The Conversion Rate Optimization (CRO) hypothesis is a fundamental statement that serves as the basis for any A/B test or experiment conducted on a website. 

Similar to a scientific hypothesis, it follows an “if this, then that” structure, outlining the expected impact of a specific change on user behavior or conversion rates. 

In the context of a website experiment, the CRO hypothesis contains all the necessary information to design, implement, and evaluate the experiment. 

Your CRO hypothesis should be clear, actionable, and measurable, providing a solid foundation for optimizing the conversion rates on a website.

This systematic approach adds precision and purpose to our strategies

When we clearly state our expected outcomes through hypotheses, we guide our efforts toward measurable goals. 

Hypotheses steer us away from randomness, developing a deliberate and goal-oriented mindset.

Understanding the Role of Hypotheses in CRO

When developed strategically and tactically, hypotheses influence every aspect of the optimization journey.

One outcome of developing hypotheses for your experiments is more precision. This precision results from the hypothesis steering your efforts away from randomness and towards specific, measurable goals. 

It’s about purpose, not chance.

Moreover, through systematically formulating and testing hypotheses, you don’t just speculate; you gain tangible insights. 

It’s a hands-on process of experimentation, where the results of each test contribute to a growing knowledge base that shapes your future strategies.

Hypotheses are also great allies for effective resource allocation. 

By prioritizing changes based on these hypotheses, you avoid random changes on your website that might not yield good results

It’s a proactive strategy ensuring that our efforts are concentrated where they will most likely yield positive results.

Finally, a hypothesis-driven approach creates a culture of continuous improvement. 

The iterative nature of hypothesis testing allows us to refine your strategies based on past learnings

Formulating a Strong CRO Hypothesis

Let’s say you’ve identified an issue on your website, and you’re now brainstorming potential solutions.

The text in the website’s hero section is too vague, using over-complicated terms. Visitors can’t understand the value we provide. Therefore they’re not convinced to buy from us.

A testable idea would be “let’s rewrite the content, making it easier for users to grasp what the product entails, who it’s for, and what its benefits are.”

In this scenario, the CRO hypothesis would look like this:

“By refining the clarity of our product information and overall presentation, we anticipate customers can better understand our offering, ultimately boosting the number of purchases.”

In this scenario, you’re aware of all elements involved in the process:

  • Your challenge
  • A solution to test
  • The testable metric for improvement

To that end, A straightforward formula to adopt for hypothesis formulation is:

Changing [identified problem(s)] 
into [proposed solutions] will result in [desired outcome]. 

While you can vary with the sentence structure, your task is ensuring the hypothesis encapsulates:

  • a clear problem description
  • a testable proposed solution
  • the intended change based on insights

All hypotheses should stem from conversion research findings, encompassing heuristic analysis, qualitative research, and quantitative research.

Let’s take an even closer look at the structure of a well-written hypothesis.

Problem Statement

Central to experience optimization is problem-solving. 

Start by formulating a hypothesis pertinent to the website visitor. Identify an aspect of the customer experience that demands attention, backed by data and insights.

Have you delved into site analytics to validate user encounters with this issue? 

Did usability testing confirm users’ challenges? 

Perhaps surveys shed light on their difficulties.

Step into your visitors’ shoes: what words would aptly describe this issue? 

The response hinges on your target audience. 

Tune in to how others discuss the topic to grasp their vocabulary and terminology.

Proposing a Solution

The CRO strategist should propose and describe the solution, allowing his team to understand the nature of the change.

Additionally, clarify why this solution is relevant to the problem.


Before testing, you need to define how you’re measuring the success of your experiment. This means deciding on a metric to follow.

Ensure the metric chosen accurately reflects the situation.

For example, if your issue is the number of subscribers, your metric should be email signups, not overall conversions.

Define success on your terms and understand what it entails, grounded in data and insights.

Key Elements and Examples of a Well-Structured CRO Hypothesis 

The three essential elements of hypothesis formulation are clarity, testability, and a foundation in data and insights.


A clear hypothesis succinctly articulates the problem, solution, and expected outcome. 

Clarity ensures that everyone involved, from team members to stakeholders, shares a common understanding of the goal.

To achieve clarity, you must first pinpoint the specific issue in the customer journey or website interaction and then describe how you plan to address the identified problem.

Finally, you must clearly articulate what changes you anticipate from implementing the solution. Here’s an example:

  • Problem: Users are abandoning the checkout page.
  • Solution: Streamline the checkout process by reducing the number of form fields.
  • Expected Outcome: Increase in the conversion rate on the checkout page.


If you can’t test a hypothesis, you’ll never know whether or not it was good.

