Big Data: Revolutionizing UX Design Strategies, Part 2

Making UX Design Changes Based on Survey Data

Let’s suppose that a survey you’ve conducted revealed that your platform provides a great user experience for most people—except those who are differently able. In the absence of your survey data, your UX designers would not be able to accommodate this segment of your audience.

With big data, businesses can leverage trends within segments of their user base to optimize their platforms. For example, if your data suggests a trend among users who are differently able, you can create or improve a user experience for these people.

WCAG 2.0 establishes an accessibility model that Web developers and designers should use to make their platforms more accessible. The problem is that most UX designers don’t really know their users’ demographics or their specific struggles. Data is necessary to decide whether to overhaul a current user experience or create a new one and impacts decisions regarding button placements, content placement, content-delivery style, and navigation. In fact, it affects the entire user journey for a Web site, mobile app, or any other platform.

The distribution of the survey data can help you understand the trends among your target audience. For example, you can understand the kind of user experience early adopters of your product are seeking and the difficulties they encounter in using it.

By creating your own survey, you can ensure that you control the data set and eliminate any and all bias from the process. You can conduct an analysis of the data that you collect to pinpoint the prevailing problems relating to the user experience, then iterate the design to solve them. This ensures that you make targeted changes relying on concrete information and that you can potentially drive up conversions and revenue.

Deriving Trends from Pre-existing Data

However, if you can’t conduct your own survey, accessing Internet archives, online surveys, and surveys from other sources can be an excellent way to start optimizing your product according to market demand and trends.

The limitations of data from other sources are that they might be outdated, biased, or have a target audience that, while it resembles your audience, is not an exact match for your audience. However, gathering such data is an excellent way of understanding general industry trends.

Using data from such archives can provide great resources and indicate the general direction you should take. For example:

• Loading speed is one of the most vital elements of the user experience. According to one study, a one-second delay in loading a Web page reduces page views by 11%.
• Similarly, 67% of the millennial population in the US would purchase a product or service from brands that offer a chatbot. So, if you’re having difficulty converting customers on your ecommerce store or if customers add products to their cart, then don’t actually buy anything, adding a chatbot might improve their user experience and help them complete their buyer journey. Moreover, according to OutGrow, 53% of customers are more likely to engage with businesses they can message. Since consumers expect quick responses and 24/7 engagement, chatbots might be incredibly useful in elevating your platform user experience.

This strategy can be a useful way of catching up with the market. Because most of this data is public knowledge, businesses may already be making such changes to optimize their platforms. Instead of relying on guesswork and inference, use data-oriented decision-making to offer your customers a better user experience on your mobile app or ecommerce store. This would give your business a better chance of achieving success in the marketplace.

Predictive Analysis

Predictive analysis generally relies on user-generated data, and looking at consumer patterns is useful for future planning and forecasting future customer behavior.

Let’s take a look at some predictive-analytics models that you can use in making better UX design decisions.

Predictive Analytics Models

Predictive models are the cornerstone of predictive analytics. They allow businesses to turn data—both past and present—into actionable insights. Analysis that helps you make better decisions relating both to UX design and the overall business creates long-term, positive business results.

Some of the most commonly used predictive models for UX design strategies include the following:

• customer lifetime value model—This model helps businesses use customer segmentation to identify consumers who are likely to invest in their products or services.
• customer segmentation model—This model helps to group consumers based on their characteristics, demographics, and purchasing patterns.

These models help businesses understand survey responses, determine the proper weight of those responses in devising a UX design strategy, and pinpoint what type of people are facing what problems.

Depending on what you learn, you can decide whether to implement a change based on the impact a particular segment of your customer base could have on your business. You’ll rely primarily on the percentage of your entire customer base that this segment represents.

What is the process flow for utilizing predictive analytics?

1. Acquire data from external sources or gather your own data.
2. Cleanse your data sets by eliminating outliers, then assimilate the data sets.
3. Develop and implement an accurate predictive model.
4. Integrate the predictive model into your platform.

Making Run-time Improvements Using Analytics

Run-time analytics are an invaluable resource for product teams. They help teams discover relevant details about a digital product’s performance.

You can use tools such as Firebase and iTunes Connect to track user behavior and activity on the platform, assess the platform’s weakness, and optimize the platform to improve overall performance.

In assessing where the user experience is hurting your business, there are a few crucial questions you can answer through big-data analytics. Let’s look at these questions and consider how you can use them to improve your UX design strategy.

Where is your customer leaving your product?

On a platform, the section, tab, or step from which users are exiting your product without completing a process or user journey can provide valuable insight into where your platform is lacking.

If you have an ecommerce store on which people are searching for things, but not adding products to their cart, this could mean you need to add more filters to your search feature.

If users are leaving from a particular section, look at the directions you’re providing to guide the user through the user journey. Often, a lack of clarity on just one page can result in the user’s not completing the consumer cycle.

This is something I have experienced first-hand. When working on a product, one of my colleagues started using its beta version. Sitting next to me, he tried to register for an account. I could see that he was struggling in places I would never have imagined. Why? Because there was a minor problem with the phrasing of the directions.

As a result, he was having difficulty understanding what one of the features was supposed to do. This was a user who understood the product concept, but still faced some difficulty with the user experience. Had this been a live product and a real user, I think that user would have had every reason to leave the platform and never return.

This is why monitoring user activity can be incredibly helpful. Data analytics give you this information. In the absence of this data, you simply would not know what users are doing. Therefore, you’d never know that such a small mistake was resulting in a massive problem for users.

What features do users most frequently use?

Monitoring user behavior to identify the sections or features that people use most frequently can help you identify the best way to design the navigation for a platform.

All parts of a screen might not be equally accessible because people hold their devices in different ways. Therefore, the features that people use more frequently should be in a position that makes it easier for people to reach them.

Take, for example, the Play/Pause button in a music app. Why is the button always centered on the screen? Because it is common knowledge that this button is essential to using the app.

If it took three steps to pause a song, most users would hate using the app. Requiring unnecessary effort to do something as simple and frequent as playing and pausing a song would be problematic. In designing a user experience, such things matter, and there are many such examples.

How long does it take users to complete a step?

The importance of gathering this data through analytics is highly underrated. When you’re designing a process, there is always an average time it should take users to complete it.

Run-time analytics let you see whether the average time users are taking to complete a process is exceeding the projected time. If the data shows the time users are spending on the process is consistently greater than it should be, there’s a problem.

This data helps UX design teams improve processes, making their platform seamless and intuitive for users—even for those who are not tech-savvy or are new to the platform.

What Does Big-Data Analytics Let Businesses Achieve?

From a UX design perspective, leveraging big-data analytics helps you create a seamless, intuitive platform. This platform is likely to have a great bounce rate, provide excellent navigation, be accessible, and guide customers through the consumer journey, making it easier for them to complete their activities.

Small additions to a user interface such as a progress bar could help improve customer retention—especially if users are leaving your site because of the time it takes to complete a process. How would you know that’s a problem? You wouldn’t—unless you’re leveraging data analytics.

Through big-data analytics, businesses have a new way to look at design, content, and navigation. You can see what your users are doing rather than assuming what they’re doing.

When you want to improve your user experience, you can pinpoint exactly where in the consumer cycle your target audience is having less than an optimal user experience and work to solve that specific problem. Your product team can focus on trying to find new ways to solve a problem rather than on trying to identify the problem. Big data and analytics do that for you.