What makes a good CRO hypothesis?
A strong CRO hypothesis gives structure to your experimentation. Instead of testing random ideas, it helps you define the problem, explain the proposed change, and describe the outcome you expect to see if the change works.
In most cases, a good hypothesis includes three main parts:
- an identified or presumed problem
- the proposed solution to address that problem
- the expected result of making the change
The problem can be identified in a number of ways, including analytics data, user research, customer feedback, session recordings, usability reviews, or previous test results. For example, you might notice that users are dropping out at a specific step in a journey, hesitating on a form, or failing to engage with a key call to action.
A simple problem statement might be: “Customers are finding the language on the site too technical, which is creating confusion and friction.”
The hypothesis then turns that insight into a testable idea. For example: “If we simplify the language used on the site, more visitors will understand the value proposition and we will see higher click-through rates and stronger engagement.”
The CRO Hypothesis Builder
The CRO Hypothesis Builder below is designed to help turn observations into structured test ideas. It takes the key parts of a good hypothesis and helps you organise them into a format that is easier to test, measure, and share with your team.
A useful hypothesis is not just about what you want to change. It should also reflect why that change is likely to help. This means thinking about the underlying cause of the problem and the effect it may be having on the visitor.
Using the example above, technical language may be increasing confusion, creating doubt, and making visitors less likely to continue. That insight leads to a clearer proposed solution: simplify the language to reduce anxiety and make the next step feel easier to take.
The expected result should also be specific. Rather than saying a change will “improve the page”, define what better looks like. That could mean a higher click-through rate, more form completions, increased progression to the next step, or stronger overall conversion performance.
How to use the hypothesis builder
To build a working hypothesis, complete the fields below with as much clarity as possible:
- How did you identify the insight? For example: Google Analytics, heatmaps, user testing, customer feedback, or session recordings.
- What did you notice? Describe the behaviour or friction point you observed.
- What do you want to change? Explain the improvement or solution you want to test.
- Which audience are you hoping to affect? For example: all visitors, mobile users, new visitors, or a specific segment.
- What should the outcome be? Describe the expected behavioural or experience improvement.
- How will this be measured? Define the metric or result that will tell you whether the test was successful.
Once you complete the form, the builder will generate a clearer, more structured CRO hypothesis that can be used as the basis for an A/B test or experimentation brief.
Example of a CRO hypothesis
Here is a simple example:
“Because analytics data shows that visitors are dropping out at the password field during sign-up, we believe that reducing anxiety around password creation will increase progression to the next step for mobile users. We will measure this by tracking completion rate through to the next stage of the funnel and overall sign-up conversion.”
This kind of structure makes the hypothesis easier to understand, easier to test, and easier to learn from once the experiment is complete.
Why hypothesis-led testing matters
A good experimentation programme is built on more than ideas. It is built on evidence, structured thinking, and clear measurement. Hypothesis-led testing helps teams focus on meaningful problems, avoid vague test ideas, and learn more from both winning and losing experiments.
By improving the way hypotheses are written, businesses can improve the quality of their testing roadmap and make better decisions about which ideas are worth prioritising.
To build a working hypothesis, first fill out the form below
Need help building stronger test hypotheses?
Kraken Data is software agnostic and works with your experimentation platform of choice. What matters most is not the tool itself, but the quality of the insights, hypotheses, implementation, and analysis behind the test.
If you would like help developing stronger test hypotheses, improving your experimentation roadmap, or connecting analytics insights to CRO opportunities, please contact us.