How to Split Test Affiliate Campaigns Effectively

Affiliate marketing is a powerful tool for small businesses, allowing you to leverage the reach of affiliate partners to promote your products. However, to maximize the effectiveness of your affiliate campaigns, it's crucial to use split testing. Why? Imagine you are trying to convince someone to sign up for a membership at your dance studio, you show them videos during dance training, discuss why they should choose your dance studio, and even offer a free trial. But what if they aren’t convinced?

Split testing in affiliate marketing is like testing out different dance moves. In dancing, the test will tell you which move to go with your chosen song, in affiliate marketing, the test will show you which one gets the most sign-ups from your chosen audience. You test different elements, see what works best, and then evaluate which is suitable for your audience that will make you reach your goal. 

Split testing, also known as A/B testing, involves comparing two campaign element versions to see which performs better. For instance, you might create two different banner ads for your product and use each version equally. By tracking which version generates more clicks or sales, you can determine which ad is more effective. Here is an overview of the split testing process:

1. Start by deciding what elements of your campaign to test. This could be the design of promotional materials, the wording of a call-to-action, or the type of content used to promote your product.
2. Next, create two versions of the element you're testing. Distribute both versions equally among the audience. Then, using your optimization tool, monitor which version performs better in terms of driving traffic, generating clicks, or making sales.
3. Once you've gathered enough data, analyze the results. If one version clearly outperforms the other, make it the standard for your affiliate campaign. Remember, optimization is an ongoing process—continually test new variations to keep improving your campaign's performance.

In the next part of the article let’s discuss those overviews in their detailed form.

Setting Clear Objectives

Start by clearly defining what you want to achieve with your split test. Objectives can range from 

• increasing email open rates, 
• improving click-through rates on a webpage, 
• to boost campaigns’ conversion rates. 

Having a clear objective helps in designing the test and measuring success. Now you have identified your objectives, choose KPIs that directly reflect these goals. Remember, when selecting the most relevant and reliable KPIs for your split test, there is no one-size-fits-all answer, therefore, ensure that your chosen KPIs are relevant, measurable, and sensitive to changes in your variations. Balance quantitative and qualitative metrics to capture both numerical performance and user experience.

Choosing Elements to Test

There are several elements that you should consider testing that will lead to data-driven decisions to enhance your affiliate business, such as:

Call-To-Action (CTA)

CTA (Call to Action) turns website visitors into paying customers. Persuasive whispers like “Click here!” or audacious shouts like “Buy now!” are examples of it. But creating the ideal call to action is a skill, not a science. Split testing can help with that. Marketers can test text, design, urgency, and personalization elements. The power of a well-placed CTA could unlock affiliate marketing success!

Ad Placements/Types/Creatives

We advise experimenting with different ad placements, types, and creatives. The purpose is to identify the most effective combinations. This involves testing banner ads versus in-text ads and different ad sizes. Other creative elements are images, colors, and messaging.

Devices

Selling things online? Great! But people use all sorts of devices these days, from big computers to tiny phones. What looks amazing on one might be a mess on another! Imagine a cool landing page that gets lots of clicks on desktops, but on phones, it’s all jumbled up and hard to use. People get frustrated and leave, and you miss out on sales.

That’s where split testing comes in. It’s like a special tool that lets you see how your campaigns look and work on different devices. You can try out different versions and see which one people like best on each device. Happy users on any device can become your customers, and A/B testing is your key to making that happen!

Content Topics

Imagine you’re a chef tempting people with your dishes. Split testing helps you figure out which “content recipes” work best. You can test different topics, headlines, writing styles, and even images. Then, you see what grabs attention and makes people want to learn more.

Maybe a funny headline gets more clicks than a serious one. Perhaps in-depth articles convert better than quick tips. By testing different content flavors, you find the sweet spot that turns website visitors into happy customers.

Video & Images

Visual content can be impactful in affiliate marketing, you can split tests different video and image formats, lengths, and styles and then, determine which drives higher engagement and conversions.

Email Subject Lines

If using email marketing, you test different subject lines to optimize open rates. You will check various lengths, tones, and personalization techniques. Thus, you can find which leads to higher email engagement.

Product Descriptions

A compelling product description can significantly influence buying decisions. Let’s check different approaches to product descriptions. For example, highlight essential features, use storytelling techniques, or emphasize benefits. Split testing will provide the most persuasive and effective strategies.

Landing Pages

A well-designed landing page can make a significant difference in conversion rates. In Split testing, you will analyze layouts, headlines, colors, and calls to action on your pages.

Creating Hypotheses

Often we imagine that doing split tests to improve your ecommerce site’s performance means quickly changing the color of the “add to cart” button will lead to a drastic increase in your conversion rate. However, split testing is not always so simple.

