This is the first article in our series “Managing your UX Research Remotely”.
Before Corona, UX research at COBE meant interviewing people, preferably at their homes or at the place where they actually use the products we are interviewing them about. In any case it was all about interacting with them and talking to them directly, personally, face-to-face.
The same goes for the analysis and synthesis phases that follow the interviews: UX research at COBE was a lot of non-digital work and personal interaction among our team members with evaluating the insights in discussions, sticking post-its all over our office walls, and creating frameworks in the form of personas and customer journeys by setting up a blueprint on a poster on the wall or floor and rearranging post-its on it.
Basically, we felt like we had established a great process that allowed us to conduct UX research in a very effective and efficient way.
Then COVID came along — and suddenly, we could no longer work together in the office or go to people’s homes to interview them. Conducting interviews, analyzing data and making sense of it in a collaborative process, as well as conducting user tests with prototypes, became a true challenge. So, we concluded, we had to reinvent our way of working, and find a new setup that serves our purpose and matches the new restrictions COVID imposes on us.
Along our journey, we discovered many great tools and learned a lot about them, and so we decided to share our insights in this series, which consists of three articles in which we describe how we transformed the steps of our research process into a fully remotely and digital setup*.
Since this was one of our major issues, we would like to start this series with how to organize research material and analyze research data in a fully digital setup.
Fortunately, even before Corona, we thought about becoming more digital with our research process, because we wanted to create a research library or repository, in which we can store and access all our research insights. Since we conduct several research studies per year, all the studies and collected insights accumulate over time and it has become more and more crucial for us to have an overview of all studies and results we worked with in the past. This would allow us to quickly screen through previously conducted studies and thus enable us to build upon the knowledge we’ve already collected.
For this purpose, we tested several software tools and ended up with Dovetail (https://dovetailapp.com/). Dovetail is a cloud-based user research and customer feedback software for the organization, collaboration, analysis, andstorage of research data and insights. In Dovetail, research studies can be arranged according to projects, and all included material such as notes, transcripts, images, videos, audio recordings etc. can be stored here as well, so there is one go-to place for everything.
For the analysis, the process we usually followed was going through each transcript individually, writing each single information or insight on a separate post-it and then starting to map the post-its on the wall in order to identify categories and patterns. Here, Dovetail turned out to be a great support: It lets you tag information and insights with different labels that can be created during the tagging process and which can be freely named. By simply selecting a piece of text and highlighting it, a tag can be added to that piece of text. By this, our process of selecting important information and clustering insights was transferred into the digital world in a very efficient and insightful way: Tagging single pieces of information will bring these pieces of information together, so clusters are created automatically. This allows us to uncover patterns across the research data.
In addition, Dovetail also offers some quite nice features that, for example, let you visualize information in the form of graphs or to filter for tagged information. You can also create highlights of the tagged information (Highlights are groups of insights to which you can add pictures, text etc. and that describe what your finding is about).
By this, you can create a research library, in which you have all your research highlights and findings in one place, and which you can search if you need specific information within previous study results.
Another great feature is that dovetail allows collaboration through comments and mentions, which makes working together on a project easy and efficient.
Up to now, we have worked with dovetail for over a year in several projects and different kinds of studies, and we are quite satisfied with the features and opportunities it provides us with. However, we still miss some features or see room for improvement.
For example, Dovetail offers to automatically transcribe uploaded video and audio data. So far, this has not really worked that well for us, because even though both languages we conduct studies in (German & English) are supported, the results are not too good. Especially in German, which is the language of most of our research studies, the results of the automated transcripts are quite disappointing. In general, the transcripts in both languages still require a lot of proofreading, and the effort for doing this is almost as high as having an additional person creating the transcript themselves.
Also, for the visualizations of the results in the form of charts we missed some features and further options.
Most of all, we found ourselves wishing that working with Dovetail would be a bit more entertaining, because — no matter how effective and efficient the tool is — it can sometimes be a bit dull to use, since the interface is super simple. However, we can absolutely live with that as a trade-off for everything Dovetail has to offer, because, in general, it is a very powerful tool that supports us quite well in reaching our aim to have a research repository and one go-to place for our insights.
So since Dovetail turned out to be the right choice for us when it comes to the organization, management and analysis of research material, it was time for us to take a look at the next step in our research process. How could we transform our usual workflow of creating frameworks such as personas from our insights into a fully digital process? This is what our next article in this series will be all about.