Conversely, a testable hypothesis ensures that you can measure and analyze the impact of your changes, providing tangible results that guide future optimization efforts.

To make it testable, you have to clearly state the KPIs that will be used to measure success.

You must also establish a control group, so your test isn’t corrupted. 

Implement the proposed change on a subset of your audience while keeping another subset (the control group) unchanged for comparison.

Lastly, you must determine the duration of the experiment to assess the impact over a specific period.

For example:

  • Metric: Conversion rate on the checkout page.
  • Control Group: Users experiencing the existing checkout process.
  • Experimental Group: Users exposed to the streamlined checkout process.
  • Time Frame: Monitor the results over two weeks.

Based on Data/Insights

A hypothesis grounded in data and insights is more likely to be accurate and effective. 

It draws on information derived from various sources, including analytics, user feedback, and usability testing.

With that in mind, analyze data from web analytics tools to identify patterns and trends before formulating your hypothesis. You should also consider insights gained from direct user feedback, surveys, or interviews.

Because gut-based assumptions are rarely effective, the next step is to validate them. 

Use quantitative and qualitative data to confirm assumptions about user behavior.

Here’s an example of how to use insights to create a strong hypothesis:

  • Data Analysis: Identifying a high bounce rate on the product page.
  • User Feedback: Users express confusion about product specifications.
  • Resukts: Improving the clarity of product information will reduce bounce rates.

Data-Driven Hypothesis Creation

By now, you’re probably aware that a logical hypothesis doesn’t just pop out of thin air; it’s firmly grounded in data, and we can measure its success

Hypothesis creation is a strategic process in itself, needing a specific framework. 

Yet, some CRO professionals are still unsure how to formulate a solid hypothesis. 

Here’s an interesting and straightforward framework for developing a CRO hypothesis.

Using Data and Insights

The first step is to include data insights in your hypothesis.

Data is at the heart of any potent hypothesis – a high bounce rate, low click-through rates, or other metrics indicating an issue. 

This data becomes the anchor, shaping your rationale for change.

Then, you need to decide on the changes based on your data findings and customer insights. 

Your hypothesis’s main idea involves identifying changes based on well-reasoned assumptions derived from your collected data. 

It could be a design tweak, a copy alteration, or a layout adjustment.

Finally, we move on to predicting outcomes. 

Specify the metric change you anticipate after implementing the proposed change – whether it’s a decrease in bounce rate, an increase in conversion rates, or any relevant KPI.

The foundation of a well-written hypothesis involves thorough research. 

Leverage analytics tools to identify patterns and trends. 

Incorporate direct user feedback, surveys, and market research to get insights into user behavior and preferences.

Let’s deconstruct a hypothesis to provide you with a more specific example:

  • Issue: because we observed a significant bounce rate on the product page for mobile users:

You’ve pinpointed the issue using analytics: mobile users are bouncing off your product page.

  • Proposed solution: optimizing above the fold of this page will keep them more engaged:

Your proposed change is informed by analytics, aiming to optimize the content and layout above the fold of the product page.

  • Expected Result: we expect to see a lower bounce rates and an increase in conversion rates.

Your expectations are grounded in data and specific numbers.

The Role of Customer Personas

Tailoring hypotheses to customer personas adds depth to your optimization strategy. 

Consider different customer segments’ unique needs and preferences to create hypotheses that resonate with specific audience segments.

Why It Matters

Hypotheses in CRO act as compass guides, and they matter for two significant reasons:

Verification or Falsification

A well-crafted hypothesis empowers rigorous testing. 

Even if disproven, it’s a source of valuable data and insights. Learning what doesn’t work is just as crucial as knowing what does.

Systematic Decision-Making

We feel compelled to reiterate that CRO isn’t random experimentation.

A structured approach ensures informed decisions. Utilizing analytics, user feedback, and market research propels you toward better results.

Designing A/B Tests for CRO Hypotheses

Now that we’ve covered the creation of hypotheses, it’s time we looked at the next natural step in the process: testing your hypothesis. 

Navigating A/B Testing and Multivariate Testing

There are mainly two ways you can test your hypotheses. 

A/B testing involves comparing control and variation versions, where only one element is being tested

This approach is ideal when you want to isolate and evaluate the impact of a single change, such as testing different headlines, color schemes or calls to action.

If you have a specific element you suspect might be affecting user behavior, A/B testing allows you to test just that one variable while conserving your resources – as it requires less time and fewer resources compared to multivariate testing.

Conversely, multivariate testing involves testing multiple variations of different elements to understand how they interact and impact each other. 

This method provides a more comprehensive view but requires a more complex setup.

