We’re excited to introduce you to Trymata’s new research repository tool! Our latest offering will allow you to store, collate, and analyze datasets from all your usability tests, plus any other research methods that your team and organization are engaged in.
While a repository may sound like it’s just a place to store data, it’s actually so much more! By importing datasets from different studies and sources into a single location, you can compare and contrast diverse findings side by side to get much further-reaching insights.
Read the announcement: Trymata acquires & integrates research repository Considerly
In fact, we chose to acquire and integrate Considerly specifically because its analytical capabilities have so much potential. We’re confident that you’ll be able to get even more out of your research with Trymata by adding the Considerly research repository to your workflow.
In this article, we’ll provide a little more detail about Considerly’s features, with our team’s suggestions about how you can start using it to get more out of your usability testing data.
What is a research repository?
A research repository is a digital platform designed to store, manage, collate, analyze, and disseminate research outputs. It serves as a central repository for all kinds of data, whether textual, video, audio, or quantitative, generated during research activities. Research repositories are essential for researchers to contextualize their findings within the broader scope of their organization’s research efforts, as well as to share findings with other members of their team and organization.
One of the primary benefits of research repositories is that they provide a secure and accessible location for research materials. By storing these outputs in a repository, researchers can ensure that their data and findings are preserved and available for future use for themselves, their team, and their organization.
By providing others in the organization easy and long-term access to research outputs, a researcher can make their findings have a greater, longer-lasting impact. Without the ability to disseminate data easily, research outputs frequently have only short-lived relevance as they are applied to a single decision and then forgotten. In a repository, they can live on and continue to be accessed and applied.
Finally, research repositories can be used to identify the ways in which datasets from disparate sources and methods tell a bigger story together. Every kind of research comes with biases and blind spots; none gives you the whole picture by itself. When you combine different datasets together – especially across multiple teams – you can really start to fill in the blanks and put together a complete picture of how users experience and respond to your digital presence.
It becomes easier to identify sweeping patterns and overarching narratives, and craft a more holistic strategy for providing the best possible user experience.
Read more: Bridging the gap between data and insight
Using your Trymata research repository features
If you have a Trymata account already, then you now have a Considerly account too! For the next year, all Trymata users are being granted free access to the new research repository.
That means even if you don’t currently have a Trymata account, you can sign up for a free trial now and get Considerly included!
To start using your free Considerly account, all you have to do is click the new “Research repository” navigation tab from anywhere in your logged-in Trymata portal.
Our single sign-on integration with Considerly means that you don’t have to do any setup; your repository account will automatically exist for you to start using immediately.
Importing Trymata user testing data into Considerly
For now, we don’t yet have an integration ready to automatically import your data – but this is on the way! After we acquired Considerly, we wanted to make it available to our customers right away, instead of making you wait.
At the moment, you’ll only be able to manually input data – but starting from your data export sheets will make this process a lot easier.
In your Considerly account, you’ll find a sample test – “Sample Project: Greater Midland Community Center Test” – prepared for you. This project has been set up to demonstrate our vision for how usability test data will be formatted post-import.
Our team has entered real test results from one of our sample usability tests (this has also been added to your account on the Usability Testing dashboard) and analyzed it using a variety of Considerly’s powerful analytical features (including AI-driven and manual ones) to show you some of the things you can do. Explore the sample project to learn more about the insights that Considerly research repository will enable you to get!
Since we’re still working on automatic data importation, you can also help to shape the way that we build this! As you explore your research repository features, feel free to get in touch with our team with any ideas or input that you have.
Data format of the Greater Midland sample test
After evaluating several different models, we decided that the best way of organizing user test data in Considerly was to create columns for each major data type, with a separate entry (termed a “note” in Considerly’s interface) for each individual chunk of data.
When you initially click into any project, you’ll see an overview page. The main page where all of the data actually lives, though, is called “Notes” on the left-hand nav. Below, you can see the “Notes” view in the sample test, with each piece of data from the user test stored in a note in the relevant column (e.g., “Tester videos,” “UX Diagnostics & UX Crowd).
