Big Data: Revolutionizing UX Design Strategies, Part 1

Inference and Logic

Strategizing UX design through deduction and reasoning follows this age-old idiom: “Put yourself in someone else’s shoes.” In some cases, UX designers and other product-team members simply interact with a platform, going through the user journey themselves to try to understand the perspective of the consumer and figure out the best way to provide a convenient user experience. Different teams across a company use a Web site or app, then come up with UX-design solutions to optimize their platform.

This approach neither provides a reliable source of data nor enables teams to empathize with the consumer, for two reasons:

1. Internal teams are significantly more knowledgeable about the platform or service than the user, so they cannot emulate the user’s interactions or thought patterns.
2. The set agenda of those who are involved in the process is to go through an entire process and figure out its weaknesses. The problem is: users don’t always intend to complete a user journey.

The first of these reasons is quite straightforward. A knowledge gap exists between the product team and the users, and the team is likely to overlook this gap in assessing the user’s knowledge about the platform. As a result, users would be unable to navigate the platform in the same way that the team members can because the users are relatively unaware of the ins and outs of the platform and its services.

The second problem is a bit more dynamic. When testing the user experience, the product team already knows they have to go through the entire process. So they compartmentalize, treating each part of the process as an individual component and judging its efficiency accordingly.

On the flip side, when consumers come to a digital platform, they know they don’t necessarily have to complete a process. So the likelihood already exists that the product team’s inference would cause them to miss the opportunity of creating a hook to keep users engaged in the process and moving forward.

Moreover, while this approach might enable the team to optimize individual components of a site or app, it wouldn’t necessarily mean that the entire user journey would be as smooth as each of the components. The transition from attracting consumers with good information to landing sales can be porous, resulting in the loss of a lot of potential business in the process. This typically happens because users don’t see the journey as linear.

However, after taking the first step in the journey, the product team would base their understanding of where the user should go next on their own on-platform activity—a data set from a team that is not representative of the consumer base.

Experience-Based Decision-Making

Experience-based UX design strategies present a different kind of problem. While they integrate actions that have shown positive results in prior years, they would always be based on outdated trends or user behaviors.

Relying on experience would result in the deployment of a user experience that had not evolved over time, hindering the ability of the business to stand out from their competitors and discouraging innovation because of the risk of failure in trying something new.

What businesses have to realize is that relying on outdated data is not the right way to leverage data. Just because a data set exists doesn’t mean that it’s the best source of information for making UX design decisions.

Plus, experience-based design strategies are often based on individual’s experiences. As time progresses, these strategies would stop delivering the kinds of results they would have delivered five years earlier when markets were not as competitive as they are today in the digital world.

On the bright side, big data is eliminating businesses’ dependency on fixed data sets that don’t represent modern trends. Data analytics has the ability to provide data from recent user activity and behavior patterns. The role of experience-based decision-making should be to execute a UX design strategy that is based on current data rather than deciding on which historical data set to base the strategy.

Trial and Error

The strategy of trial and error has existed for centuries—for doing just about anything and everything. So it is no surprise that it has found a place among businesses’ approaches to UX design.

For trial and error, the key has always been to have both short-term and long-term strategies in place. But, for UX design, it isn’t as simple as having a long-term alternative in mind.

Optimizing a digital platform means you must design the best possible user journey for the present. This has resulted in a few unique challenges for product teams. I’ve seen some of the following questions go unanswered:

• Which of our ideas should we implement first?
• What should the metric for success be for the UX design?
• How long should we stick to one strategy before scrapping it?

These questions are almost impossible to answer unless there is sufficient data available. For businesses, relying on trial and error in designing the user experience has meant their growth projections have been in constant flux.

Another problem that is a byproduct of this trial-and-error approach: in circumstances where a business is growing, it is hard to know whether the UX strategy they’ve implemented is the force behind that growth, or it is the result of something else such as increased demand for a complementary product. Therefore, a dilemma results when UX designers seek to justify the user experience they’ve designed and its role in a company’s success.

However, the biggest resulting problem for businesses has been the lack of a clear vision for user experience. As a result, once you get the user experience right, if trends then change, getting it right again is based completely on chance, just as it was the first time around.

With the use of data, once you nail down an optimal UX design and validate its results through activity tracking and monitoring, you can pinpoint a user interface’s strengths and weaknesses. This lets you employ the same strategic approach—if and when user behaviors start deviating from the current trends.

The biggest limitation of a trial-and-error approach is simple: You might get a UX design strategy right once, but you cannot guarantee consistent results in the absence of relevant data. For businesses, this means the possibility exists of hurting both existing clients and potential clients, which would subsequently reduce the business’s ability to generate more revenue through a convenient user experience.

Consumer Surveys

Some companies leverage surveys of a sample user base to map out weaknesses in the user experience. While such surveys do generate data that can guide your UX design strategy, they have one specific limitation that reduces their long-term impact.

Much as with every other kind of data, these surveys can become outdated. Continuous data generation is not possible using consumer surveys. The cost of repeating such surveys is often too great to bear over a long period of time. So, at some point in the future, your UX design would stop delivering the same results as before.

Another problem I’ve seen with consumer surveys is that, while the sample group of people you’ve surveyed might have been chosen based on a generalized user persona, that persona is not representative of the actual target audience.

This is not to say that consumer surveys don’t work. The criticism here lies in how the sample group was chosen and how companies derive a consumer profile. One approach that is always misguided is the attempt to create an ideal-consumer persona.

As a matter of fact, no business has an ideal consumer. The approach in attempting to create the perfect consumer persona is to create one generalized consumer persona, then segment the different characteristics of this consumer to derive strategies for more specific target audiences. This sort of market segmentation is becoming more and more popular with the rise of big data because it offers something that is more solid than mere guesswork.

What’s Next for UX Design Teams?

Big data is empowering all areas of expertise in unique ways, and UX teams can benefit from this empowerment. Using mature data with trend trails and predictive analysis is a critical component in designing UX strategies.

This gives businesses the ability to reshape their user engagement and deliver an experience that attracts and converts new clients and keeps your current clients happy, so the business generates more revenues through customer attraction and retention. But this is a challenging task, to say the least.

In Part 2 of this series, I’ll explain how to leverage data for augmented performance. The potential that big data provides is enormous, and businesses that latch onto this trend will be able to provide a significantly better user experience that is intuitive, seamless, and engaging for their target audience.