However, if you suspect several elements may be interconnected or affecting each other, multivariate testing allows you to test these variables simultaneously.

Defining Test Parameters

Before running your test, make sure you outline parameters such as sample size, test duration, and variations

The sample size is the number of participants in your test group.

More metaphorically, the sample size is the bedrock of statistical confidence. 

Choosing the right sample size ensures your results are reliable and representative of the broader audience.

The duration of your test impacts the reliability of your results. 

Too short, and you might miss trends; too long, and you might introduce external variables that skew your findings.

To define it, you should consider any seasonal trends influencing user behavior. Ensure your test duration captures a representative timeframe.

Remember that longer durations enhance your ability to detect subtle changes, but be mindful of potential external factors that could impact results.

Finally, variations represent the changes you’re testing. 

Whether it’s different headlines, colors, or layouts, defining variations clearly sets the stage for impactful insights.

In A/B testing, isolate one variable per variation. This ensures you can attribute any changes in results directly to that specific change.

On the other hand, in multivariate testing, carefully coordinate changes to understand how different elements interact. 

Ensuring Statistical Significance and Validity

Statistical significance is your assurance that the observed effects in your test results are not flukes but are likely to represent true changes in user behavior.

Striving for a high statistical significance level, typically 95% or 99%, is about building confidence in your findings. It’s saying, “We are 95% certain that these results are not due to random variation.” 

This ensures that your insights stand strong, free from the shadows of chance and biases, forming the foundation for informed decision-making and continuous optimization.

Analyzing Test Results and Learning from Outcomes

You’re left with an outcome once your test has run its course and achieved statistical significance. 

The test can be either a success or a failure. 

However, the interpretation of your results shouldn’t be as black and white. 

Let’s explore the close-to-last step of hypothesis testing: interpreting test outcomes and extracting insights from both successful and unsuccessful tests.

Interpreting Test Results to Confirm or Refute Hypotheses

Interpreting test results is how you’re finding your way through an ocean of data. 

It’s about uncovering the story behind the numbers, confirming or refuting your hypotheses, and understanding how changes resonate with your audience.

Keep in mind that numbers alone don’t tell the whole story. 

Dive deep into the data, exploring not just the what but the why behind changes. Understand user behavior and the factors influencing outcomes.

Revisit the significance levels you set. 

Are the results statistically significant? A significant change indicates that it’s likely not due to chance.

You should also break down the results by user segments. 

Analyzing how different groups respond to changes provides nuanced insights, helping tailor strategies to specific audiences.

Learning from Both Successful and Unsuccessful Tests

Whether or not your test was a triumph, each outcome uncovers a goldmine of insights. 

In this case, successes unveil strategies to implement, while failures are stepping stones to refinement.

Pinpoint the specific changes that contributed to success. 

Was it the revamped headline, the vibrant color scheme, or a streamlined checkout process? 

Knowing what worked is your playbook for future optimizations.

For example, you can apply successful elements to other areas of your website or campaigns. 

If a change boosted conversion rates on one page, consider implementing similar tweaks elsewhere for consistent success.

When it comes to unsuccessful tests, the first task is to uncover the reasons behind failure. 

Was it a flawed assumption, poor implementation, or external factors? 

Diagnosing the problem paves the way for corrective actions.

Look at unsuccessful tests as lessons, not defeats.

Extract insights on user preferences, behavior, and areas for improvement. This knowledge informs future hypotheses and strategic adjustments.

Iterative Testing: Refining Hypotheses for Ongoing Optimization

Finally, iteration is the heartbeat of successful optimization. 

Iterative testing involves refining hypotheses for continuous improvement based on insights from previous tests.

With insights from previous tests, reassess your hypotheses. Are they still aligned with user behavior and business goals? 

Adapt and refine hypotheses based on the evolving landscape.

In terms of implementation, rather than radical overhauls, start with gradual changes. This allows for more precise identification of what resonates with users and minimizes potential disruptions.

Don’t forget to track the cumulative impacts of iterative changes. Small tweaks may have compounding effects over time. 

Continuously monitor and adjust based on ongoing results.

Implementing Successful Hypotheses

Speaking of implementation – this is the last step in hypothesis testing. 

After all, if you found out that 8 hours of sleep/ night makes you happier, more energetic, and with a new lust for life, you wouldn’t go back to sleeping 5 hours, would you?

It’s the same in CRO – once you find a winning combination, you want to keep on enjoying its benefits. 

Let’s use the nuanced process of integration, scaling your learnings, and the invaluable practice of documenting successes for future reference.

