4 Ways to Implement Data for a Better User Experience Design

Designing an optimum user experience can typically be broken down into 5 steps: Empathizing, Defining, Ideating, Prototyping, and Testing. When attempting to define the typical user experience for a product or service, a high-quality data analytics program will become your new best friend. Using industry-standard data visualization tools such as Tableau, Microsoft Power BI, or Adobe Analytics, UX researchers can make game-changing discoveries on a company’s target audience, user sentiments, grouping data, survey data, and more.

To help you make the most of these state-of-the-art data visualization tools, UXAX has assembled a guide to our top 4 tips for leveraging data to create an impactful user experience.

How Can Companies Connect Data Analytics to UX?

Understanding the Relationship Between Data Gathering and UX Practices is Critical for Creating a Tailored Customer Experience

Many tech companies nowadays focus on creating user-centered products. By combining best practices for data analytics and UX design, companies can successfully create valuable online experiences for their customers.

But when do we begin to implement data analytics into our design life cycle?

Design components can be broken down into 4 steps:

1. Create user personas
2. Begin defining the task models
3. Redesign the UX
4. Conduct a heuristic evaluation

Whenever you begin working on data-driven UX, you should ask yourself:

• Why is data essential for this particular design?
• How can you efficiently collect useful data?
• What is the right way to use data once you have collected it?

Let’s examine each step in detail.

1. Create user personas

What is a user persona? In short, user personas are fictional representations of your dream customer. Creating these personas can help you understand your target users’ needs, experiences, behaviors, and goals.

In order to create an accurate persona, you will need to perform the following:

• Background research on your target audience
• Qualitative and quantitative research on customers
• Objective market research on the industry your company inhabits

Using data analytics tools, you can gather information on a user such as their:

• Age
• Geography
• Spending Capacity
• Average Screen Time
• Behavior and Characteristics
• Occupation
• Education

With this information, you can make connections between a person’s age and spending habits or average screen time and occupation.

By performing this research and creating a user persona for various customers, you can create an accurate representation of your target audience’s needs and its primary motivations and goals. You can also help various designers and stakeholders stay on the same page when it comes to who your audience members are and to inform your future business decisions.

2. Begin defining the task models

UX designers often rely on a logical task model to help them build a successful user experience. Task models can be defined as “descriptions of various tasks within a workflow.” In order to start defining these models, researchers must document relevant business and user information in a comprehensive format.

For instance, if we want to find trends on users who spend more than 4 hours on the phone, we can research information on factors such as:

• The apps that they use
• The percentage of time they spend on social media
• Their age

Once we gather this information, we can begin using data analytics tools to observe trend lines and employ bubble plots to show instances of segregation.

Implementing Data for Task Models:

1. Identify the trend you want to analyze
2. Break the task down into sub-tasks and segment the data as either categorical or continuous; make sure it is specified in terms of objectives and areas of interest
3. Draw a layered task diagram of each sub-task ensuring it is in line with the data you have segmented
4. Produce the data visualization

3. Redesign the UX

No matter how innovative your idea is, a bad UX design can jeopardize your project. That’s why you need to always support your final decisions with some solid numbers. Working out these different relations for your data often works best when redesigning your user experience.

To start, you can exam past factors that succeeded and those that failed. These findings should be supported by data clustering or segmentation. You can also divide your data into positives and negatives. By now, you should understand what has worked and what hasn’t in the past.

Let’s take a look at this situation:

Company A is facing a problem. Whenever users arrive to their payment page, they drop out mid-transaction. To fix this problem, a UX researcher must identify what is causing this drop out rate before redesigning the page to address it. Finding the average length of time it takes for a user to drop out after reaching the payment page can help researchers determine what is causing the customer to leave.

4. Conduct a heuristic evaluation

Heuristic evaluation involves finding the usability problems in a UI design to improve them during an iterative design process. A small set of evaluators usually examines the UI/UX and determine its compliance compared with recognized usability principles.

To complete a heuristic evaluation, remember to complete both qualitative and quantitative data search:

1. Conduct qualitative data research to learn the why’s of user behavior. Gather this data into clusters before deriving regression models to pull up qualitative outcomes. These outcomes must be non-numerical, such as a person’s preferences, feelings, or inclinations.
2. Once there is a prototype is available for user testing, use quantitative data research to learn how people actually interact with the product. Quantitative data is based on measurable evidence, such as the completion time for a task, and is acquired through methods including A/B testing, and even eye-tracking.

Are you ready to learn more about using data analytics to create an unforgettable customer experience? Contact the team at UXAX today for more information.