What was the last mobile application you opened?
Maybe you used Uber to book yourself a ride. How easy was it to navigate? Did you feel like you had to follow complex instructions to book a ride? Probably not.
Do you know why? Because Uber spent years perfecting something called UX Design or user experience design in its app.
You can say the same about some other applications that you use daily. WhatsApp, Instagram, Tiktok, Pinterest- all big applications spend years trying to perfect their UI/UX design so that you have the best in-app experience possible.
The way a person engages with a product or service is called user experience (UX). It also includes that person’s feelings and attitude about the experience, as well as their impression of the system.
User Experience Analysis (UX Analysis) analyses every aspect of the experience, from how convenient a product is to how it makes a consumer feel. This goes hand in hand with UI design, or User Interface Design, which refers to a particular product’s visual design elements.
Data-Driven Design may be defined as a method of designing that is based on data discoveries. It’s a method of creating or enhancing a service based on quantifiable criteria. UI/UX designers who reject facts and rely only on intuition risk squandering effort and cash on poor solutions. This false-consensus effect affects many designers, who project their habit patterns and responses onto consumers and make judgments based on their own views and perspectives. Every hypothesis must be confirmed, even though all designers have some idea about what solutions would work for their consumers.
Data may reveal a lot to designers about user behaviour: your consumers’ preferences, for one.
Even if you employ the finest designers on the planet, they won’t be able to foresee what your consumers want since designers aren’t consumers (unless they’re building a service for designers, which is a rare instance).
The gap in knowledge between a designer and a user is vast in business, and it’s just false to believe that designers know exactly what people want and need without appropriate user research, genuine testing, or data.
As a result, designers cannot make judgments based solely on their own opinions. Designers must engage people to collect information and adapt user experiences of the mass consumer successfully.
There are several instances of data-driven UX methods that have resulted in a significant increase in returns. Virgin America, in particular, utilized A/B testing to create a new website design in 2014. As a result,
Another noteworthy example is Music & Arts, an e-commerce website. They employed heuristic evaluation and usability testing to guide a website makeover. Their online revenue grew by around 30% year over year after the project was completed.
Vocier, a luxury luggage manufacturer, also demonstrated this by increasing lead-conversions by 75% without changing their site or service, by just using data-driven approaches. They were able to accomplish this result by correcting minor UX issues.
When we hear the word “data,” we almost immediately conjure up images of quantitative data in the form of numbers.
Data is more than just numbers. Data also includes qualitative data, which includes things like sentiments, opinions, and experiences that cannot be stated numerically. To help with the design phase, you may utilize both quantitative and qualitative data.
So, there are two types of data in question:
Quantitative data is represented by numbers and provides answers to queries such as how many, how much, and how often. Quantitative data sources include website analytics, A/B or multivariate testing, heat-maps from eye-tracking research, and large-sample surveys.
On the other hand, qualitative data concentrates on the “why.” It provides information about the motive and intent of users. Qualitative data may be collected through interviews, competition analysis, usability tests, focus groups, and diary studies.
Both forms of information are useful because they complement one another.
Let’s assume you want to start an online course website where you can offer your audience courses on programming. Unless you make it specifically for people who need that skill, they wouldn’t use it.
You must understand: what are the many industry jobs for which you must give training? What are the topics that people are interested in learning about? What degree of evaluation will they have to face to obtain course credits? You’ll survey your target industry, meet with real people, and collect the necessary data to find out these answers. This information will assist you in developing appropriate user personas, and hence, effective course material.
Using the information gathered from user personas, such as goals, segmentation, and behaviours. It’s simpler to predict how your consumers will engage with your application and what path they’ll take to achieve their goals.
You can only build meaningful user flows if you recognize your users’ habits and motives.
For reference, you’ll need the following data to develop user flows for your online course site: How will a consumer locate the courses on your website? How will they be able to enrol in classes? How will they be able to view the course materials? Again, data is the only way to find out the answers to these questions and then utilize that information to create user flows that will assist your users.
You can now deliver a tailor-made and personalized experience to your consumers based on collected data. A tailored experience will give the consumer a unique personal touch and boost their faith in your service. Spotify, YouTube, and Netflix, for example, present material that the user might be interested in based on data gathered from his or her prior activity within these services. The data acquired by smart devices may now readily be used to establish a tailored experience for consumers in this digital environment.
You must watch your users’ interactions inside your online courses site in order to give them a tailored experience. You may use an analytics program to learn: How a user interacts with your site, What courses they search for on your site, and Which pages they spend the most time on. You’ll gather and evaluate this information in order to create a tailored experience for your users.
We ought to connect the dots between data and design enhancements to utilize data to determine your unique consumers’ desires, challenges, and requirements in order to use data to guide design in a significant way.
Data on its own isn’t very useful. To build meaningful product experiences, you must first analyse data in order to transform raw data
If you’re utilizing data to guide design decisions, you have three options:
Proving: Validate design decisions using A/B testing and analytics.
Improving: put your plan into action and track the results. This usually refers to data-driven design iteration, which involves tracking changes over time and using data to improve the products.
Discovering: Rather than merely analysing, synthesize (use data to explore new patterns and delve deeper into problems).
It’s no wonder that empathy — the capacity to go outside of oneself and view the world through the eyes of others — is essential for creating a valuable product.
It’s critical to remember that data is produced by and about humans, not algorithms. The traces of human behaviour should be represented through this data. As a result, look for human tales to help you make sense of your data:
Look for data to learn more about what individuals say, do, think, and feel. Determine what is most important to them.
Find out why humans do or don’t do things based on their emotions. Recognize the motives and pain points of your users.
When it comes to innovation and design, we should think of data as something that helps us make judgments about what to do next, but we shouldn’t allow data to make those decisions for us. Following a solely data-driven strategy has a few drawbacks. To begin with, metrics are restricted since they are dependent on what you have previously rolled out for the application.
Taking everything into consideration, you realize that you only have a small fraction of the data that you’ll need to create a winning product. Secondly, no amount of data or empathy can compensate for the fact that a designer must make judgments about how to implement a design.
This implies that a designer must be a curator of what is and isn’t relevant. You must have a clear idea of what you want to accomplish. It is possible to achieve your goals. It develops through time as a result of experience: making judgments, making errors, and learning from them. Data may be used to support and validate your idea.
While collecting the data you require isn’t the most difficult element of establishing a data-driven design process, dealing with the data will almost certainly be. This is arguably the most significant need for starting up with a data-driven design: patience is critical to your work’s progress. It’s vital to keep track of the consequences of your design modifications and analyse the overall market reaction to them. Bear in mind that no matter what changes you make; your users will need to adjust for a while. However, you can better influence company decisions and persuade decision-makers that streamlining is required if you have qualitative and quantitative evidence.
With mobile application experiences, augmented reality (AR), and virtual reality (VR) technologies slowly grabbing the market, UX designers can now take advantage of new business prospects through data-driven design. A product manager should base his product decisions on competitive product knowledge as well as existing rival items. In companies where heuristic assessment is the most widely utilized method for assisting product owners determine whether to redesign an interface, data-driven design plays a critical part in the UX design process.