User Experience Design That Is Data-Driven
The process of developing a user-centered product should be informed by research that demonstrates what works and what doesn't for individuals who use the product. It's not unusual for a great idea to fail to transition smoothly from the drawing board to the finished product. To reduce the chance of failure, an increasing number of product designers are collecting and analyzing data as they create products.
Product designers may employ data-driven design to better understand how people engage with their goods. In this post, we'll look at how to connect data to a web design and how to utilize the results to better future designs.
What is data-driven design, and how does it work?
Data-driven design may be defined as a method of designing that is based on data discoveries. It's a method of creating or upgrading a product based on quantifiable criteria. Designers who reject evidence and rely exclusively on intuition risk squandering time and money on useless solutions. Many designers suffer from the false-consensus effect, in which they project their own behaviors and responses onto consumers and base their judgments on their own beliefs and experiences. Every hypothesis must be confirmed, even though all designers have theories and assumptions about what solutions would work for their users.
Data can reveal a lot about user behavior, such as their preferences, what they like and hate, how they engage with digital products, and which devices they use.
How to make data work for you throughout the design process
When planning your design process as a product designer, you have a number of opportunity to use data. In general, data may assist designers in making decisions and finding solutions to challenges at any level of the product design process. However, this can only happen if designers understand what data they want and how to create a suitable design with it.
Here are a few things to keep in mind if you want to use data efficiently in your process:
Recognize the demands of users as well as the objectives of the company.
It's not simply about collecting as much data as possible when it comes to data-driven design. It's all about gathering data that can help you uncover insights into user behavior and applying that information to improve your product. As a result, it's critical to first comprehend user requirements and company objectives. Invest time and effort in user research, and choose a set of KPIs that correspond to your company objectives.
Create a list of your major data sources
When it comes to incorporating data into your design process, you should always begin by assessing your data sources. When we compare a startup that has just published its first product to a firm that has built a sustained and lucrative internet business, it is clear that the data-gathering processes are very different. In general, a startup's data collection process will be more difficult since it will lack a steady user base and it will be difficult to discover a cost-effective approach to evaluate user activity. As a result, it's critical to know how much time and effort you'll be able to devote to data collection up front. This information will aid in the planning of design activities; team members will prioritize tasks and improve the research-design-validation process.
Visualize the data
Many people believe that they are "visual learners." One of the most effective methods to draw attention to crucial points is to present facts aesthetically. It is much simpler to capture your audience and deliver your message when you use data visualization as a tool. This is especially true when dealing with big amounts of data (like COVID-19 dashboards). Even basic charts and graphs can help people understand the material better.
When you want to see connections between a few indicators on multiple platforms, such as time-on-task, user confidence, and job completion, visuals are extremely useful.
What's the difference between data-driven and data-informed design?
Two techniques to working with data are data-driven and data-informed design. Data is the core of the data-driven design method, and it is used to analyze every choice. Data is utilized as a reference when a design choice is made in data-informed design.
Both models might be advantageous depending on the nature of your job. When it comes to performance optimization, for example, a data-driven strategy may be appropriate—quantitative measurements like time-to-load may assist you identify when performance bottlenecks will occur. Data-informed design, on the other hand, is ideal for determining what difficulties a user has while interacting with your product, introducing design modifications that will address those issues, and measuring the effect of those changes.
Combine quantitative and qualitative data
Many UX professionals feel that data is simply a collection of numbers. This is a prevalent misconception. While quantitative data is the bedrock of data-driven design, you shouldn't make your choice purely on it. It is advisable to utilize a blend of quantitative and qualitative methodologies while developing using data. Why? Because quantitative data will tell you what actions consumers take while using your product, but qualitative data will tell you why they do it and, more importantly, how they feel about it.
It's also vital to note that relying on a single research approach will not provide you with the amount of knowledge required to make effective adjustments. So let's have a look at some of the most common quantitative and qualitative methodologies.
Methods for gathering quantitative data
The answer to the question "what do people do when they use your product?" may be found in quantitative data collecting approaches. The methods listed below will also assist you in determining which aspects of your product (features) to measure first and how to do so (what metrics you want to use for that).
Multivariate testing and A/B testing
A/B testing (also known as bucket testing) is a technique for comparing the performance of two or more pages or screens at the same time. A/B tests compare two variants of a design (for example, the color of a call-to-action button) to evaluate which one works better. A/B tests are simple to do since you may display one half of your audience version A and the other half version B. The purpose of this testing is to determine which version of your design is more effective for your users by assessing conversion rates (e.g., for a landing page, this might be the number of sign-ups).
Multivariate tests, on the other hand, alter many variables (like an entire header of a page). Multivariate testing involves defining combinations of variables. The purpose of multivariate testing is to figure out which combination out of all the possibilities performs the best. Regularly doing A/B or multivariate testing can help you achieve greater conversion rates because the data will reveal which solutions are most effective for your target audience.
Web design analytics may reveal who has visited your site, how they arrived, how long they remained, and what they clicked. Tools like Adobe Analytics and Google Analytics may help you collect useful analytics like average session time, bounce rate, and so on. If you want to improve your app's or website's conversion rate, start with heavily visited regions because they will help you collect vital data quickly.
Heat maps and other eye-tracking technologies can also be used. You'll be able to develop interaction patterns based on big groups of visitors focusing their attention on a certain region of your page/screen if you see this behavior.