What are the best ways to create variations in an A/B test?

- Ensure a clear understanding of what is being tested. - Understand how the test will be measured, including which KPIs and results will be analyzed. - Limit the A/B test to a maximum of two questions to simplify analysis. - Create cell A and cell B for analysis with the correct variations needed according to the hypothesis, while keeping other elements equal in both cells.

Why the heck are you even split testing? What will you gain? How do you know what success even looks like? These are the questions that the novice fails to answer adequately. We just think about changing a button color or a headline, generally. But why wouldn't you prioritize your optimizations? Why wouldn't you take a moment to look at your entire funnel with a broad view and focus your efforts on the ones that'll make the biggest difference? Beyond establishing an adequate hypothesis, know WHAT you should be testing first out of all the possible variations you could do. Then, test only one variable at a time.

Tackle nuances in copy, visuals, and CTAs for meaningful insights. Avoid shortcuts like mere color swaps; delve into user psychology. Incorporate customer feedback for authenticity. Crafting variations that reflect user preferences, rather than generic tweaks, ensures richer data and more conclusive results.

When defining your hypothesis, few things to keep in mind - 1. Identify the problem backed by real data. 2. Think about the best possible solution for the problem. 3. Identify metrics that will help you measure the solution. 4. Based on the above points, you define a clear measurable hypothesis. For example, if we change the CTA button text from "Sign up" to "Start free trial," It will increase the signup rate by 10%.

When setting up an A/B test, the significance of a well-defined hypothesis can't be overstated. It's not just about choosing what to test, but understanding why it matters. A great hypothesis goes beyond the surface; it delves into the psychology of your audience. For instance, changing a call-to-action button involves more than just the wording; it's about tapping into what motivates your audience to act. Is it a sense of urgency, a fear of missing out, or the allure of a free trial? This deeper understanding guides the creation of variations that are not just different, but strategically aligned with your audience's motivations.

In simpler terms, when you're trying to figure out what works best, like deciding the color of a button on a website, it's tempting to change lots of things at once – like the color, shape, and what the button says. But that's like mixing too many ingredients in a recipe without knowing which one makes it taste better. To avoid this mix-up, it's smarter to change just one thing at a time. So, if you're curious about the button's color, keep the shape and text the same and just switch up the color. This way, if more people click the button with the new color, you can confidently say, "Aha! It's the color that's making the difference!" It's like being a detective but with websites instead of crime scenes.

Focusing on one variable at a time is crucial for obtaining clear, actionable results. When you change multiple elements simultaneously, it's like trying to listen to several songs at once – you can't really tell which melody improves the experience. By isolating one variable, you turn your test into a precise experiment rather than a guesswork game. This method allows you to measure the impact of a single change accurately, be it the color of a button or the phrasing of a headline. It’s a bit like conducting a scientific experiment where controlling variables is key to understanding cause and effect.

Set clear success criteria for your test. How will you know if your A/B test was a success? Define your success criteria in advance so that you can objectively evaluate the results of your test. For example, if you are testing a new call-to-action button, you might define success as a 5% increase in click-through rate.

One small detail which seems obvious but can be just as easily overlooked is that time is also one of the factors in this respect. Sending out email A at one point in time with button x and then sending out email B with button y at a different point in time will also pollute your test.

Choosing one variable at a time in A/B testing is a fundamental principle known as isolation of variables. This approach is essential for accurately attributing any observed differences in outcomes between the control group (A) and the experimental group (B) to the specific change being tested

Variations should be distinct enough to elicit a measurable response, but not so drastic that they introduce unrelated variables. It’s akin to adjusting the spices in a recipe: too little and you won’t notice a difference, too much and you risk altering the dish entirely. When testing elements like button text, it’s essential to craft variations that embody different value propositions or emotional appeals. This enables you to gauge not just the effectiveness of a specific phrase, but also the underlying motivators for your audience.

Focus on variations that genuinely test your value proposition. For example, instead of just tweaking your call-to-action text, try contrasting two fundamentally different benefits of your product. This approach uncovers what truly resonates with your audience, leading to more impactful insights.

When designing variations, the changes made should be relevant, purposeful, and aligned with the goals of the experiment 1. Relevance to Hypothesis 2. User-Centric Approach 3. Focus on Key Elements 4. Avoid Trivial Changes 5. Take into account the broader context of your website, application, or campaign 6. Test variations incrementally, especially if you are unsure about the potential impact of a specific change

In A/B testing, maintaining brand consistency is key. Variations, like different images on a landing page or changes in copy, should align with your brand's established aesthetic and tone. For example, if testing landing page images, each should match your brand's color scheme and style, resonating with your product and identity. Similarly, any adjustments in copy should reflect your brand's voice. This approach ensures that A/B testing enhances user experience without causing confusion or diminishing trust. It's about optimizing within the framework of your brand's identity, reinforcing the recognition and trust you've built with your audience.

Testing variations before launching is crucial for several reasons, and it plays a pivotal role in ensuring the success and effectiveness of any changes you plan to implement.

Proper testing takes time. And my biggest advice to those that are split testing is knowing when to completely start over and SCRAP THE TEST ALL TOGETHER. If you launch a campaign and you're getting abysmal performance. Please, dear goodness, don't think changing your button color or CTA copy will all of a sudden change your life. If your landing page isn't getting ANY conversions, know when to start with a whole new design, set of copy, or try an entirely new offer. Use your common sense. A/B testing is for optimization; making gradual improvements over time. It's not a lifeline for poor campaigns.

A few things that we have applied in the outdoor advertising space: 1) Comparing two separate geographies/markets with OOH in only one of them allowing you to compare website visitation and/or footfall visitation. 2) Adding in our attribution stack where we geofence all of your OOH placements so that we can create a control group for those that we know were not exposed to your OOH ads (by tracking mobile devices that pass your OOH placements) & measuring website visitation/footfall visitation for those exposed to your ads vs those not exposed to your ads. 3) Geofencing your OOH ads & serving mobile ads to those mobile devices exposed to your ads & comparing the CTR for those exposed to your OOH ads vs your CTR for previous mobile campaigns.

One often overlooked aspect is the psychological impact of variations. It's not just what you change, but how it aligns with human psychology. For instance, a case study showed that simply changing the color of a call-to-action button from green to red increased clicks by 21%. This wasn't just a color change; it tapped into the psychological association of red with urgency and importance. Another insight is the power of word choice. A test revealed that replacing a single word in an email subject line led to a 46% higher open rate. This highlights how subtle language nuances can significantly impact user behavior.

When it comes to A/B testing there are 3 areas that get overlooked all the time, that I've seen dramatic results with: 1) Limit Distractions: Distracting prospects from the CTAs. Try removing rich video/imagery and see if your conversions go up. 2) Overcome Anxiety: Prospects have concerns in the back of their minds about things like privacy - which deters engagement. Filling out a form and providing an email is a BIG ask. So, ask yourself how you can address this anxiety by providing some social proof or information on how you'll treat their data. 3) Create Urgency: Lastly, find ways to create a bit of urgency. Maybe you only have room for 2 new clients. Think like a travel website - "only 2 rooms left" - what urgency levers do you have?

When A/B testing ads focus on testing variations in ad headlines, images and CTAs. Those are the most influential parts of the ad, changing body copy will have less of an influence.