A matrix for prioritizing user research

A guide on how to focus one’s efforts regarding research based on various situations.

“…with limited time and resource, how should we decide on what action to prioritize first?”

Ideally, there should be a balance between the demand and the ability to conduct research requests. However, sometimes demands are too high, or researchers are under-resourced. Then, what should be done if we are put in this less than ideal situation?

Jeanette Fuccella, the Director of Research & Insights in Pendo.io, US addressed those issues through her article in 2021. She developed a 2x2 matrix that can guide researchers to focus on their time and energy in various contexts. Her framework attracted many researchers, including myself. This time, I will summarize my take on her original framework since I think it can be useful for many people. It consists of:

The original prioritization framework, as developed by Jeanette Fuccella (2021)

A. Ship it & measure phase

B. Research light phase

C. Design heavy phase

D. Research heavy phase

Now, I’m going to break it down one by one to give you a better understanding:

A. Ship it & Measure phase (High problem clarity, Low risk)

Happens when:

• You and your stakeholders already have concrete hypotheses and backup data (ie: facts supported by previous research or recent study regarding trends, user’s behavior, funnel analysis) about customers and their problems, AND
• The risks of misinterpreting the situation tend not to be significant, since the problem is likely to be known and therefore, expected.
• This tends to happen more when a concrete solution of said problems has been made and is ready to be tested/implemented.

Main keyword: Analytics-driven

What to do:

• Skip primary user research.
• Make an informed hypothesis, take courage to implement a new feature/design (it can be through experimentation or A/B test first, not full launch)
• Measure the results
• Monitor the data and learn from the experiment

B. Research Light phase (Low problem clarity, Low risk)

Happens when:

• You and your stakeholders are clueless about what is going on (ie: lack of data and knowledge about the topic) that you guys can’t even form coherent hypotheses about the situation, AND
• Research questions in this quadrant are often murky but don’t present the same types of risks as with the “Research Heavy” quadrant. Since the risks are low, the need to be accurate is diminished.

Main keyword: Lean research support — increasing problem clarity while minimizing resource investment.

What to do:

• Collaborate with the broader team to identify and test hypotheses through rapid research activities (ie: guerilla test, desk research, quick survey/polls, rapid Q&A, etc) that will improve problem clarity so that team can move to design and solutions.
• With enough findings from rapid research, the team will have quantitative and qualitative data to help refine the problem space.

C. Design heavy (High problem clarity, High risk)

Happens when:

• You and your stakeholders already have a concrete hypothesis and backup data (ie: facts supported by previous research or recent study regarding trends, user’s behavior, funnel analysis) about customers and their problems, AND
• The risks of not building the appropriate solution to address the problem is crucial and can bring big drawbacks to the company

Main keyword: Design research-driven — this quadrant exists to empower and inspire designers

What to do:

• Collaborate with other divisions to create a design that is representative in answering the problem that is deemed crucial. This can be done in many ways (ie: using findings from primary research — ie: IDI, UT, Survey, Diary study — as the basis for ideation workshop, etc).
• Provide decision-making confidence for those involved in a collaboration to make sure that the design work can be done efficiently and appropriately answer the problem. The expected outcome will involve around design prototype.

D. Research heavy (Low problem clarity, High risk)

Happens when:

• You and your stakeholders are clueless about what is going on (ie: lack of data and knowledge about the topic) that you guys can’t even form a coherent hypothesis about the situation, AND
• The risks of not building the appropriate solution to address the problem is crucial and can bring big drawbacks to the company. Solutions too quickly run the risk of solving the wrong problem–or no problem at all.

Main keyword: Generative research, Research-driven — Questions in this quadrant ultimately save time by requiring you to slow down and invest in the research.

What to do:

• Spend time to carefully conduct primary research. This will help the team uncover the unknowns and sharpen the knowns of the situation. The team can then create blueprints from the findings, such as end-to-end user journeys or defining the JTBD of users.
• The ROI will be experienced subsequently across the product team, as they will be able to form better hypotheses and craft more accurate assumptions due to the depth of their understanding.

It is known that product teams often work under pressure. They are expected to simultaneously balance the speed of their work with which they need to deliver new features and the need to test hypotheses and uncover insights from users. This is where researchers play a key role in helping to guide the team on what research that can be done. With this 2x2 prioritization matrix, hopefully, people will be able to identify their given situation and make decisions on what kind of research to prioritize accordingly.

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