will behave and deliver solutions that better meet user needs

• It provides the large scale evidence needed to support business decisions

An Example

Netflix, the video streaming service, collects a whole host of the user viewing data including when people pause or stop watching midway through an episode or series. They famously used this research as an integral part of informing the production of ‘House of Cards’. Using data-driven insights on audience preferences, Netflix crafted the series to specifically align to what the viewers liked and disliked, therefore creating an optimized viewing experience.

So how does the quantitative and qualitative work together in UX?

While the big data insight can expose the ‘what’ is happening, the qualitative insights bring in the ‘why’. It is the complementary nature of these two elements that feed into crafting an optimized user experience. Ultimately, UX and big data need each other to obtain a much richer understanding of the user to create a truly optimal experience.

Caroline Richardson, UX Designer at DotLabel says “A UX Designer should never say “I think the user would want…” because it isn’t their job to assume; data should inform us of what the user wants.”