Ben Holliday
Mar 10 · 5 min read

Back in 2015 I wrote ‘Everything is hypothesis-driven design’. It remains one of my most (and still frequently) read blog posts.

Something I’ve been thinking about more recently is how this translates to user research and ‘discovery’.

A discovery should be focussed on research and learning. Helping a team or organisation challenge their existing ideas, knowledge and understanding of a problem space. I believe that research hypotheses, based on an understanding of assumptions, are a great way to frame and organise this work.

Starting with testable assumptions

Hypotheses can be thought of as testable assumptions.

An assumption is either something that we believe is true, or can be something that we’re expecting to happen. It will be based on what we think we know, as well as our personal experiences or individual viewpoints.

Assumptions will often turn out to be wrong, or at least partly wrong. This is okay as long as we’re clear about the assumptions we’re making.

In a discovery it’s especially important to have a shared understanding of the assumptions that you’re working with.

A discovery might start with a problem statement or an existing product or service. It might also start with a set of ideas, future vision, or a proposition intended to introduce a combination of new business and service models. All these areas of focus will mean that there are important assumptions to test.

A format for capturing assumptions at this point would be a combination of something like:

We think that [this] is true OR We think that [this] will happen

Which is why we think [this] is happening OR Which we think creates [this] opportunity

If we’re then wanting to move from a list of assumptions to thinking about these statements as testable ‘hypotheses’, the key thing missing here is a unit of measurement.

The unit of measurement is user research

As this is about research and learning (discovery), the measure is simply what we want to learn from user research. Each assumption can become testable through a qualitative research plan.

How much research focus is required for each assumption depends on the level of certainty you need. This might be the certainty needed in order to start reframing a problem, or simply having enough confidence in a set of research insights and the design opportunities they start to create.

Working as part of a team, you will always need enough certainty to be in a position where you are confident enough to take the next steps to move forward.

Building on the previous format for capturing assumptions, we can easily add user research as a unit of measurement:

We think that [this] is true OR We think that [this] will happen

Which is why we think [this] is happening OR Which we think creates [this] opportunity

We can learn more about this assumption by speaking to [these] users in [these] scenarios

While I’ve focussed this example on qualitative research we could also gather quantitive data to learn more about each assumption. Quantitive data can help us to understand the size and scale of the impact and relative importance for each of a set of assumptions. It’s important to remember that some assumptions will always be more significant than others. Understanding their relative importance should be an additional focus of discovery work.

To summarise:

We are using research to reduce risk, or to increase the certainty we have in the assumptions we are making, as well as understanding their relative importance.

Sometimes, research will show that there is no viability or service needed compared to an initial set of assumptions made from within a business, team or organisation. But, more often than not, research will help reframe a problem into a different set of opportunities–often dependent on testing and learning what does and doesn’t work using a design (prototyping) process.

Using assumptions as the basis of a research plan

A good approach for planning discovery research is to start with your assumptions. From here, you can plan for who you need to talk to, and focus time and effort in the right places–turning each assumption into a research hypothesis.

Make sure you capture any assumptions about where you believe you will find the people that you need to talk to. And also list the needs you believe these people have in relation to the problem or type of product/service/policy area that you’re working in.

A useful way to focus and make a start is by listing the following:

  • These are the assumptions we have/are the most important assumptions we believe we are making.
  • These are key questions we think we need to answer to learn about these assumptions.
  • These are the people we think we need to talk to about these assumptions, and where we think we can find them.
  • These are the needs that we believe these people have in relation to our product/service/policy area.

Once you’ve listed all of these, it should be possible to create a full set of research hypotheses that you can use as an anchor for your work.

An anchor for learning

Discovery in my mind is less about about a fixed phase of research, and more about a mindset and approach to continuous learning.

The way I’ve framed this post works on the basis that discovery is always about starting with what we think we know already, or working from a shared set of assumptions and/or experiences.

When focused on discovery, I don’t think we should set aside all prior experience and knowledge. This includes our own knowledge, as well as knowledge built from a detailed understanding, and the direct experiences of people and specialist domains within the organisations we’re working with.

It’s more important to treat all previous experience and knowledge as a starting point. This means being aware that, taken as part of a new context, this prior knowledge and any set of assumptions may well prove to be wrong or less relevant to a new understanding of user needs, and a broader view of policy, organisations and future business models or services.

Starting with a clear and shared understanding of the assumptions you are making is a useful approach. Allowing you to move quickly to clearly defined research hypotheses, which can then act as an anchor point for discovery research and learning.


Originally published on my blog at hollidazed.co.uk on 10 March, 2019.

Leading Service Design

Leading Service Design with Kate Tarling, Ben Holliday and guests

Ben Holliday

Written by

Chief Design Officer, FutureGov / also find me at hollidazed.co.uk

Leading Service Design

Leading Service Design with Kate Tarling, Ben Holliday and guests

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