Data-Driven Design: The Data Duck Way
Never design blind.
When designing we are guided by the invisible hand of data: information about what works and what doesn’t. Features that look good on paper may end up being impractical or confusing, preventing users from achieving their desired outcomes. Failing to consider data (or using data in an ineffective way) can have serious implications for the success of a project.
If you rely solely on 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 even harmful).
Using data effectively can lead directly to improved business outcomes. 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.”
The Data Duck started in 2008 as a small design firm, using data to create User Experience designs. We have come a long way since then, but we’ve always stuck to our core: data led design.
Design that is backed by data and helps designers understand the target audience. It increases the likelihood that our designs are objective. Data insights reveal the users’ pain points and opportunities while unearthing new trends.
Here’s a little peek into our methodology.
For example, perhaps your product has already launched, and you’re looking to design a new iteration of it with some improvements. Your data-gathering process will look very different than that of someone creating a new product from scratch. It might not always be realistic or cost-effective to overhaul your entire product or business concept.
Ideally, you’ll want to make data-driven decisions right from the very beginning. But realistically, you’ll need to consider the unique needs of your organization when deciding how data can help you.
Data-Driven, Data-Informed, and Data-Aware Design
A data-aware design team would put quantitative data on an equal footing with other decision-making factors. This kind of team views data from UX testing as just one of many potential sources of valuable information.
When deciding which of these three approaches is right for you, it’s important to consider the individual dynamics of your team, as well as the circumstances of your particular project.
Data-Gathering Procedures: Best Practices
Now that you’ve planned how to use your data, it’s time to conduct tests to gather the hard numbers you need. Below, we’ll discuss the process of planning for a UX research experiment.
Creating a Hypothesis
In the previous section, we spoke about establishing goals for your project (what will your data eventually be used for?). Once you’ve done this, you can turn your attention to developing a hypothesis.
Creating a hypothesis for a UX experiment is much like creating one for a science experiment. Many of the same rules apply.
A useful hypothesis is a testable statement, which may include a prediction. A hypothesis should not be confused with a theory. Theories are general explanations based on a large amount of data.
They go on to give the following example of a “formalized hypothesis”:
“If skin cancer is related to ultraviolet light, then people with a high exposure to UV light will have a higher frequency of skin cancer.”
The statement has two parts: If X condition is met, then Y result will occur. It refers to a cause-and-effect relationship between two factors.
When creating a UX hypothesis, however, it’s necessary to go a little bit further. Beyond just discussing the cause and effect, we need to explain which users our hypothesis applies to, and why we think the result will occur.
An example of a formalized hypothesis for a UX research project can be seen below:
If the color red creates a sense of urgency in users, then making the “checkout” button on our website red will increase conversions among users browsing our product pages.
As you can see, the UX hypothesis is slightly longer than a scientific hypothesis. It contains all the information we need to test and answer the question.
Ensuring a Sufficient Sample Size
Once you’ve created a hypothesis, you’re ready to begin your experiment.
When conducting an experiment, there are certain best practices to keep in mind. One is that you need a sufficient sample size to ensure results are significant.
If your sample size is too small, the value of any data you gather will be questionable. For this reason, it’s important to ensure that your organization has a method of finding and incentivizing UX testers to participate in the experiment.
1. Eliminating Confounding Variables
You’ll want to eliminate confounding variables as much as possible when planning your experiment.
For instance, in the above “red button” example, it might not be a good idea to redesign your entire website, then test the old version of the website with the former button color and the new version with the red button.
In that case, users might be tempted to purchase because the new overall design makes the website look more trustworthy—the results could have nothing to do with button color.
2. UX Data Collection Techniques
There are many different UX techniques you can use to collect both qualitative and quantitative UX data.
3. Quantitative Data Collection A/B Testing
A/B testing is also known as split testing.
“… [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.
UX surveys are a key source of both quantitative and qualitative data in UX research.
A good survey should have well-designed questions—ensure that your questions are not leading and that the purpose of the question is clear. You 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.
If your product is a website or app, tools such as Google Analytics are a great source of quantitative data (click-through rate, bounce rate, etc.) to help you make decisions.
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 your website or app.
4. Qualitative Data Collection
In UX, a competitor analysis involves examining another company’s product to identify any comparative strengths, weaknesses, or areas for improvement.
It is important to read 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 the understanding that what works for others may not always work for you.
Interviews are a great way to gather qualitative data from users. Although time or budgetary constraints might limit the number of interview subjects, the insights gathered through a phone or in-person conversation will be more in depth than what you could get from a survey alone.
User Journey/User Flow
Creating a model, such a user journey or user flow, can be a helpful way to conceptualize how users are interacting with your 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 interviews.