10 versions of UX Research Documentation
Pros, cons, and favourites.
In the past 10 years, I have also probably tried 10 different forms of research documentation and dissemination. Here's a rundown of what worked and didn't.
In order of my favourite ones to revert to:
1. The product-planning debrief
Made for small product-focused working teams, that values action and nuance over formal reporting.
In other words, it needs a research-mature team: that understands how to connect observed behavior, to needs, to priorities, to feasibility - and when to think of when.
Bad for future-proofing.
What I'd do better: Detailed minutes.
Notes + Miro/ Post-its + Wireframing -> only understandable to the people present
I can also call it: Top 3 takeaways
Since, it's most often a debrief that I run after small usability tests (5-10 users; 5-10 tasks each) - where the team gets together and contributes/ writes down their observations.
2. The customer journey mapping session
What I use most often as a failsafe for discovery research, particularly related to (digital) service improvement (which has been my work for the past decade).
It hits the key information you need -> which touchpoints when, what info is important, what decisions are being made, what sub-actions are being done.
It's the fastest route to knowing what needs to be fixed.
Not fit for non-discovery phases. So, if the team's in evaluative method mode, then don't do this.
Also, this takes a bit of research (i.e. time).
3a and 3b. The full-blown usability results "table"
I have at least 2 forms of this table (depending on my teammates'/audience's need for detail).
Primarily, it's a summary of the key test differentiators.
A. One version I have is a high-level summary - that shows either aggregated task success rate, or average completion time for - various tasks, screen sizes, competing products tested, demographics or technographics - depending on the goal of the test.
B. This is more straightforward - just a summary table of the same (task success, completion time or pain points) per participant (with profile/description).
Obviously, I mainly use these for usability testing. Summaries this direct don't apply to discovery research (unless forced to. Which I also experienced once in my life).
4. Monthly User-data-from-different-channels summary (triangulation)
This one I love, but keep forgetting - only routinely did once/at one job. I attempted to implement at another team, but failed at doing it collaboratively/scaling it so that others could apply.
I summarize key learnings from: Product analytics, Internal company information/imperative, Ad hoc/Qualitative research and social media/customer service feedback.
This is connected to product plans/feature changes, and sent out to the team. I love what this represents - connecting multiple streams of customer findings to org plans.
Con - is already quite processed, so has hidden pre-work/data clean-up, that isn't visible or apparent in the final output.
Monthly is actually not "frequent" enough for most cases, but this was the sustainable pace, for me (we were in a very lean team).
I think if I was able to systematize this, it would be awesome.
5. Key Drivers analysis
I realize that my brain defaults to this for qual - and for good reason. I think it's still a remnant from my market research days - where the foundational piece of information was choice drivers.
What this has: Hygiene factors VS Differentiators
I love making this analysis, because the analysis steps are so fun. The final outcome is rewarding, too.
Obviously, only applies to discovery research - anything whose goal is determining preference "why would someone buy/choose/use".
Cons - I think there needs to be "thick enough" data to make this analysis - and you can only do this using data from in-depth interviews/actual detailed conversations on why people do what they do. It can't be inferred.
6. Pure artifacts and images + stories = Story-sharing
This is actually my favorite - if I could do it all the time.
It takes a team that is open-minded, or at the very least aware of the value of qualitative information.
i.e. This does not work for teams and individuals hard-wired to value numbers.
Also in other words, you really can't use it all the time - unless you are/have a good evangelist.
Back to the pros. This is really it, for me. The epitome of the value of observational, behavioral design research. Seeing "the how" and the nuance.
Great for "innovation" - finding the unspoken, intuitive considerations people have when they do something. It isn't something you can always explain.
The other "information" you get from it is "three-dimensional awareness, or contextual awareness - it's a different type of imagination boost. Which is really the spark you need for solving problems creatively or sharply.
7. The full report
I don't particularly enjoy this, but every long-term job of mine has had this at some point.
Ingredients: background and methodology, rundown of answers to all the goals of the study: can be behavioral archetypes, drivers, a journey and conclusion.
Essentially, it's a long document describing who the userbase is (based on the participants), motivations, options and decision factors, lifestyle, service or user experience and recommendations or implications.
Pro: comprehensiveness
Con:
- I have never made one in faster than 10 working days - due to the data processing and clean-up work, analysis and actual writing and report-crafting (models, etc.) phase.
- Time + makes research seem like a static artifact.
8. The database: "Atomic" research repository
You know, I want this to work. I really do. I know this is supposed to be THE way.
9. Key quotes per theme/ finding
10. The "navigation bar" summary with participant photos
Additional entry: The one-pager format: Created by my design research team member Vikki.
Was a handy summary (most would probably say it's an executive summary). [I do not have an excerpt of this, as this was my friend's output.]
Bonus round: Least favorite format ⬇️