The control page is the standard version of a page or app, against which CRO professionals are measuring variations’ performance. This page represents the original design or content that you want to analyze for potential improvements. 

When conducting A/B testing, changes or alternative versions are introduced, and their performance is evaluated in comparison to the control page to determine the most effective elements for achieving specific goals, such as increased user engagement or conversion rates. 

Essentially, the control page provides a stable reference point for assessing the impact of alterations in an objective and systematic manner.

The importance of the control page lies in its ability to establish a solid foundation for experimentation. 

Much like a stable platform, it allows researchers and developers to measure the efficacy of changes introduced in alternate versions. 

In the long term, this comparison delivers a nuanced understanding of what resonates with users, be it in terms of design elements or content provided. 

Through a consistent control page, experimental design becomes a measured process, ensuring that insights are true and actionable. 

In a nutshell, the control page adds methodical rigor to the dynamic process of A/B testing, steering it towards informed and data-driven decisions.

The Role of the control page in A/B Testing

Before focusing on the control page in particular, let’s look at the overall methodology of A/B testing.

A/B testing, or split testing, involves comparing two versions of a webpage to determine which one performs better. 

It’s a virtual battle of ideas, where you have your original control page (the A) and a tweaked version (the B). 

The goal is to identify which changes resonate more with your audience, i.e., which page performs better.

In this virtual battle of ideas, the control page acts as the referee, a yardstick against which you measure the performance of the variation. 

This comparison is the fundamental process through which you measure and analyze the impact of website changes. 

Whether they involve tweaking the website layout, the design, or the content, changes on your website can either hurt or improve the user experience – and you need the control version to steer you in the right direction. 

Evidently, to ensure the integrity of your experiments, you’ll first need to validate the integrity of the control page.

If your control page isn’t consistent, you’ll end up testing against a moving target – you’ll end up with data that’s more confusing than clarifying. 

To create a reliable baseline for your experiments, you’ll need to keep the control page constant. This is how you can confidently attribute changes in performance to the variations introduced and get a clear picture of what’s working and what’s not.

Control Page and Test Page: Understanding the Differences

These two pages are distinct elements in the A/B testing process, each playing a distinct role in shaping the user experience. 

The control page serves as the benchmark, the status quo of your webpage. It remains untouched, allowing for a reliable comparison against the variant. 

On the other side, the test page is the experimental contender, embodying the changes or tweaks introduced during the A/B test. 

The test page is where you get creative, testing hypotheses and exploring potential improvements.

The Importance of Only Making One Change at a Time

The cardinal rule, when transitioning from the control page to the test page, is to introduce only one change at a time. 

This approach ensures that any observed differences in performance can be confidently attributed to the specific variation. 

Whether it’s altering a headline, adjusting colors, or tweaking the layout, isolating changes enhances the clarity of results and facilitates a more precise understanding of their impact.

Ensuring Both Pages are Served to Similar Audiences under Similar Conditions

For a fair comparison, it’s essential to ensure that both the control and test pages are served to similar audiences under comparable conditions. 

This involves randomizing the distribution of traffic between the two versions to eliminate bias. 

Factors like user demographics, geography, and device types should be considered to create a level playing field. 

Be consistent in audience exposure, so you ensure that any observed differences in performance are genuinely reflective of the impact of the changes rather than external variables.

Setting Up a Control Page

So, you know your control page needs to be constant throughout the entire experiment – but how do you set up this page?

Here’s a practical approach that will help you create a solid framework for deriving meaningful insights from your experiments.

Criteria for Selecting a Control Page

Start by identifying the webpage that currently serves as your standard. 

This should be a page that consistently attracts traffic and represents the typical user journey. 

Consider factors such as conversion goals, user engagement, and overall performance metrics

To your best ability, avoid choosing a page that has recently undergone significant changes or one that deviates from the usual user experience.

Ensuring the Control Page is Representative

Use heat maps and analytics tools and to regularly audit your control page to ensure it stays relevant and mirrors the current user experience. 

Take note of any recent updates in design, content, or functionality. 

If your website has undergone a facelift or introduced new features, make sure your control page reflects these changes.

The goal is to capture a snapshot of your site as users are experiencing it in real-time, providing a baseline for accurate comparisons.

Best Practices for Documenting the Control Page Setup

Create a detailed documentation plan for your control page setup. 

Document the specific elements of the control page, including visual design, content structure, and any interactive features. 

Note the date when the control page was selected and record any modifications made for clarity. 

This documentation becomes a vital reference point for future analyses and ensures consistency across testing iterations. 

