User research helps you understand your users through observation techniques and is a great source of useful insights, though, it does have its cons.
In this article, we will review how blending user research with quantitative data delivers more stable and valuable insights.
We are not going to do a deep dive into the user research methodologies, but we do want to highlight a couple we use pretty often in our practice.
Usability testing helps walk a user through important scenarios and give context to problems as well as frustrations that may be presented.
In-depth user interviews allow you to look closely at some of your users and get the sacral truth about their motivations and needs.
The problem is that we may start to believe in everything users share with us, and we turn a blind eye on the thing that 6 interviews can’t describe a whole audience.
This is like as if we would try to determine an average height of men living in New York by evaluating 6 residents. Dubious statistics, right?
Before conducting research you need to determine the subject. Data analysis is the best guide in the right direction.
Let's imagine you analyze the product funnel steps, and find that users who have three kids have the product details step completion rate as less than comparing to other user segments. This is a segment with a great average check as well as a long life cycle that you may want to retain and grow using your product or service. In this case, we would conduct usability testing with your existing users, to learn more about the friction points they may have. After we know the context of the issue, we can generate different ideas to fix this. Next, before implementing any of the ideas it’s worth to launch various experiments in this segment to see which ideas are going to work and which are going to fail.
Series of e-commerce websites usability testing revealed that most of the users feel confused during the checkout process because it starts with payment information and billing address. Based on this insight, a product manager decides to fix this issue and change the checkout steps order as soon as possible. Sounds reasonable, but looking at the data you will notice that out of 80% of the users landed to this step actually complete it, and the other 20% leave the step without committing any action. This is a vivid example of the fact that all reported problems should be supported by data, because you can end up trying to solve non existing problems.
It happens very often that design or product teams perceive any user feedback as a call to action. If users claim that pictures need to be bigger and it would be nice to add some new features. If you implement everything your users ask you, you will end up with a monster instead of a great product. Therefore, all UX and feature changes should be prioritized and validated with an A/B testing cycles, to make sure it resonates with your audience, not a tiny group of users.
There are many ways and tools to improve user experience, and it doesn’t make sense to build a cult around one or another. Combining different types of data is the key to a deep understanding of your users and bringing a real value to them by solving important problems.