How to Identify, Prioritise, and Test Assumptions for Product Success
Every great product starts with a simple idea—and let’s be honest, a whole lot of assumptions. You might be convinced that your AI-powered productivity tool will change the world, or that your app for busy parents is exactly what they’ve been waiting for. But how do you know? How do you turn those assumptions into actionable insights that guide your next steps instead of leading you astray?
That’s what we’re going to dive into today: understanding, prioritising, and testing your assumptions to set your product up for success. By the end, you’ll feel confident in how to identify the assumptions you're making, transform them into hypotheses you can test, and use a handy tool called the Impact-Uncertainty Matrix to decide what to focus on first.
What Are Assumptions in Product Development, Anyway?
Okay, first things first: assumptions are those beliefs we all have about our products or users that feel true but haven't been proven yet. They drive our decisions—like what features to build or how to market our product. But here's the thing: left unchecked, assumptions can be dangerous. They can send us down paths that end in wasted time, resources, and, let’s be real, a fair amount of disappointment.
So why bother with assumptions at all?
Because they can also be incredibly useful! If you test them early, you can avoid big mistakes and build something people actually want. Let’s bring this to life with an example: imagine you’re creating a productivity app for project managers. One assumption might be, “Project managers struggle with managing post-meeting action items.” If you don’t test this, you could end up solving a problem that doesn’t even exist—or miss out on a better opportunity.
Different Types of Assumptions You’re Probably Making
Let’s talk about the different types of assumptions because not all are created equal. Here are the key types you should be aware of:
Exploratory Assumptions
These are open-ended and help you discover new insights.
Example: “Project managers experience stress from managing meeting-related tasks.”
Hypothesis: “We believe that at least 60% of interviewed project managers will report significant stress related to meeting management.”
User Assumptions
These are beliefs about your users—who they are, what they want, and how they behave.
Example: “Busy professionals need tools to automate meeting follow-ups.”
Turning It Into a Hypothesis: “We believe that busy professionals will save at least 20% of their time using an automated meeting follow-up tool.”
Problem Assumptions
These assumptions are about the significance of the problem you're solving.
Example: “Users often forget to complete meeting follow-ups, leading to miscommunication.”
Hypothesis: “We believe that miscommunication due to missed follow-ups affects productivity, and if true, at least 70% of surveyed users will report this as a major pain point.”
Solution Assumptions
These are about whether your product or feature will actually solve the problem.
Example: “An AI-powered summary tool will improve meeting productivity.”
Hypothesis: “We believe that using an AI-powered meeting summary tool will increase productivity, as measured by a 30% reduction in post-meeting admin work.”
Desirability Assumptions
These assumptions address whether your target audience finds the product attractive and is motivated to use it.
Example: “Users will be excited about using AI to simplify meeting tasks.”
Hypothesis: “We believe that at least 75% of users surveyed will express excitement about using AI features to simplify their meeting tasks.”
Value Proposition Assumptions
Do people value what you’re offering, and are they willing to pay for it?
Example: “Users are willing to pay a premium for an AI solution.”
Hypothesis: “We believe that 50% of surveyed users will be willing to pay £20/month for an AI-powered meeting assistant.”
Market Assumptions
How big is the market? Is it growing?
Example: “The market for meeting productivity tools is growing rapidly.”
Hypothesis: “We believe the meeting productivity market will grow by at least 10% annually, based on market research data.”
Viability Assumptions
These assumptions consider whether the product or business model is financially and operationally sustainable.
Example: “Our pricing model will generate enough revenue to cover costs and scale the business.”
Hypothesis: “We believe that our pricing model will achieve a 30% profit margin within the first year of launch.”
Feasibility Assumptions
Can you actually build and deliver this thing?
Example: “Our team has the technical expertise to build an AI-driven tool.”
Hypothesis: “We believe our current development team can build an AI tool within six months without exceeding the budget.”
Mapping Assumptions: Impact-Uncertainty Matrix
Now that you’ve listed out your assumptions, how do you figure out which ones to test first? Enter the Impact-Uncertainty Matrix. It’s a simple way to prioritize, and here’s how it works:
High Impact, High Uncertainty: These are your “Critical Assumptions.” They could make or break your product, so test them ASAP.
High Impact, Low Uncertainty: “Validated Priorities.” You’re fairly confident about these, but they’re still important to your product’s success.
Low Impact, High Uncertainty: “Explore Opportunistically.” These are interesting but not urgent. Test them if you have extra resources.
Low Impact, Low Uncertainty: “Low Priority.” These are the least important and can be deprioritized.
Using the Matrix:
Take each assumption and plot it on the matrix. For example, if you’re not sure whether your AI feature will resonate with users (high impact, high uncertainty), that’s a critical assumption to test right away.
From Assumptions to Hypotheses: A Step-by-Step Guide
Ready to get practical? Here’s how to turn your assumptions into hypotheses:
Write Down Your Assumption: Be as clear as possible.
Think About Evidence: What would need to happen to prove or disprove this assumption?
Create a Hypothesis: Use an “if-then” format to make it testable.
Example: “If project managers use our AI meeting tool, then 70% will report reduced stress within a month.”
Testing Your Hypotheses and Gathering Evidence
Now comes the fun part: testing! Here’s how:
Qualitative Methods: Talk to people. Conduct interviews or focus groups to dig deep into user needs.
Quantitative Methods: Run surveys or set up A/B tests to get hard data.
What to Do with Your Results:
If your hypothesis is supported, great! You’re on the right track.
If not, it’s time to pivot or refine your idea. Remember, this isn’t a failure; it’s a learning opportunity.
Final Thoughts
The beauty of this whole process is that it helps you work smarter, not harder. By testing your assumptions early and often, you reduce risk and set yourself up for success. So, grab a notebook and start mapping your assumptions today. Your future product—and your users—will thank you.
I’d love to hear from you! What assumptions are you testing right now, and how are you going about it? Drop a comment below or share your experience.
