A variation refers to a modified version of a webpage or a specific element within a webpage. 

These modifications are used in A/B testing to observe and analyze user responses, with the ultimate goal of identifying the most effective configuration that achieves specific business objectives (usually conversion rates).

This method allows us to keep improving our websites based on real data, not just guessing.

By doing this, you can make decisions based on what really works for the people using your websites, making the online experience better and more effective.

Control vs. Variation – Omniconvert Case Study

How Variations Impact the Outcome of A/B Tests

Keep in mind that variations are created for A/B tests – where we are essentially comparing two or more versions of something.

To that end, variations allow us to test different ideas or changes we’ve made to a webpage. You’re experimenting with various elements such as colors, text, images, or buttons to see which version works better.

By presenting different variations to different groups of people at the same time, you can understand what users prefer. 

For example, if you’re testing two different headlines, showcasing two distinct benefits on a webpage, you can see which one gets more positive responses from users. 

Going even further, you can understand what users appreciate most in your products, then apply this info to improve your products.

The ultimate goal of variations is often to improve something specific, such as getting more people to click a button or make a purchase. 

A/B tests with variations provide you with real data, not just opinions or guesses. 

This data leads to informed decisions about what changes to keep, based on how users actually interact with the different versions.

It’s like fine-tuning a recipe – you try different ingredients until you find the perfect combination. 

Similarly, you use variations to adjust your website and create the best experience for users.

Techniques for Creating Variations

When it comes to creating variations for A/B testing, you need to test elements that might resonate most with your audience. 

Here are some simple ideas to help you design effective variations:

Changing Call-to-Actions (CTAs)

One powerful way to create variations is by tweaking the call-to-action buttons. These are the buttons that guide users to take specific actions, like ‘Buy Now’ or ‘Sign Up.’ 

Try altering the wording, color, or size of these buttons to see which version encourages more clicks and interactions. 

For example, changing ‘Buy Now’ to ‘Get Yours Today’ might make a difference.

Modifying Design Elements

Design plays a crucial role in how users perceive a webpage. Experiment with variations in design elements such as images, colors, and layout. 

Maybe you want to test a new color scheme or a different arrangement of product images. Small changes can have a big impact on user experience. 

Altering Content

The words on your webpage matter. 

Consider creating variations by altering the content, especially headlines and product descriptions. 

Test different tones, lengths, or styles to see which resonates better with your audience. 

For example, a playful and casual tone might work better for some products, while others might benefit from a more formal approach.

Analyzing Variation Performance Against Control

Sometimes you’ll read about a successful idea and copy it, thinking you’ll get excellent results. 

However, since every business is different, you’ll probably be disappointed. 

That’s why every variation should be created to support a specific hypothesis.

A hypothesis is like an educated guess about what changes might improve your website’s performance.

Then, you’ll go on to conduct an A/B test to validate your hypothesis – a digital experiment where you test different versions of your webpage to see which one performs better.

This is a systematic way of understanding how changes impact user behavior and achieving specific goals. 

It also ensures that decisions about your website are not based on guesswork but on real data.

Now, to understand the variation of your performance, you’ll need some sort of scorecard. In our case, they are vastly known as KPIs. 

When analyzing variations against the control, it’s essential to keep a close eye on these metrics:

  • Conversion Rate: a higher conversion rate indicates that a variation is more effective in getting users to do what you want them to do.
  • Click-Through Rate (CTR): a higher CTR suggests that a variation is more engaging and compelling for users.
  • Bounce Rate: a lower bounce rate indicates that users find the variation more relevant and engaging.
  • Average Session Duration: a longer session duration suggests that users are more interested and engaged with the content.

In your analysis, look for statistical significance to ensure that the observed differences in performance are not due to chance. 

You should also break down the data into different segments to understand how variations perform across different audience groups. This helps customize your strategies based on specific user behaviors.

If the variation outperforms the control, consider making it the new control and continue testing new variations. This iterative process helps in continuous improvement.

Best Practices for Creating Variations

Evidently, you wouldn’t be going through all the trouble of designing variations and running A/B tests unless you were on a hunt for unlocking valuable insights. 

To help you out, we’ve compiled a list of best practices in this process, so you’ll get not only accurate testing but also meaningful results that can guide future efforts.

Keep Changes Incremental

When embarking on A/B testing, it’s essential to keep changes incremental. 

This approach enables a focused examination of each modification’s impact on user behavior. 

Alter one element at a time, so you can pinpoint exactly what contributes to improved performance.

Know Your Audience

Understanding your audience provides a roadmap for successful variation creation. 

Tailoring changes based on the preferences and behaviors of specific audience segments ensures that modifications resonate with their expectations. This nuanced understanding forms the bedrock of effective A/B testing.

Set Clear Goals

Defining clear goals helps you chart a course for your A/B test. 

Whether your objective is to increase clicks, sign-ups, or purchases, having specific and measurable goals provides a direction for creating variations that align with overarching business objectives.

Embracing these best practices establishes a robust foundation for crafting variations that resonate with your audience and drive continuous improvement in A/B testing strategies. 

The Importance of a Hypothesis-Driven Approach 

Remember us mentioning how the variation comes to support your hypothesis? 

Think of a hypothesis as your guiding star, steering your tests towards purposeful and impactful outcomes.

