Our team at Tetra Insights has completed rigorous research work for dozens of companies across industries. Through this experience, we’ve uncovered some common pain points and stresses of executing effective research. Among the most common challenges are getting buy-in and ensuring that findings are both meaningful and actionable.

To solve these problems, we developed a simple and reliable method for generating research that cater to the needs of stakeholders and ensure that product teams get the insight they need to build great things.

We call this simple Decision Mapping.

Decision Mapping helps solve the following common research problems:

  1. Creates clarity on why research is being done
  2. Syncs research and data work with strategic decisions
  3. Improves research buy-in across the team
  4. Contextualizes research data amongst other meaningful sources of insight
  5. Ensures research results are directly actionable

We’ve found this simple strategy to be an invaluable part of our toolkit, so I wanted to share a tactical overview of how it works and how you can apply it to your organization.

How does decision mapping work?

Step 1: Clearly define the decisions you are trying to make with the help of research.

Examples of decisions to be made:

  • Should we build this product?
  • Can we remove this feature without negative repercussions?
  • Which navigation concept should we build into our mobile app?
  • Will a mobile app be valuable for our existing desktop users?
  • Do we need to revamp our customer onboarding experience?

Your decisions can typically be written in the form of a question. They can range from a broad, overarching decision (e.g. should we pursue this opportunity?) to something very UX-specific (e.g. which sign up flow design variant should we implement?).

Choose a pending decision that your team agrees is important enough to dedicate time and resources to solving. Hypothetical decisions can be helpful for framing a research exercise, but the closer to real a decision is, the easier it will be to get buy-in and hone a research plan.

Step 2: Outline the key questions, hypotheses, and assumptions that will influence the decisions being made.

Decision to be Made: Should we build this product?
 → Key question: What pain points are our target users currently experiencing?
 → Key question: What are they using now to solve these problems? What do they like/dislike about these solutions?
 → Hypothesis: Our workflow automation tool would save these users hours of time.
 → Assumption: Our target users have budget authority up to $1,000 for a solution like ours.

Try to make your list of key questions, hypotheses, and assumptions as exhaustive as possible. Not all of them will be useful for data collection purposes, so it’s important to have a thorough list from which research and data needs can be prioritized.

Also, as a general tactic for strategic decision-making, listing out these components helps provide structure and a rubric for which a good decision can be made.

Step 3: Map questions, hypotheses, and assumptions to potential data points that would answer or validate/invalidate.

Decision to be Made: Should we build this product?
→ Key question: What are they using now to solve these problems? What do they like/dislike about these solutions?
→→ Data Point: Third party research about the most popular solutions for X.
→→ Data Point: Conversations with target users providing details about what solutions they use for X and what they like/dislike about those solutions
→→ Data Point: Conversations with target users having them evaluate marketing websites for popular solutions for X
→→ Data Point: Landing page tests seeing if people will opt-in for more information about a new solution for X
→→ Data Point: Conversations with Tony & Anna in our HR department
→→ Data Point: Survey responses from target users asking them to list solutions to problem

An important component to this step is that the data points that you list here need not actually be available. This is an exercise in listing out where relevant and useful data could possibly be found. Whether or not you can actually get that data is separate.

This step allows you to think through all reasonable options for answering the questions or validating/invalidating the hypothesis or assumption. By listing all potential options of helpful data, you will broaden your scope of sourcing insight, as well as elucidate why user research is useful in the context of other information.

A side benefit is that some of the data points that are useful can be better generated by other members of your team (outside of UX and research). Perhaps the product manager, engineer, or marketing lead can find the analytics data or third party research that is helpful. By providing a wider array of valuable insight options, it helps to increase buy-in and commitment across your team.

Step 4: Determine which data points can be generated with user research

Decision to be Made: Should we build this product?
→ Key question: What are they using now to solve these problems? What do they like/dislike about these solutions?

