How we’re using continuous discovery to become an insights driven squad
Our squad gets together every week (yes, including engineers!) to talk with a ClearScore user
Why should your whole squad speak to customers on a regular basis? In this article, you’ll see how this customer research methodology empowers our team to have a user first mindset. We’ll explore how we speak to ClearScore users each week, what the benefits are, and how we measure success.
What exactly is continuous discovery?
The framework we are using was created by Teresa Torres, a product discovery coach. She has written a whole book on the end to end methodology and how to implement the process in your team.
A working definition of continuous discovery:
Regular touchpoints with customers
By the team building the product
Where they conduct small research activities
In pursuit of a desired outcome
Let’s break that down and explain how it works for our squad:
Regular touchpoints with customers
Every week we schedule a remote interview with a ClearScore user. They are invited to tell us about their relationship with our product and we get to hear first hand their needs and pain points surrounding their experience.
By the team building the product
It’s really important for us that the whole squad is given the opportunity to hear first hand what our users have to say. Each week, the interview is attended by product designers, content designers, product managers, engineers and test engineers from front end, back end and native applications.
We try to follow a mantra used by Government Digital Service: research is a team sport. Every member of the squad has the voice of the user guiding them, not just relying on one or two members of the team to educate the squad. Not only is the whole squad listening but they also input into the documentation and agreed needs and pain points that we have excavated from the interview.
Where they conduct small research activities
Each interview is 1 hour each week, which is a small commitment in our busy calendars. We can use that time however we like — to get feedback on the live product, test prototypes or simply hear a story about their experience of applying for credit products. Over time we have adapted our interview guide to focus on the specific areas of interest we think will give us the most value.
In pursuit of a desired outcome
As with any good product framework, continuous discovery is anchored by a specific objective and measurable outcome. As our squad is part of the Marketplace function our outcomes are commercially driven but always with a user-centred focus. It is up to us to figure out what is causing friction within the conversion funnel and to help users select the right products for them.
So what are the benefits of this framework compared to traditional user research methods?
In a traditional user research framework a bulk of research might be done up front to guide the project for weeks, if not months. That could mean it is not always possible to revisit or validate insights that have been identified or speak further with users as the work develops.
With continuous research we can adapt and change our questions over time as we learn more about our user’s behaviours. It also means we are always less than a week away from speaking to a user if we need some quick feedback.
What do we actually do every week?
Our sessions are remote or hybrid so we all jump on a zoom call and the facilitator leads the session. We use a trusty interview guide to excavate the specific insights that we want to gather in a specific week.
Our interview is usually split into two sections:
- Credit product story
- App walkthrough
The credit product story starts with a simple question: Please tell us about the last time you applied for a credit product, such as a loan or credit card. Then we go deep into the details. We want to know the full end to end experience of what the user went through. Sometimes they talk about using ClearScore, sometimes they don’t talk about our app at all.
This gives us full context of the journey before a ClearScore user even picks up a device. What lead them to needing that loan? What was the reason they started thinking about it? Where were they when they started applying? What time of day was it?
This question also centres the person in the specific story. By relating to a lived experience it means people are less likely to generalise about their behaviour or even worse speculate about other people’s behaviour — we’re not interested in that.
We find there is a lot more truth and rich insights in asking about things that users have already done rather than things they say they do.
The app walkthrough is where we ask a user to open their ClearScore app. Based on the story they have just told us, we ask them to show us how they would complete this task on the ClearScore app. Here we get to know about their mindset as they go through different stages of the app flow. We get feedback on usability, content, and comprehension. There is a lot of rich insight from simply getting a user to talk through their experience of looking for a card or loan on ClearScore.
Here’s a snapshot of our ClearScore user interviews:
https://www.youtube.com/watch?v=eNIxPgTqO3s
After the session the squad has a debrief and agrees on the specific needs, pain points and opportunities that have been identified as part of the interview. This is then added to our opportunity solution tree.
The opportunity solution tree is a framework, created by Teresa Torres, which allows us to visualise a problem space quickly and easily. At the top of the tree is the specific outcome that we are trying to achieve. Under that we group different opportunities that we have heard from users into common themes and child opportunities
When it comes to testing solutions this tree framework allows us to see the full opportunity space of the product we are working on. It’s also a useful artefact for onboarding people to the team so they can understand all the discovery we have done.
We also create artefacts that allow other teams to see the insights we have gathered. We create a research snapshot of every interview which is a visual overview of the customer we spoke to and the main things we learned.
Interested in implementing continuous discovery on your team? Here are three ways we keep our practice successful:
Distributed responsibility
Operationalised logistics is important so that there are no bottlenecks in the process relying on one person. That means the team takes it in turns to facilitate, note take and set up the interviews themselves. We utilise tools Notion, Miro and usertesting.com to streamline recruitment and documentation.
Attendance accountability
As mentioned, we believe research is a team sport and our goal this year is to become an insights driven squad. That means keeping everyone accountable for their own research exposure hours. In order to do, this we have identified user research attendance as one of our product OKRs which is tracked the same as any of our other product specific metrics. We have a goal of 80% of the squad attending at least 3 interviews in the first quarter of the year.
Measuring success
We’re not just doing research for the sake of research. We make sure the work we are doing always ladders back up to the product objectives we are trying to solve for. We do this by balancing the needs of our users with the goals of our product and identifying the most valuable opportunities from there.
The biggest indicator of success from this process has been hearing an engineer say ‘remember that user who said X? well how about we try Y’
This was when we knew the squad was becoming fully engaged and starting to look at problems from the lens of the customer.
It’s been great to see not only are we achieving our research participation goals but also seeing squad members coming back for even more than that. This demonstrates they are not only attending because they have to but because they have a genuine interest in our user research.
Recommended Reading: Continuous Discovery Habits by Teresa Torres
Thanks to Denzil for assisting with running the research sessions in the video, and Dom for editing. Participants in the research shown were actors and the screens contained dummy data for privacy reasons.
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