When you create a standardized process for documentation, you’re not only adding transparency to your experimentation but you’re also streamlining the replication of experiments for ongoing testing efforts.

Identifying Variables for Testing

Keep in mind that CRO isn’t just about making random website changes; it’s about making informed, hypothesis-driven adjustments that lead to a deeper understanding of user interactions.

Let’s see how you should approach selecting the variables to test in a way that clears the path for data-driven optimizations.

How to Choose Which Elements to Test Against the Control Page

When selecting elements for testing against the control page, focus on aspects that directly contribute to your predefined goals. 

These could include headlines, call-to-action buttons, images, or even entire sections of your webpage. 

Prioritize elements that are likely to influence user behavior and contribute to the success metrics you’re aiming for. 

For example, imagine you’re running an online fashion store, and your primary objective is to increase the conversion rate, encouraging visitors to purchase.

The key is to choose variables that align with your specific objectives for the A/B test.

In this scenario, your A/B testing strategy would revolve around selecting variables that have a substantial impact on user behavior and, consequently, contribute to increased conversions. 

High-impact variables could include the placement and design of your “Add to Cart” button, the visibility of product images, or the clarity of product descriptions.

The Importance of Hypothesis-Driven Testing

Before making changes, formulate clear hypotheses. 

Ask yourself questions like: “If we tweak the wording of our call-to-action, will it result in higher click-through rates?” or “Will adjusting the color scheme positively impact user engagement?” 

Evidently, all these questions should arise from data and UX/UI audits – it shouldn’t be a guessing game. 

Hypothesis-driven testing helps establish a structured approach, guiding your experiment with a clear purpose. 

This ensures that your testing efforts are not arbitrary but rooted in hypotheses that can either be validated or refuted based on the data collected during the experiments.

Keeping Changes Isolated to Measure Impact Accurately

Finally, it’s crucial to isolate variables if you want to accurately measure the impact of changes.

This means introducing one modification at a time while keeping all other elements consistent with the control page. 

By doing so, you can confidently attribute any changes in your metrics to the specific change you implemented. 

This approach allows for a granular understanding of how each variable influences user behavior, providing actionable insights for optimization.

Measuring the Effectiveness of the Control Page

To effectively measure the performance of the control page you’ll need an approach that combines KPIs, tools for analysis, and an understanding of statistical significance. 

Here’s a breakdown of these elements:

Key Performance Indicators (KPIs) to Track

Firstly, you’ll need to identify the right KPIs to track. 

Depending on your specific goals, KPIs would include conversion rates, click-through rates, bounce rates, or revenue per visitor. 

These metrics provide quantifiable insights into user behavior and the impact of changes on your webpage. 

For example, if your objective is to increase engagement, tracking time-on-page and interaction rates becomes essential.

Essentially, you’ll want to measure the same KPI that first signaled an opportunity for improvement during your data and UX/UI audits.

Tools and Software for Monitoring and Analyzing Results

Fortunately, you have a variety of tools and software at your disposal to streamline the monitoring and analysis of A/B testing results. 

A/B testing platforms like Optimizely, VWO, or Adobe Target offer features for experiment setup, traffic allocation, and result analysis. 

These tools typically provide statistical significance calculations and visualizations, making it easier to interpret the impact of changes on the control page.

Omniconvert Explore 

Omniconvert Explore is your go-to platform for improving your website’s performance. 

Not only is this platform flexible and feature-heavy, but it’s also intuitive and simple to use. 

Unlike some other tools that limit how you can personalize experiences for different user groups, Omniconvert Explore takes a more flexible approach. 

You can segment your customers based on criteria such as the device they use, where they’re coming from, where they are, and even their past online behavior.

What sets Omniconvert apart is its super broad range of options to target specific groups, offering one of the most extensive lists of targeting options in the market.

Determining When the Data Is Statistically Significant

Statistical significance is a critical aspect of A/B testing. 

It indicates whether observed differences in performance between the control and test pages are likely due to the introduced changes rather than random chance. 

Tools like statistical calculators or built-in features in A/B testing platforms often provide significance levels. 

Generally, a significance level of 95% is commonly accepted, meaning there’s a 95% confidence that the observed results are not by chance. 

If the data doesn’t reach statistical significance, it may be premature to draw concrete conclusions, and further testing or a larger sample size may be necessary.

When to Update the Control Page

Now that we know how to select variables to test, it’s time we looked at the when (no pun intended.)

When can you consider the control page no longer relevant? When should you turn the variation into the website standard?

To find this moment, you’ll need to balance the need for improvement with the reliability of existing data. 

Let’s expand this idea. 

