Making it easy to learn about users and their needs

May 9, 2022

As our user research practice grows, more people across and outside of the Royal National Institue for Deaf People (RNID) ask about our findings. In this blog, I’ll share how we’ve made it easier for people to learn about users and their needs.

How it used to work

I’ve maintained a user research repository (basically, a really big spreadsheet) since I started at RNID. It’s where I store findings and includes things like, insights, observations and quotes.

Let’s say a content designer asked what we know about British Sign Language (BSL) translations. I’d search in the repository, find relevant information and share links to items in Airtable.

So, why weren’t other people looking in the repository?

I noticed that people were still coming to me instead of looking in the repository. I started to ask why, and found that:

There were a few exceptions. Those who found the repository helpful explained why and how they used it, which showed how we might make it easier for others to use too.

I search, ‘assistive technology’ or ‘hearing aids’ and I see if I can find something. Otherwise, I search by a type of user, for example, ‘deaf person’ or ‘hearing loss.'

Building a simpler frontend

After learning about the problems with our current setup, I recommended creating a frontend, or a simple interface, for the repository. The frontend would:

Our freelance developer, Rich, started building a proof of concept. He suggested creating a static site, using a static site generator, which connects to the Airtable API and generates a website.

How we structured the library

While Rich worked out Airtable’s API, we needed to learn how we should structure the library.

I planned a two-part study to learn:

This would help us understand how to structure the library so it makes sense for the people using it.

First, what do people search?

To learn how people search for research, I asked colleagues to read one interview transcript and one insight and, using sticky notes, write down which word(s) they would use to search for that snippet of data.

I found that people from the same discipline tend to use similar words to describe a topic, but this can differ across disciplines. I recommended updating tags to make them more descriptive or something that a user would say.

Next, how do people categorise different tags?

Using a sample of the tags we created, I ran a card sort where I asked colleagues to organise tags into categories.

The card sort showed that most people, across different disciplines, put tags into similar groups and use similar names to describe them. I used these findings to recommend which fields we use to describe each user need and insight in the library.

These exercises also helped us recommend a structure for the library overall. Alex, our Design Lead, mocked up how the library might work based on this.

He proposed we have a single main page where people can search for both insights and needs through a basic text search, as opposed to having separate pages and searches for each.

We shared recommendations with Rich who pulled it all together in a working prototype.

What's next

We’re launching a prototype of the library soon, which we’ll share to gather live feedback.

If we find that we’re solving the right problem then we’ll continue to increase awareness of the library and improve over time.


Thanks for reading. This was originally published on Medium.