The face of UX is changing. Just a few years ago product decisions were often based on a designer’s wants and intuition. Today, they’re more often based on feedback and information provided by users. In a world where analytics rules, design is becoming data-driven. Data forms the cornerstone of our product development process; it can quickly inform development priorities for enhanced user experience, improved user satisfaction and increased adoption rates.
In order to keep up with the times, your designs need to be based on data. In this article, you’ll learn how to include data as a core component of your design process.
Why do designers need data?
Steve Jobs famously said, “It’s really hard to design products by focus groups. A lot of times, people don’t know what they want until you show it to them.” So why not follow his advice, hire a top designer team and let them make all the decisions?
There are a couple problems with this approach. First, Steve Jobs wasn’t a designer, he was an inventor. He was strongly focused on creating new product categories and revolutionary technologies. While his approach might be good for inventors, it’s hardly applicable to the regular product designers because when you create a new product in an existing product category, your users will have a great sense of what they want and need.
Second, even if you’ve hired the best designers in the world, they can’t predict what your users want simply because designers aren’t users(except of course if they’re designing a product for designers, but that’s a rare exception).In our industry, the gap in knowledge between a designer and user is huge, and it’s simply wrong to think that designers understand exactly what users want and need without any proper user research, or without any real testing or data.
Thus, designers cannot make decisions based simply on what they think. Designers have to engage users to gain insights so they can effectively tailor user experiences.To build user-focused experiences, designers need to use a data-driven approach.
Defining Data-Driven Design
Data-driven design sounds like a great buzzword, but what does it really mean? Basically, data-driven design lets data drive many of the decisions made about design. The goal of data-driven design is to develop a better understanding of everyday experience.
Two Types of Data: Quantitative and Qualitative
Before getting straight into a data-driven design, we have to make sure we understand what counts as data. There are two types of data:
- Quantitative data: Data that tells you WHAT is happening (or not happening). Usually, it’s a numerical data.
- Qualitative data: Data that tells you WHY this is happening. Qualitative insights aren’t numeric.
Most analytics tools provide a lot of quantitative data.
For example, in the context of a website, Google Analytics can tell you how many visitors has come to your website, how they got there and what actions they took. What this data doesn’t tell you is why. Why does a certain group of users take one action, while a different group prefer another? Why is one piece of content more interesting for your users than another? The ‘why’ aspect is of the highest importance because it opens the mystery of user logic.That’s when we turn to qualitative data.
Is Qualitative Data Better Than Quantitative Data?
Absolutely not! In fact, you should never trust just one type of data. Use both together to really understand your product’s usage patterns. Armed with quantitative and qualitative data, you can make a more informed decision; quantitative data tells you a current status, qualitative data gives perspective. For example, you can look at quantitative usage data to see how your product is actually being used. For features that aren’t being used as much as you expect, you need to dig deeper with qualitative feedback to understand the problem. Data will help you in formulating a hypothesis about a problem and how to solve it.
3 Essential Elements of Data-Driven Design
To use data to inform design in a meaningful way, we need to connect the dots between data and design improvements to use data to identify your specific users’ desires, problems and needs.
A data-driven design strategy is based on 3 things:
Data on its own is fairly useless. In order to create meaningful product experiences you need to analyse data to turn raw data into meaningful information and insights designers can use.
As noted in the article 6 myths of data driven design, if you are using data to inform design decisions, you have three ways to look at things:
- Proving: validate design decisions by A/B testing and using analytics.
- Improving: implement and measure the impact of your change. This often means the data-informed design iteration—tracking changes across time and optimizing the product using data.
- Discovering: don’t’ just analyze, synthesize (use data to explore new patterns and dig deeper on problems, rather than just to prove who’s right or wrong).
Also it’s very important to be able to quickly identify and connect the most important data for use in analytics. The following guidelines should help:
- Know what data you want. Without knowing what you are trying to measure and what the current baseline to measure against is, you are essentially designing blind. Figure out what you want to learn and what data you need to learn in.
- Extend the data insights. Data from multiple sources creates a more nuanced picture and, in the end, an actionable outcome. Use all available data-points to conceive of ways to improve user experience: analytics, A/B tests, contextual research, customer service logs, data from social media, surveys, interviews, usability tests, support tickets, and other sources.
- Avoid being overwhelmed by data. Many teams become conservative over time as they rely more and more on data to make decisions. They ultimately become paralyzed and unable to build something really new (think of Google’s 41 shades of blue testing). Don’t be one of them.
- Avoid confirmation bias.Confirmation bias is the tendency to search for, interpret, favor, and recall information in a way that confirms one’s beliefs or hypotheses. Poor decisions due to the biases have been found in many organizational contexts.
- Include numbers and context. Don’t offer “high-level” metrics, find data points that answer specific design questions. Remember, you need the numbers and the context to tell the real story. So give the meaning, then details.
It’s no surprise that the ability to empathize — to step outside of yourself and see the world as other people do — is a core to designing a meaningful product.
It’s important to understand that a data is generated by and about people, not machines. Data should represent the traces of human behaviour. Thus, seek human stories to give meaning to data:
- Find data to understand what people say, do, think and feel. Figure out what important for them.
- Get emotional reasons for why people do or don’t things. Understand your users’ motivations and pain points.
When we understand the user’s perspective, we make products that are better suited to our users. One good example of using empathy in the design process is Tesla. Tesla has done things to package up its technologies in a way that is both new and interesting, but also familiar at the same time. The design intent behind the Model S is clear — create a “strangely familiar” product. If customers want to drive an adoption of electric cars they need them look like a traditional car. This makes the product feel more friendly.
Empathy can be built up by watching and interviewing your users, analyzing surveys and using more traditional research methods, such as diary studies and usability testing. Your ultimate goal should be set on creating a product people love.
When we think about data in terms of design and innovation we should think of data as something that helps guide our decisions of what to do next, but at the same time, we shouldn’t let data decide for us. There are a couple issues with following a purely data-driven approach. First, metrics are limited because they’re based on what you’ve already built (all available data is based on your current audience and how your current product behaves). Taking this into account, you understand that you only have a small subset of the information that you need to build a successful product. Second, no amount of data or empathy will replace the fact that a designer needs to make decisions on how to interpret data. This means that you as a designer need to be a curator of what is meaningful and what’s not. You need to have a vision for what you are trying to do. Vision is achievable. It’s built up over time through experience: by making decisions, by making mistakes and by learning along the way. Use data to validate and help you navigate your vision.
Finding Balance is Key to Good Design
The danger in any product design environment is when designers rely on one part of a strategy too heavily and end up optimizing for the wrong thing. As in all things in the real world, there needs to be a balance. Good product design comes from finding the right balance between data, empathy, and vision.
The world is changing. Just a few years ago it would be difficult to imagine that we’d know the impact that an experience would have on the market before we shipped it. Today, this is a reality, and many product-based companies are building experiences this way. As designers, we have a mass of opportunities in front of us to fundamentally rethink how we work with data, and how we drive meaning and insights from it. We should use these opportunities to make better design decisions, ultimately creating better products for our users.