By laying out a concise and testable statement about your expected outcomes, a hypothesis ensures that your testing endeavors are purpose-driven, avoiding random changes that may lack strategic intent.

Moreover, a hypothesis is not merely a prediction; it’s a key tool for learning and iteration. 

Whether or not a test is successful, the insights gained contribute to an iterative process of continuous improvement. 

Without a structured hypothesis-driven approach, there’s a risk of investing time and effort in experiments that do not significantly contribute to your understanding or optimization objectives. 

It’s like going on a road trip without a clear map – you may drive, but you won’t know where you’ll end up.

Continuous Learning Through Iterative Testing

The worst thing you can do for variations is to treat them as static entities. 

This means neglecting continuous learning and missing out on opportunities for improvement. 

It’s not about making a change and moving on but rather about refining and optimizing based on the feedback loop provided by your audience. For variation versions, this means acknowledging that they are not set in stone; they are a starting point for improvement.

Schedule regular analysis sessions to review performance metrics, user feedback, and emerging patterns that can guide your next set of iterations. 

Let data be your compass, basing your iterations on insights gleaned from the data collected during A/B tests.

Remember to encourage input from different team members, including designers, developers, and marketers. 

Their diverse perspectives contribute to well-rounded ideas that address various aspects of the user experience.

Overcoming Common Challenges

Like in any other expedition, challenges are inevitable when setting out to create your variations.

Here are some common block you might stumble upon, and ideas to overcome them effectively.

Insufficient Data

One challenge often encountered is insufficient data. 

This occurs when there isn’t enough user data to draw meaningful conclusions from the test. In this case, you may be moving, but the direction is uncertain.

Patience and incremental adjustments are your allies. 

Rather than making sweeping changes, start with small, controlled variations. 

This allows you to gather data gradually, ensuring a solid foundation for decision-making. 

Additionally, extending the test duration can provide more opportunities for data collection, leading to better insights.

Confirmation Bias

Confirmation bias refers to the tendency to favor data that confirms pre-existing beliefs or hypotheses. This bias can lead to misinterpretations and flawed decision-making.

Counteracting it requires a conscious effort on your part to stay objective. 

Clearly define your hypothesis before the test begins, and stick to it. 

Regularly review and challenge your assumptions. 

You should also encourage a culture of openness within your team, where diverse perspectives are valued. This helps in avoiding the trap of selectively interpreting data to fit preconceived notions.

Inaccurate Measurement and Analysis

Evidently, accurate measurement and analysis are the cornerstones of meaningful A/B testing. 

However, inaccuracies can arise from flawed implementation or misinterpretation of results.

In this case, rigorous testing protocols and meticulous data analysis are your allies. 

Ensure that your A/B testing platform is correctly configured, and the test is executed consistently. 

Double-check the accuracy of your metrics and statistical significance calculations. Consider involving multiple team members in the analysis process to minimize the risk of oversights.

Case Study – Overlay Variation

During our work with clients such as Tempur, Decathlon, Max Mara, Leroy Merlin, and many more, we’ve designed variations for >50k experiments to test our hypotheses. 

Some were minimal – a CTA moved higher on the page, while others required an entire revamp of a webpage. 

One of the more subtle variations happened for AliveCor –  a pioneering brand at the forefront of digital health technology. 

We began the journey with the purpose of driving more sales on their website, while also nurturing and growing their email subscribers list. 

It’s not an uncommon challenge – most eComm companies are struggling on these two fronts. However, it does require a more diplomatic approach, as we didn’t want to hurt the UX in our quest. 

So we brainstormed and debated different means to achieve this purpose, stopping at one idea: a five-second overlay, displayed to users who weren’t subscribed, after landing on a specific page. 

The overlay offered a 10% discount on the next order, in exchange for subscribing to the newsletter.

Here’s how the experiment looked like: 

Since the experiment displayed a high chance to win, while generating significant growth in the Conversion Rate, we declared it winning. Here are the specific results: 

  • 16.74% increase in Conversion Rate
  • 12.98% increase in Revenue/user
  • 98.1% chance to win

In this case, the variation with the overlay proved highly effective in collecting leads – leads which could be nurtured into sales. 

While some people would say that a discount doesn’t necessarily increase sales, and even hurt the revenue, our experiment proved that users used the discount to purchase higher-value products.

Would you like it if the next Case Study were about your results?

Maybe you don’t have the know-how, time, or bandwidth to research, then design A/B tests of your own. But you desperately need to improve your website and create better customer experiences. In this situation, we’ve got your back!

Our talented Managed Services team can take over the whole CRO process – from audit to result – for you!

If you want less guessing and more results, we’re here for you. Check out our suite of services here:

Wrap Up

As you can see, the variation is a central element of the A/B testing process – at the core of which lies your desire to improve your rates. 

Whether you want to gain more subscribers, increase your revenue, or grow your customer base, it’s always best to test your data-based assumptions in a controlled environment. 

Truth be told, you’ll never know if something could work, unless you try it out. So, our advice to you is to embrace a curious mindset, and experiment with your website. 

You can use Omniconvert’s Explore to create your first variations, or give us a shout out and we can take it over for you. 

With Omniconvert by your side, it couldn’t be easier.