Young UX researchers: overcome your fear of numbers to stay relevant
This article was first published on Medium on Jun 7, 2021
A large share of UX researchers has worked in different areas of design before moving into research, entering the field without any education in statistics. As UX research becomes more mixed method, I want to encourage every young UX research professional to overcome their fear of numbers, and start investing in data analysis skills.
Photo by Mika Baumeister on Unsplash
UX research is both simple and very complex.
Over the years, UX research has become more and more professional, not only visible by the growing ResearchOps community, but also more accessible, as we see in a growing number of job postings and companies investing in UX research. With growing popularity in design thinking, more and more people become interested in the field, move into UX research from psychology, ethnographic studies, human factors, design or other fields.
The power of UX research lays both in its simplicity and also its complexity. While UX research is simple in the sense that the outcome of projects are mostly understandable and need no deep background knowledge to be understood (in comparison to many areas of science), UX research is also complex if you want to do it right. There is a lot you can do wrong, that will impact the outcome, accuracy, and reliability of your findings. The nuances and details matter so much that you need the professional to do it and it is not something anyone could do. As a professional UX researcher, you need the detailed skills to meet the highest standards of research execution, while speaking a language that everyone understands in order to have an impact on the product and business of your company.
Qualitative research is easier to master than data analysis.
Many UX researchers who previously worked in design move into the field with their old self in mind, building up the skills to answer their own questions from the past. And as designers, most of these questions were qualitative:
Why are people dropping off in the purchase flow after selecting a payment method?
Why do people watch different kinds of video formats on different screen sizes?
How do people consume content while commuting to work?
How do people feel when reading humor and sarcasm in website copy?
Qualitative UX research is very close to the here and now of design, helping make decisions, identifying potential problems in the solutions, understanding reasons for ignoring the intended path. Qualitative research sheds light on concrete problems and the answers it produces are relatable and easy to grasp — you can watch people fail navigating through a product, you can hear people talk why a product does not match their needs, you can build empathy for the person talking. You can view the world through their eyes, take the users’ perspective and reevaluate your own approach. With qualitative research, you will get an excellent understanding of why and how things happen.
Beyond the colorful images it creates, qualitative research is also easier to become good at, especially compared to quantitative research, including statistics and analytics and data visualization and all that scary and confusing stuff. Qualitative research is where your personality as a researcher can shine, your high level of empathy and positivity will contribute to the atmosphere you will create when interviewing a user, your love for details will allow you to identify the nuances when carefully listening and observing what your users say and do. Your creativity will lead to excellent reports, powerful presentations and smart ways of visualizing the main issues you observed. Becoming great at script writing, interviewing techniques like five whys and customer journey mapping is a matter of practice, active listening to feedback, further practice, critical self-reflection, further practice, working through best cases and general principles, and yes, even more practice. If you want to become a great qualitative UX researcher, you control your destiny. Work hard and you will become better over time.
In the business world, facts are based on numbers.
While in the field of qualitative research, it is the individuals’ effort that predicts long-term success, the same does not feel true for quantitative research. Many people describe a real fear of numbers, a complete lack of access to that alien language called statistics. Looking at data sets makes people cringe.
Numbers — some people’s nightmare
But the truth is, and I am telling you that as a UX research professional with 7+ years of experience in research: You will only have real impact on the top level of decision making if you are able to speak the language of numbers. If you want to impact business decisions in your company, you must (!) argue based on numbers. If you want to shape the long-term strategy of your company, you must know how to speak numbers. And most importantly: If you want to build a long-lasting career in UX research, sorry, but you must learn statistics. Otherwise, at some point others will take over. It is a simple fact that 50% of potential research questions require a quantitative methodology and you just won’t be able to handle them. You will always need others to support you, run the numbers and most frustratingly: They will have the power and last words. Numbers don’t lie. Who are you with your five participants? Are they even representative? We all went through that, so what you want is both: The power to handle numbers and tell a story with them, and getting colorful learnings from individual interviews. You want to become mixed method: Explain, to which degree your qualitative observations apply across a large sample of users.
Watching recent trends in design, you will find more UX/UI designer positions popping up. And more often you will read job descriptions that say front-end development a plus. While you could pretend that these companies don’t even know the difference between UX and UI, soon it will be normal to cover the full bandwidth of skills required for product design. The same trend started in TV production many years ago: While it was normal that a TV crew shooting footage contained a cameraman (or woman), a sound professional and an editor, these days you will find more all in one VJays, professionals who write the script, shoot the footage and make sure the video material has decent sound.