When you click into any note, you can see all of the data stored inside. You can import or copy and paste plain text, tables, images, and videos. At the top of the note, you’ll be able to access formatting options similar to any WYSIWYG editor – bold, italics, lists, blockquotes, even emojis.
In the notes with each tester video, for example, we’ve imported the mp4 video file at the top of each one. Below that, we’ve copy-and-pasted the video index (straight from the Trymata video player), annotations (from the test data export), and transcript (from the video player).
Analytical features: sentiment analysis, tagging, discovery graphs
Once your data is imported into Considerly, you can start analyzing. Considerly has a number of powerful features for digging into your data and figuring out what it really means. (To use these features, make sure the “Analysis mode” toggle in the top right corner of any note is turned on.)
The first one we recommend trying out is the automatic sentiment analysis. To use it, just highlight any amount of text, and then click “Analyze sentiment” in the tags panel that pops up on the right.
In just a few seconds, it will run an artificial intelligence based analysis to determine which parts of the text data express positive or negative opinions.
Each instance of positive and negative sentiment will be auto-tagged as such, so you can skim through and see which parts of your user feedback were associated with positive or negative feelings. The sentiment analysis feature is especially useful for large pieces of text like video transcripts.
Beyond the automatic sentiment tagging, you can also create your own tags and add them to any piece of data.
A great place to start with this is to recreate the tags you’ve used for your Trymata annotations, to tag anything related to issues you’ve already identified from your videos. (In the future, we’ll be able to recreate your Trymata tags in the research repository automatically as we increase the level of integration between Considerly and our usability testing suite).
In your sample test, you’ll see tags created for “navigation,” “content,” and “guide chart” – all of which started off as tags in the original video annotations on Trymata. After applying them to the imported annotations in the repository, we also applied them to other data points that were related to the same issues, in the transcripts, post-test surveys, UX Crowd results, and anywhere else.
We also recommend creating tags for whole tasks or major chunks of your user flow. For example, you can see in the video transcripts in the sample test notes that entire tasks have had a tag applied to identify what the tester was working on in that section. All of Task 3 for each tester has been tagged with “membership,” since that task revolved around finding membership info; similarly, Tasks 5 & 6 have both been tagged with “rentals,” the theme of those tasks.
By tagging big chunks of your data with the task topic, you’re able to get big-picture insights about those task topics. If you’ve run the sentiment analysis, added tags about specific issues you’ve found, and tagged your tasks, you can then see, for example, which step had the highest concentration of negative feedback, or where navigation issues tended to happen, or a variety of other insights regarding how everything overlaps.
Insights like this can be found in a snap via the “Discovery” tab on the left-hand navigation. Once you’ve added tags as desired, this tab will generate insight graphs showing things like the total frequency of all tags across the project, the volume of overlap between different tags, and how often tags came up in different notes.
Looking at the discovery graphs in the sample test, for example, we can see that there was about an even amount of positive and negative feedback in total for the test. The next most common tag was “navigation,” indicating that navigation-related issues may be something that should be prioritized soon.
If we wanted to explore that further, we could hover on the overlap graph to find that there were 19 instances where the “navigation” tag overlapped with the “negative” tag – and click on that connection to view all 19 instances, aggregated together (or all 14 instances where “navigation” overlapped with “ideas/suggestions”).
Then again, maybe we would notice from the overlap graph that the biggest overlap between any two tags was between “negative” and “rentals,” and follow that thread to see what the common threads were in that relationship.
Clicking into these relationships, by the way, will take you into the “Highlights” section of the nav, from which you can continue to filter your data contents in different ways. For example, if we wanted to see how “navigation,” “negative,” and “rentals” all intersected, we could add the 3rd tag in the search bar to get that much more specific. You can also filter by notes, so if you want to include or exclude specific chunks of your dataset, you can do so easily here.
Welcome to the next level of research insights!
We encourage you to poke around the sample test on your own to see how our team decided to input and tag different pieces of data, and the kinds of insights you can find as you interact with different graphs, filters, and search results. Even more so, we encourage you to try adding some of your own results to your research repository and see the kinds of extra insights you can get!
Keep in mind that the integration of the research repository into the Trymata platform is still in its early stages, and much remains to be automated – we’d love to hear your feedback and input about what you’d like the future of this feature to look like!