Criteria for Deciding 

Firstly, before you decide to integrate elements from the test page into the control page, you’ll need to carefully evaluate your A/B testing results. 

Look for statistically significant improvements in KPIs that align with your goals. If the test page consistently outperforms the control page and these improvements are deemed meaningful, it may be time to consider updating the control page. 

However, be mindful and make sure that observed differences aren’t due to external factors or seasonal fluctuations.

Evolving the Control Page Over Time

The evolution of the control page is an iterative process guided by data-driven insights. 

After identifying successful changes through A/B testing, consider integrating those elements into the control page. 

This evolution should be gradual, focusing on one proven modification at a time. This approach helps maintain a clear understanding of the impact of each change and minimizes the risk of unintended consequences. 

Regularly reassess and update the control page as new insights emerge from ongoing testing or as your website’s goals evolve alongside your business.

Maintaining a Change Log and Documentation for Historical Comparison

Finally, you should consider maintaining a change log and detailed documentation of any changes. 

Record each modification made to the control page, including the reason behind the change, the date of implementation, and the specific elements adjusted. 

This documentation serves as a valuable reference, allowing you to track the evolution of the control page over time.

With a clear record of changes, you can evaluate the effectiveness of each modification and understand how your website has evolved in response to user behavior and testing outcomes.

Case Studies: Effective Use of Control Pages

Now, let’s take a brief intermission to dive into a real-world illustration of this methodology in action. 

In our partnerships with esteemed clients like Tempur, Decathlon, Max Mara, Leroy Merlin, and many others, we’ve conducted over 50,000 experiments to test our hypotheses and enhance user experiences.

From subtle adjustments, such as elevating a CTA, to transformations involving the complete revamp of entire webpages, each experiment tells a unique story of optimization

One of our experiments regarded the Add-to-Cart Page of Orange – the telecommunications malamouth, and one of our established clients. 

For any eComm site, this page is one of the fundamental touchpoints in the customer journey – improving it means creating a highly profitable page. 

During our work with Orange, we aimed to improve the Cart Page to push users further into the conversion funnel and complete more purchases across all devices.

The methodology we used was the same as the one discussed in this entry. 

We first analyzed reports in Google Analytics regarding checkout behavior, then we delivered improvement suggestions in order to determine if the cart abandonment rate can be decreased. 

After sharing our insights, we initiated experiments on the cart page.

The target audience for this experiment was already inclined to make a purchase from Orange, having at least one item in their cart. Our objective was to encourage those who hesitated to complete their purchase immediately upon reaching the cart page.

This is how the control page looked like before the experiment:

To guide users with items in their cart further down the conversion funnel, we leveraged two highly effective persuasion principles: scarcity and urgency.

Our proposed experiment involved presenting users with items in their cart with two sentences designed to evoke these principles.

We tested three distinct color schemes and two different positions for these messages, all strategically placed above the contents of the carts.

Here’s one of the variations which we tested against the control page: 

This variation delivered these results: 

  • 7.65% – increase in Conversion Rate
  • 11.53% – increase in Revenue/user 
  • 98.65% – chance to win

Curious for more? Then read the entire Case Study here

This experiment highlights the power of adding both scarcity and urgency nudges during checkout, bringing in great improvements when done right and ethically.

Some people might raise eyebrows when it comes to experimenting in the checkout process. 

They worry it could be a bit disruptive or technically difficult. 

However, these results show that the positive outcomes from a successful experiment extending through the funnel are way more valuable than the concerns folks might have about testing in the checkout process.

Your Turn

How about showcasing a Case Study that shines a spotlight on your achievements?

Maybe you’re short on the skills, time, or resources to dive into the process of researching and designing your own A/B tests. But if you’re eager to boost your website and provide top-notch customer experiences, don’t worry – we’ve got you covered!

Our skilled Managed Services team can handle the entire CRO process for you – from running audits to delivering tangible results!

Let’s chat about how we can make this happen for you.

Wrap Up

So, what’s the takeaway from this entry?

While initially a simple webpage, the control page is actually crucial in the A/B testing process. Not only does it anchor experiments but it also fosters an intimate understanding of user preferences. 

The control page is there to maintain a methodical rigor for your experiments, ensuring that insights derived from testing are both true and actionable. 

All in all, the essence of this page lies in its ability to provide a stable platform for meaningful experimentation.

Speaking of experimentation, keep in mind that A/B testing is an ongoing process. 

The path to optimization lies in the commitment to refine, adapt, and relentlessly optimize – a journey that transforms websites from mere virtual window shopping into relentless sales agents for your company. 

Don’t forget that we’re here – should you ever need a helping hand.