There are so many ways to synthesize research findings. Let's just say some of these methods are better than others.
One method I have always used are user research summaries. This approach can be controversial because colleagues may not read reports. However, I believe it's a critical way to share out insights for several different reasons:
- They're a nice snapshot of the essential information
- You can start to thread patterns/themes from a variety of sessions to see the most impactful information
- They allow teams to efficiently digest the sessions through words and media
- You can include actionable next steps and recommendations based on insights
- They are customizable for your audience
- You can digitize them with tags for improved "findability"
I started writing these research summaries years ago, and have continued with them at every company where I have worked or freelanced. They have provided an easy way for me to communicate findings quickly, and allow for impactful changes within the team mindset and the product.
How to craft a user research summary
Each research summary comes from a generative research session or a usability test.
Through the years, the summaries have evolved, and I have adapted them to different teams. For some organizations, they come in the form of a Google Doc (the example below), Google Slides, or a Pages document with more visuals.
Whenever I am summarizing a usability test in this format, I always include screenshots with annotations.
I go through the following steps before I begin writing a research summary:
- Step 1: Listen to the research interview again
- Step 2: Write a transcription of notes (I use Excel) of the entire conversation while listening
- Step 3: Tag each (relevant) data point with a need, pain point, motivation, or goal
- Step 4: Do a mini-affinity diagram with the session, where you group all the needs, pain points, motivations, and goals
- Step 5: Note the most mentioned or repeated information (within the session and across previous sessions)
- Step 6: Pull the most relevant quotes from the session, which are the ones that help give teams context to the participants
- Step 7: Consider the next steps or recommendations based on the insight
Typically, this entire process will take me two to three hours per one-hour interview. It requires a lot of effort, but it saves you time when you are preparing for a more extensive synthesis after many research sessions.
These screenshots and summaries also allow teams to take place in synthesis sessions more easily. They can scan through these documents and bring ideas to meetings without having to watch every interview.
Once I go through the above process, I make a note of what I want to include in the summary.
My must-haves checklist of a user research summary
- A small description of the person you spoke to (ex: what segment or persona that person belongs to, and the percentage of customers they represent)
- What teams/squads/tribes would get the most from this summary
- A brief background of the project to give context to the goals were
- Notable (and verbatim) quotes that represent the user and help teams make decisions
- Insights and themes within the session, and across any previous sessions
- Recommendations based on the insights
- Video on audio clips that allow people to see/hear the quote or insight. When dealing with usability testing, screenshots with annotations and video clips are essential
- Links to any additional documentation the team would find helpful to dig deeper
As I mentioned, how the user research summaries are structured depends on my audience. What I love about research summaries is the fact that they are highly customizable. You can use these as a way to summarize one particular interview, a series of interviews, or even a monthly summary of the research done for one specific team.
Two research summary examples
Since it may be difficult to imagine what I am describing above, I'll
give two samples of how I structure and construct my research summaries.
Both of these summaries contain completely falsified data, but reveal
the type of information and structure I use.
The first example is from a generative research session.
For this project, we were looking to understand and report on user's
mental models on travel. In this particular research summary, I give an
overall impression of the interview and draw parallels from other
sessions. I don't provide recommendations because I want the team to
digest the information without my bias.
I will send these out before a team synthesis session so colleagues can understand the themes and highlights but come to their conclusions on the next steps.
- Date: 10/04/2022
- Desktop/App: Desktop
- Persona: Moving Mary
- Place: Paris
- Employment status: Student
- Relevant squads mentioned: Booking, Ticketing, and Innovation
With generative research, we are looking to create a general
understanding of how our users think about travel, and how they are
interacting, at a high-level, with the TravelBuddy website or app.
"I am excited about sustainability, so it would be nice also to
include suggestions like, what is the most sustainable way with the
least emissions. There are some websites, which let you calculate the
impact you have on the environment in terms of CO2 emissions, but that
function would be nice. That way, you can negate the environmental
impact (ex: Atmosfair)."
"I was looking for a trip from Paris to Amsterdam to make a
reservation in a special train compartment because I was traveling with a
guitar. I know there is a special baby compartment on the trains, but I
could not book this on TravelBuddy, so I had to go to the carrier
website directly to book. So, sometimes I think, why should I book
tickets with you?"
"If I already searched for a particular trip and didn't finish
booking yet, it would say something like 'trips in-progress.' If you
haven't finished booking your trip, you should be able to continue. Or,
meanwhile, we found you these other trips we can recommend to you, like
the cheapest way. It would be like a travel companion." See video clip
Themes and highlights
1. As we've heard before, the number of changeovers are critical in the decision-making process
- For this participant, the number of changeovers was paramount, as she was traveling with a guitar and luggage
2. She has been having the same struggles as other participants when
trying to edit a trip as everything resets, and she has to start from
- "Now I need to try this again and start all over again. I have
to organize the search and all the filters again. It is annoying." See
video clip (link)
- "I tried to go back and change a trip to
one-way, and there is no way to edit it, which is frustrating. I can't
change anything and, if I want to, I have to start from scratch."
3. She is another user who did not know there was an account area,
and she recommended we include a travel profile. The travel profile
would consist of relevant information, but also stats on CO2 emissions.
See video clip (link)
- "I would like to see a little more like a log-in function, so
you could have a travel profile where I could see my recent trips. If I
always go somewhere, based on that, you can make recommendations. It
would have easy access to similar trips I've booked before, so then I
don't have to re-enter all the stuff."
- "If you see I often
travel with a certain carrier, you could give me special deals for
certain routes. You do email me about some offers, but it would be nice
if they were more targeted for me."
Other tools she uses
- Booking.com for hotels
- Google Flights
Links to resources
- Research notes
- Research session recording
- Participant folder
The second research summary I use is much more tangible and consists
of direct recommendations or next steps. I do this after a usability
test, or when I am synthesizing for a team that needs the next steps.
Generally, in this scenario, we might not have time for a group
synthesis session, or this could be a summary of the group synthesis
Insight 1 – P1, P4, P5, P6, P8
We do not provide sufficient engagement analytics for our clients, inhibiting them from making data-driven decisions.
- Visual content analysis metrics (what is in the picture/what performs best)
- Content recommendations based on analysis – Industry/vertical benchmarks
- Tie revenue back to highest-engaging photos
"If we knew the most popular photos contained dogs, we could
cherry-pick the content that we know would increase engagement and
Insight 2 – P2, P3, P5, P8
Many clients are asking for manual reports from account managers, as our platform isn't providing sufficient metrics/data.
- Allow users to compare metrics month-over-month or year-or-year
- Hashtag metrics would allow users to adjust hashtag strategy independently
- Pull out/recommend content similar to top-performing photos
- Make note of which photos have been used and in which social channels
"I need metrics that will help with two things: proving that the
platform is worth the price and allowing us to strategize without having
to run to our account manager."
These are not the only two ways to provide research summaries to
teams. However, these have been incredibly useful in providing an easily
digestible snapshot of findings and for helping the teams synthesize
more massive quantities of information into smaller chunks.
Doing so increases the likelihood that your research will be taken seriously and set the ground for action in the future.
Nikki Anderson-Stanier is the founder of User Research Academy and a qualitative researcher with 9 years in the field. She loves solving human problems and petting all the dogs.
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