Which of the listed data points can we with user research?
→→ Data Point:
Conversations with target users providing details about what solutions they use for X and what they like/dislike about those solutions
→→ Data Point:
Conversations with target users having them evaluate marketing websites for popular solutions for X
→→ Data Point: Survey responses from target users having them list 
solutions to problem X

Based on your list of potential data points, pick out the ones that can best be provided through user research. The best points to include qualitative research are when you want to observe actual behaviors (e.g. interacting with a prototype, assessing competitive marketing materials) or when you want to probe deeply to understand unmet needs, pain points, and desires (e.g. jobs to be done, concept validation, feature prioritization). You can also include questions that work well for a survey, as that can be a meaningful part of a user plan.

Step 5: Create a draft of user test questions/exercises that would provide the required data.

Data Point: Conversations with target users providing details about what solutions they use for X and what they like/dislike about those solutions.
→ User Test:
→→ Question:
Tell me about what you currently use for X in your current job.
→→ Exercise: Can you please open up the application and show me how you use it for X?
→→ Question: How does this solution compare to other products you’ve used in the past for X?
→→ Question: Is there anything you dislike or find particularly painful about this solution?

Data Point: Conversations with target users having them evaluate marketing websites for popular solutions for X
→ User Test:
→→ Exercise:
Please open your browser and go to [marketing website]. Take a few minutes to browse the site, and mention anything that catches your eye or you find particularly interesting.
→→ Question: Now that you’ve looked at 3 different websites marketing solutions for X, which one are you most interested in learning more about? Why?

Data Point: Survey responses from target users having them list solutions to problem X
→ Survey:
→→ Question:
Please list any software you’ve used in the past 5 years to manage X.
→→ Question: On a scale of 1–5, please rate each software you listed based on how likely you would be to recommend it to a colleague looking to solve problem X.

By rigorously defining decisions and then mapping them to points of inquiry, a meaningful research plan can be generated. This is a thoughtful, proactive method of research plan creation.

There are some important conceptual elements that separate Decision Mapping from other techniques of creating research plans. Some key considerations include the following:

1) User research is just one source of data and insight

User research is undoubtedly an invaluable component of generating user insight (if you’re not doing user research regularly, you should start immediately). But it is only a single source amongst many other potential sources of valuable insight.

Too often teams can try to answer all questions through research even though there are a wide variety of other sources of data. Insight can come from production environment analytics, third party research reports, heuristic best practices, and even just having a conversation with an expert. The Decision Mapping technique allows you to consider all available arrows in your quiver for helping inform strategic decisions.

2) Not all data points are equally valuable

Sometimes, the data from your research work may be less valuable than other data (for instance, a live landing page making a real sale offer as part of a product validation experiment). With Decision Mapping, we don’t assume that research will always be the best use of time and resources. Make the effort to determine which data points are going to be most reliable and meaningful and plan to contextualize research findings among other inputs.

One thing that we often do internally is assign a ‘probability of reliability’ to our data points. For example, observing real behavior is typically 90+% reliable, whereas self-reported survey data is somewhere in the 70% range in our consideration. This is a more complicated method of assessing data, but it’s also a helpful way to make sure you’re not overstating findings. It’s important to remember and remind your team that all data is probabilistic and it is impossible to predict outcomes with 100% accuracy.

3) Decision Mapping is a team sport

This technique assumes you are looking to get buy-in from a wide variety of stakeholders — potentially including executives, management, marketing, design, engineering, and anyone else involved in strategic execution for building and selling your solutions.

For some research work, you may not actually want the rigor of directly linking research to decisions — you may just be doing exploratory or confirmation work. Be sure that you don’t make too many assumptions about what is strategically important and skip the step of getting feedback and buy-in, otherwise this technique loses a large part of its meaningfulness.

4) Don’t let a lack of strategic decisions stop you from doing research

You should be doing user research regularly (at least a handful of customer interviews 1–2 times per month). Strangely, many organizations will say they don’t have any important strategic decisions upcoming. Though this is likely just a misunderstanding of what strategic decisions are, don’t let the rigor of Decision Mapping get in the way of just doing research regularly. Make sure to have other questions and exercises that are simply of interest available for your monthly research plans. You can have a set of questions and exercises you ask every month, which are still very helpful for benchmarking and tracking changes over time.

Decision Mapping is a powerful and flexible method for building targeted research plans that are insightful and actionable. If you haven’t tried it before, give it a spin for your next research exercise and let me know how it goes.



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