Integrating Successful Hypotheses

A successful hypothesis isn’t a standalone victory; it’s a piece contributing to the larger picture of CRO success. 

Integrating these wins ensures that their impact reverberates throughout your optimization efforts.

The first step in integrating successful hypotheses is to determine whether they align with your overarching CRO strategy. 

They should complement and enhance, rather than disrupt, the flow of your optimization initiatives.

Then, you should identify key touchpoints in user journeys where successful changes can be seamlessly integrated. 

Use successful outcomes as a springboard for iteration. 

Consider expanding and refining that element across various pages or scenarios if a particular change led to increased conversions.

Scaling Learnings Across Different Contexts

Success, however you define it, shouldn’t be confined to a singular context. 

Scaling allows you to leverage successful hypotheses across diverse contexts or platforms, maximizing their impact.

Pinpoint elements of successful hypotheses that can be transferred to different contexts. 

This could include design elements, messaging strategies, or user interface improvements.

While scaling, be mindful of contextual nuances. 

What worked seamlessly on one platform might require slight adaptations for optimal performance in a different context. Adapt without diluting the essence of success.

Use A/B testing to validate the scalability of successful elements. Test variations in different contexts to ensure they consistently resonate with your audience.

Documenting Successes and Insights

Documenting successes serves as a testament to your wins and becomes a valuable resource for shaping future strategies.

Create a detailed log of successful hypotheses, outlining the changes, context, and observed impact. 

This record serves as a reference point for future decision-making.

Integrate user feedback into your documentation. 

Understanding how users responded to successful changes adds depth to your insights and aids in refining future hypotheses.

Finally, for a little order, categorize successes based on their impact and relevance. 

Knowing which changes had significant effects versus incremental improvements helps prioritize future optimization efforts.

Case Studies: Effective CRO Hypotheses in Action

As we are a team of self-proclaimed data-geeks, we love using data to develop strong hypotheses and then test them alongside our clients. 

To that end, let’s look at our work with Leroy Merlin.

Our partnership with Leroy Merlin Romania started in 2021, and during this time, we conducted over 45 experiments with Leroy. 

Here’s one of our favorite ones. 

In the exploration of Leroy Merlin’s website, our UI/UX audit ventured into the user journey, uncovering fascinating findings that could reshape the online experience.

Our heatmap analysis brought to light an interesting discovery. 

Only 34.3% of users navigated down to the benefits and services section on the homepage. This revelation hinted at untapped potential.

What caught our attention was the stark contrast in user engagement – some benefits hidden on a dedicated page proved more alluring to users than the handful presented on the homepage.

Armed with these revelations, we crafted our hypothesis to center stage: 

By repositioning the benefits and services section under the hero banner and replacing some of the less compelling offerings, we can improve the chances of users proceeding further down the funnel and ultimately making a purchase.

A diagram showing the different types of control.

Our hypothesis centers on enhancing the user journey, envisioning a scenario where users seamlessly progress down the funnel, propelled by the allure of revamped benefits and services. 

The ultimate goal? 

To transform casual visitors into confident purchasers driven by a digital experience tailored to their preferences.

Testing this hypothesis brought these results: 

  • 10.38% increase in conversion rate
  • 9.57% increase in revenue/user 
  • 95.5% chance to win

Encouraged by these initial results, we went on to identify further opportunities for design improvement. 

We realized users were less inclined to click on the sections to access more information, and the content occupied considerable space.

With this insight, we formulated a new hypothesis:

By showing only the title for each benefit and hiding the rest of the copy, we would minimize the need to scroll and encourage users to move down the funnel.

We tested this idea against the variation from the previous experiment:

A screen shot of a dashboard showing different options.

This experiment was also a success, with the following results:

  • 14.36% increase in conversion rate
  • 8.43% increase in revenue/user 
  • 98.4% chance to win

Our findings serve as guiding lights, leading us as on a path where user engagement and conversion are not just goals but inevitable outcomes. 

This was actually the case with Leroy Merlin – check out the full Case Study here and discover more successful experiments! 

Are you looking for someone to take over your entire CRO journey, from delving into research to seeing the final results? 

Search no more – we’re here to help!

Let’s set up a call and team up to eliminate the guesswork, prioritize smart analysis and testing, and achieve some fantastic results for you!

Wrap Up

As you’ve seen, a well-crafted hypothesis can become your guide – it gives you direction and focus in your CRO processes. 

If grounded in data and clarity, the CRO hypothesis becomes the catalyst for impactful changes.

However, formulating the hypothesis alone doesn’t offer much. 

Instead, formulating hypotheses has to kickstart a process of ongoing learning and constant experimentation.

Good luck, and remember – we’re just a click away!