Data-driven design: Providing optimal user experience
By: Ishan Manandhar
Tags: Design, User Experience Design, User Experience, User Research, Designer

What is data-driven design?

The process of design is backed by the findings from the data we accumulated. It’s a process on which our design decisions are based on the reflection from the data and user behaviour. A well-crafted user experience designing process involves in measuring the experience itself. This valuable insight can only be reflected in the data. The behaviour and reaction that the users take help us, product designers, to validate our findings, hypothesis and measure the outcomes.

“What’s measured gets managed. Numbers have an important story to tell.”

— Peter Drucker.

These data help product teams to understand their target users, reveals information about users’ pain-points, unearths new trends, supports data-driven design, and assures teams that their work is on right track. User data can lead directly to improved business outcomes. UX methods incorporate data-driven design, which has proven, tangible outcomes.

Why data-driven design?

We designers design products with research, findings and observation from our users. There is no other way to validate our hypothesis that we have decided and made during the process of designing the products. The only way that validate these assumptions is data. If we are relying on the instinct or best practices to make decisions without performing any data-driven investigation, you risk wasting money on changes to design choices that are ineffective or worth not a penny.

The right use, validation, and interpretation of these data help in improving the performance of the product usage as well which also can be measured in figures. Even the best designers in the world cannot predict what users want. It’s the process of learning from our users, which ensures that users problem is solved and their needs is premier to our product’s success and this all needs to be iterative.

Data-driven vs data-informed design

These terms are used and described in the book Designing with Data by Rochelle King, Elizabeth Churchill and Caitlin Tan which helps us to understand and articulate the different terms data-driven design, and contrast it with data-informed and data-aware design.

Data-driven and data-informed represent two different approaches. Both of these approaches uses data as its foundation, and every decision product team takes is evaluated and driven by data. Being data-driven focuses on optimization and efficiencies along the way.

Data-informed means we are not necessarily on one narrow path and that we are working with input beyond quantitative data. A/B testing different experiences or structured usability tests may fall in this category.

Being data-aware means we understand the wide range, and limitations, of data collection and make decisions on which methodology is best on a problem by problem basis. Data-aware teams might find equal value in making decisions based on output from a stakeholder workshop, user interviews, or even statistically significant results from an A/B test.

Research by MIT’s Center for Digital Business found that “companies in the top third of their industry in the use of data-driven decision making were, on average, 5 percent more productive and 6 percent more profitable than their competitors.”

One of the things we discovered is that increasing the number of things that you measure or improving the fidelity of your measurements often actually doesn’t result in certainty. It doesn’t actually result in something that is crisp as this one is better than that. It just reveals a deeper complexity that there are actually more things involved. Then it really becomes a balance. We still have to have an intuition. We still have to make a judgment about what’s important and what isn’t.

— Jon Wiley, Director of Immersive Design at Google

How can we collect data?

Visual data for smart UX

There are many ways on how we can collect both qualitative and quantitative data. Many UX practitioners believe that the data means numbers, but this is a misunderstanding which is a myth. For driving our design with data we need both qualitative and quantitative data. Quantitative data will tell you what actions users take when using our product, while qualitative data will tell you why they do it and, even more important, how they feel about the overall experience. So, we need to incorporate these two techniques in shaping our design decisions.

Quantitative data collection method

The type of data which are numbers and shows who, what, when and where. This type of data shows scale. What quantitative data doesn't tell you is why. We obtain much quantitative data regarding the usage of a Web site or application from Google Analytics, Google Tag Manager, Google Optimize and other measuring tools (eg: Hotjar, Crazy Egg, Optimizely, Usertesting).

A/B Testing

A/B testing is also known as split testing. Hubspot defines A/B testing as:

“… [an] experiment wherein you ‘split’ your audience to test a number of variations of a campaign and determine which performs better. In other words, you can show version A… to one half of your audience, and version B to another.”

When performing an A/B test, it is important to ensure that only one variable is changed (whenever possible), and that the control and experimental groups are similar in size — you can show version A to one half of our audience, and version B to the other. The major goal of the test is to measure the level of comparison.

Analytics

With the analytics we can tell who came to our website and how they got there, how long they have stayed there and what they have clicked. Such data can help in collecting valuable metrics such as average length of the user session, bounce rate, etc. If you want to optimize a conversion rate on our app or website, it’s recommended to start with highly trafficked areas because they will help you gather valuable data faster.

We can also use eye-tracking tools such as heat maps. Heat maps use eye tracking to understand where users are looking on a screen. If heat maps from multiple users indicate a pattern, this could prove valuable when re-organizing content assets or redesigning our website or app.

Survey

UX surveys are a key source of both quantitative and qualitative data in UX research.

A good survey need to have well-designed questions — ensure that our questions are not leading and that the purpose of the question is clear. We should also try to limit the number of questions (no more than 10 to 15) so that users don’t abandon the survey halfway through.

Qualitative data collection method

The type of data that demonstrates why and how. Why does a certain group of users take one action, while another take a different one? Why does one piece of content keep visitors on our Web site longer than another? Qualitative data offers perspective and helps us understand not just what happened, but why and how it happened. We often communicate qualitative data through personas, journey maps or empathy maps.

With good data, we can create a better user experience and influence user behavior more effectively.

User Journey/Flows

Creating a model of how the users use our product can be really helpful in conceptualizing the user are interacting with our product. The information you gather from your user flow can help you identify potential weak areas, providing a starting point for further investigation through A/B testing or user interviews.

Competitor Analysis

It would be a plus point in identifying the weakness of the competitors. The competitor analysis involves examining another company’s product to identify any comparative strengths, weaknesses, or areas for improvement.

It is important to tread carefully when doing a competitor analysis. Simply imitating competitors is not always an effective solution. Instead, it is best to use competitor analyses as a means of gaining inspiration, with this understanding we can improve and their weakness can be our advantage.

Interviews

Interviews are a great way to gather qualitative data from users. It’s a good way to approach the questions with open-ended and close-ended questions. We also might limit the number of interview subjects, the insights gathered through a phone or in-person conversation will be more in-depth than what we could get from a survey alone.

Social media and user feedback

Social media reflects on what expectations users bring to an experience. By exploring common user complaints, you will be able to prioritize product requirements. This activity will also help you get a more relevant in understand their words and reactions to the product offering.