As UX research grows in profession, the expectations grow with it. We aren’t just a group of support roles for design, UX research is powerful enough to contribute to the definition of business plans, market expansion, product strategies, price definition, highest level management decisions. But that can only happen when we speak the language of business — which is a language based on numbers:
How many people recall seeing a targeted ad on the website?
What market differences do we find for the NPS?
Were people in condition A more or less frustrated than people in condition B?
Does the age of the users correlate with their intention to purchase?
Do those who perceive our brand as “modern” show higher willingness to recommend our service to their friends and family?
None of these questions can be answered with qualitative methodologies such as user interviews, usability testings, focus groups, etc. — they all require the collection of data sets. But all of these questions are relevant for top level executives. If you are able to answer these kinds of questions and tell a story around data, you will have an impact on the business and a seat at the table. If you are not, your work remains anecdotal.
Learn statistics, there is no way around it.
I speak to you being in the privileged position that I had to learn statistics and quantitative research methods for five years during my psychology studies. It never stopped, it was painful — but it was the most important skill I took away from university, and into my career as a UX Researcher. And yes, it might be easy for me to lecture people who did not walk through any scientific field in university, but that does not change what I said above: You need to argue with numbers when you want to impact the big decisions in your company.
If you need a bit of motivation, listen to me:
I can assure you that you won’t need 80% of statistics that are lectured in university. You only need a small but powerful analytics portfolio.
I can assure you that your learning curve will feel rewarding. Once you know how to use fork and knife, the opportunities are endless.
You will not just learn a new skill, working with numbers will literally change the way you look at your own profession, the industry you work in, and potentially the world you live in.
If you are smart, you will learn two things at once: Data analysis and programming. Don’t even start with Excel or SPSS, not even for the basics. Immediately start with R or Python. I promise you, you won’t regret it. This is what the industry is using. It is the future. Don’t invest in tools from the past.
From zero to this: Three days, maximum
Data Collection
During your journey, you will learn different concepts about how to properly ask questions in a questionnaire and how to make sense of them once you collected your data. To get started, you will only need to know a few things:
Your most important question types are single-answer questions, multiple-answer questions and and rating scales. These three question types will be enough to get started. You can find an introduction into different question types here.
The kind of data you collect impacts the analysis you can run with it. In short, if you don’t collect numbers, you can not do calculations (average, median). Find more information about Scales here.
You need to follow some principles in order to design good surveys. You can find more information about survey guidelines here.
If you want to try designing your own questionnaires, I recommend SurveyMonkey as your tool of preference. It is well designed, simple to use offers a list of templates that make your start easier.
Also, invest time into the formulations of your questions. I recommend this video as a start:
Data Analysis
Phase two after data collection is data analysis. Now you are sitting in front of your large excel sheet and don’t know what to do with it. In order to remove fear and anxiety, make sure you don’t feel overwhelmed by a large number of rows and columns in front of you. There is no reason for it. Numbers don’t hurt.
As a start, watch some basic tutorials about statistics. You will memorize some terms and principles. This phase of learning the basics is time-consuming, but there is no way around it. You must learn how to handle the data in front of you, and the best ways to learn that is through educational courses and hands on practice.
Express yourself, statistically speaking
Your ultimate goal is to translate a set of raw data into something meaningful, into a relatable story and concrete recommendation towards your stakeholders from design and management.
In order to do that, you will need to get started yourself. Statistics and data analytics are not theoretical topics, they are highly practical. I recommend you start your journey by using the programming language R. It will allow you to write analytics scrips, create beautiful visualizations and apply your code to other sets of data over time. Mastering R will save you more time that you must invest in the beginning, it is a lot more fun to use once you reach a certain flow and the community is huge, making sure you will find answers to all your questions online and tons of packages to solve your specific problems.
While you will find a lot of great videos on YouTube, I highly recommend investing in some practical tutorials that force you to work with data and solve problems in order to move forward. While I strongly recommend the Data Camp course on R, there are more courses you can look into:
I strongly believe you will only benefit from investing into statistics and data analytics. You will do mistakes, you will be frustrated, but that is normal to everything new. At some point, you will start connecting the dots and from there, you only grow.
Good luck!
