In order for a product to be successful, it must be functional, easy to use, not frustrating to use, and ultimately evoke positive emotions. User awareness, and thus user expectations, are increasing, which is why tools that allow us to understand their needs seem indispensable in the design process. One of such tools - in my opinion, also one of the most important - is UX research.
Do I really need this at all?
Your e-commerce website generates a lot of traffic, but the conversion rate is low? Everyone says your online store looks great, yet you're wondering why it's not selling? Or maybe you've recently rebranded your online business and sales are skyrocketing? These and many other questions can be answered by UX research.
UX, or user experience, encompasses a range of experiences, emotions, and perceptions that users have when interacting with a product, such as your e-commerce website or application. User experience is actually everywhere, and although it's a relatively young concept, you could say it "existed" even before it was named, from a doorknob to a complex mobile application.
UX research is nothing else but evaluating the user interface in terms of functionality, design, clarity, intuitiveness, and visual aesthetics of a project. Each research study concludes with a report and recommendations indicating optimal solutions. It's worth noting from the outset that there is no one correct path or technique for conducting UX research. UX research uses multiple methods, and each of them can (and should!) be used for different research purposes and situations.
How to conduct effective UX research?
Before choosing a specific research method, it's worth answering a few questions.
How to choose the right research method?
The selection of a research method depends on various factors, such as the type of data we will be working with, whether we need inspiration or quantitative indicators. The context of use is also important - whether we want to observe how users behave, what they are looking for on our website, or if we assign them specific tasks. Finally, the research budget, the size of the research team, and the availability of respondents are also important considerations. What should always come first is defining the research objective. After all, we don't conduct research just for the sake of research. We need a goal, a question, or an assumption that we want to confirm or refute.
When to use quantitative research and when qualitative research?
Unfortunately, there is no straightforward answer to when to use quantitative research and when to use qualitative research. However, there are guidelines that can be followed. But what are the differences between the two methods and which one should be chosen? Qualitative research may seem like a natural choice, as we want our digital product, whether it's an e-commerce website or an application, to meet the high expectations of users. However, this perspective may be misleading. It's worth looking first at the characteristics of both research methods.
In quantitative research:
- a significantly larger group of participants is involved ("great - there will be a lot of data!"),
- typically closed-ended questions are used, such as questionnaires or diary studies ("phew - they won't take long"),
- such research allows for determining both the target population/user group and providing general answers to problematic issues ("wow - a wide range of applications!"),
- these studies are usually easier and cheaper to conduct, mainly due to the analysis of their results ("now I know what I choose!").
So, are quantitative research methods better? Not necessarily 😀 Or maybe yes 😀 Or rather, they're our favorites - it depends 😂
In qualitative research:
- it is usually conducted with a significantly smaller number of respondents; these are called in-depth studies ("is it reliable?")
- there is no closed set of questions here. The moderator or researcher encourages participants to delve deeper into their feelings or observations. Examples of such research methods include individual interviews or focus groups ("how do I know what to ask?")
- data analysis requires experience, skills, and time ("do I need a specialist = a bigger budget?")
- conducting such research is much more difficult, resource-intensive, and simply more expensive ("now I know what not to choose").
Of course, this is an exaggeration and a biased view. Yes, quantitative research methods are generally faster and cheaper, but in certain situations they may be ineffective or unnecessary. While quantitative research often identifies problems or areas where something is not working, it may not provide answers on how to fix it or what users expect. Quantitative research helps define the area for improvement, and sometimes, in simple situations, it can indicate which solution to choose. Qualitative research provides concrete solutions to problems that are difficult to define without involving target customers.
A/B tests on layout preferences for filters. Assuming we have only two possible placements for filters on the store (horizontally above products or in a left-hand column), it is easy to determine which solution users prefer using A/B tests. This can be done relatively quickly, unambiguously, and with a large group of users, which legitimizes the chosen solution more than just relying on "it seems to me" statements. However, when we are building filters and do not know based on what parameters users would like to browse products, we cannot determine this easily. We either rely on the philosophy of "it seems to me," or we use qualitative research to find a solution.
And what if I don't know which research method will be more effective for my problem?
In such cases, it is always worth consulting the issue with a specialist who will likely be able to indicate what and how to research in a given case with a high probability. In reality, the most effective approach is often a combination of both methods. For example, if we are not even able to define the problem, we can start with qualitative research to understand needs, then use quantitative research such as surveys to assess the scale of the problem, and finally, deepen the most important issues through individual interviews (qualitative research).
Example of using combined research methods:
Case: Your e-commerce site has high traffic, but low conversion rates. Using tools like Google Analytics, you know that a significant number of users drop off during the checkout process.
You decide to use quantitative research methods, as they are cheaper, faster, and you already "know where to look."
You prepare a form with closed-ended questions to ask users, such as:
What bothers you the most about completing a purchase?
- number of steps in the process,
- lack of visible summary,
- high delivery cost,
- long waiting time for delivery,
If the research indicates that the main problem is the delivery cost or waiting time, the solution would be relatively straightforward (though still ambiguous). However, if respondents indicate forms as the main problem, the situation becomes more complicated. New questions arise: Are there too many fields in the form? Are the labels understandable for users? Is the form validation appropriate? Further quantitative research would likely generate more questions before reaching the root cause. Therefore, at this stage, it would be better to use qualitative research, such as individual interviews, to pinpoint specific elements that frustrate users.
Do I REALLY need this?
And it's worth mentioning right away: no, it doesn't have to be expensive and time-consuming to yield tangible benefits. In fact, thanks to research - whether qualitative or quantitative - we can save a lot. We choose and implement only those solutions that have real application and tangible benefits for users. Even so-called hallway research (catching coworkers, friends, or acquaintances and asking them which solution seems better to them) is better than the philosophy of "it seems to me." We don't always have the resources (or budgets) for focused qualitative research with a group of specialists. Of course, the results of our "homemade" methods may not be as valuable as the results of research with a carefully selected group of respondents, but again - they will be much better than a one-sided "it seems to me that..." approach ;)