Unfortunately, implementing random changes to your pages won’t always significantly improve your results – there should be a reason behind your web experiments. This brings us to the question: how do you know which elements to experiment with and how can you create an effective AB test hypothesis?

Instead of searching for a quick “DIY” solution, it’s often more valuable in the long term to take a step back and do two things:

Identify the real problem 

What is the source of your poor performance? Is it a high bounce rate on your order confirmation page, too many single-page sessions,  a low-performing checkout CTA or something more complex?

Establish a hypothesis 

This could show the root of the problem. For example, a great hypothesis for split testing could be: “Our customers do not immediately understand the characteristics of our products when they read the pages on our e-commerce site. Making the information more visible will increase the clicks on the “add-to-cart” button.”

Initially, making a split test hypothesis may seem too simple. At the start, you mainly focus on one change and the effect it produces. You should always respect the following format: If I change this, it will cause that effect. For example:

Changing (the element being tested) from ___________ to ___________ will increase/decrease (the defined measurement).

Here are two examples of hypothesis phrased according to the formula explained above and that can apply to e-commerce:

1. Changing your CTA from “BUY YOUR TICKETS NOW” to “TICKETS ARE SELLING FAST – ONLY 50 LEFT!” will improve your sales on our e-commerce site.
2. Shortening the sign-up form by deleting optional fields such as phone and mailing address will increase the number of contacts collected.

Remember, the impact of the change you want to bring must always be measurable in quantifiable terms (conversion rate, bounce rate, abandonment rate, etc.).

Designing Your Split Test

Now that you have your hypotheses ready it would be easier to design your split test. When designing your split test there are multiple types you can choose from but the very common and easier to use are the A/B testing and multivariate testing. Let’s talk about them,

A/B Testing

When you perform an A/B test you create two different versions of something — like a landing page, call to action (CTA), or web page — to see which performs better.

For instance, let’s consider a web page that’s selling picture frames. A web developer may want to conduct a test between two headlines: “Buy Picture Frames” or “Buy Cheap Picture Frames.” Two landing pages are developed, and the results are tracked. The question to be answered is whether the word “cheap” makes any difference when it comes to conversions.

After an appropriate number of people go through the split test, the developer compares the results of the tests against one another. The developer then decides which headline to use based on the results.

The image below is an example of an A/B test.

Note: Use A/B testing when you want to test two specific designs against each other, and you want meaningful results fast. It is also the correct method to choose if you don’t have a ton of traffic to your site as you’re only testing two variables, so significant data is not needed.

Multivariate Testing

While an A/B test might show audiences two different website formats or designs, multivariate might show differences such as different wording or fonts on a call-to-action to see which button gets clicked more.

The image below is an example of a multivariate test.

In image, notice how each variation plays with placement, color, style, and format. Unlike A/B testing, where the two variations are usually noticeably different, the differences in variables in a multivariate test may be more subtle. In short, multivariate testing uses the basic elements of A/B testing, but it tests multiple parts of your page/ads at one time.

Going back to our last example, that means you could test the headline of a page along with the layout of the products you offer. That makes multivariate testing great for site overhauls and other major changes online. You can use it to see which newly-made pages get the best response from your audience without testing single elements at a time.

Note: Only use a multivariate test if you have a significant amount of website traffic. That way, you can truly determine which components of your website yield the best results.

Segmenting Your Audience

Audience segmentation in split testing, also known as split test segmentation, is a strategic practice that involves dividing an audience into smaller groups to target specific segments more precisely. This method acknowledges the diversity of an audience by recognizing and catering to the unique characteristics and preferences of different user groups. Audiences are often segmented based on demographics, interests, or user behavior. The test results show which groups respond best to your offers and messaging.

One of the most basic segmentation divisions is between new and returning visitors. It’s a good starting point, but those groups can also be broken down even further. For example, suppose you have an online store and need to test a new layout. In that case, segmentation allows you to test it specifically on returning customers or first-time visitors instead of testing a new layout on all visitors. This focused approach ensures that your collected feedback and data are relevant to each group.

The power of segmentation lies in its ability to deliver more accurate results. It enables you to understand how different groups interact with your website, providing insights that are not just generalized but deeply relevant to each segment.

This targeted testing leads to more effective optimizations, as you’re not guessing what works for your entire audience but knowing what resonates with each specific group.

Setting Up Your Split Test

Step-by-step guide to setting up a split test at OnClickA

Analyzing Results

Once you’re satisfied that your test has gathered enough data, reached the required statistical significance level, and run long enough, it’s time to begin the analysis process.  The variation you were testing will either win or lose, or the results will be inconclusive. Regardless of the outcome, you should focus on the learnings as you will need those to inform your next tests. One thing that you should know is that, in some instances, losing tests will give you more insights than those tests where the original underperforms.

Here are some steps to follow when analyzing and interpreting split test results:

Check the validity of your test

Before you look at the numbers, you need to make sure that your test was set up correctly and ran long enough to reach statistical significance. Statistical significance means that the difference between the versions is not due to random chance, but to a real effect. You also need to check for any external factors that might have influenced your test, such as seasonality, holidays, promotions, or technical issues.

Compare the key metrics of your test

Once you have a valid test, you can compare the key metrics of your test, such as conversion rate, average order value, revenue per visitor, bounce rate, etc. You can use visualizations such as charts or tables to display the results and highlight the differences.

Analyze the segments and subgroups of your test

Sometimes, the overall results of your test might not tell the whole story. You might want to dig deeper and analyze the results by different segments and subgroups of your visitors, such as device type, traffic source, location, behavior, etc. This can help you identify which segments performed better or worse with each version, and why. However, you should be careful not to over-segment your data and lose statistical power, or to over-interpret the results and fall into the trap of false positives or spurious correlations.

Interpret the results and draw conclusions

After you have analyzed the data, you need to interpret the results and draw conclusions from your test. You should try to explain why one version performed better or worse than the other, and what are the implications for your website and business. For instance, you might decide to implement the winning version, run another test, or make further changes to your website or landing page.

One of the common mistakes in data analysis is relying on surface-level insights without delving deeper into the data. This can, evidently, lead to misguided decisions. Moreover, this superficial approach can also overlook underlying trends and anomalies that are critical for informed decision-making.

Implementing Findings

After conducting split tests and gathering data, the next crucial step is implementing the findings to optimize your campaigns. 

• Begin by thoroughly analyzing the data to verify the results and ensure the test ran long enough to be statistically significant. 
• Compare key performance indicators (KPIs) such as conversion rates, click-through rates, and average order values to identify the winning variations. 
• Understand why these variations performed better by delving into user feedback and behavior. 
• Prioritize tests that have the most significant impact on your KPIs and segment your audience to customize your campaigns for different user groups. 
• Monitor the performance of these changes to ensure they deliver expected results and be prepared to make further adjustments if necessary. 
• Communicate the outcomes and insights from split tests with your team and stakeholders to align strategies. 
• Lastly, learn from failed tests to refine future tests and balance data-driven decisions with intuition and creativity.

Conclusion

One of the primary benefits of split testing is the ability to base decisions on concrete evidence rather than assumptions or personal preferences, whether you're a seasoned marketer or a beginner, A/B testing is an essential strategy to take your digital marketing efforts to a new level. By conducting controlled experiments and analyzing user behavior data, businesses can gather empirical insights into the impact of different variations on key metrics. This data-driven approach eliminates subjective biases and ensures that changes made to digital assets are supported by measurable results.

Ready to take your affiliate marketing to the next level? Join OnClickA, where you can leverage powerful tools and expert guidance to optimize your campaigns through effective split testing. Collaborate with a community of like-minded marketers, gain valuable insights, and stay ahead in the competitive landscape. Don't miss the opportunity to enhance your marketing strategies and drive greater success. Join OnClickA today and start maximizing your affiliate marketing potential!

Join

Q&A

Q: What is split testing in affiliate marketing?

A: Split testing, also known as A/B testing, involves comparing two versions of a campaign element to determine which performs better. For instance, you might test different CTAs, ad placements, or landing page designs to see which one drives more traffic or conversions.

Q: Why is split testing important for affiliate campaigns?

A: Split testing helps you identify what resonates best with your audience, leading to more effective campaigns. It allows you to optimize elements based on data-driven insights, ultimately increasing your conversion rates and ROI.

Q: How do I set clear objectives for my split test?

A: Start by defining what you want to achieve, such as increasing email open rates, improving click-through rates, or boosting conversion rates. Choose KPIs that align with these goals to measure the success of your tests accurately.

Q: What elements should I consider testing in my affiliate campaigns?

A: Key elements to test include CTAs, ad placements, content topics, video and image formats, email subject lines, product descriptions, and landing page designs. Testing these elements can provide insights into what drives engagement and conversions.

Q: How do I create effective hypotheses for my split tests?

A: Formulate hypotheses by identifying a problem and predicting how a change might solve it. For example, "Changing the CTA from 'Buy Now' to 'Limited Time Offer' will increase conversion rates." Ensure your hypotheses are specific and measurable.

Q: What’s the difference between A/B testing and multivariate testing?

A: A/B testing compares two versions of a single element to see which performs better. Multivariate testing, on the other hand, tests multiple elements simultaneously to determine the best combination of changes. A/B testing is simpler and requires less traffic, while multivariate testing is suitable for more complex changes and larger audiences.

Q: How do I implement the findings from my split tests?

A: After analyzing the data, identify the winning variations and implement them across your campaigns. Document the changes and results, and continue testing new variations to keep optimizing your performance.

Q: What are best practices for making data-driven decisions?

A: Use reliable testing tools, maintain consistency in implementing changes, communicate results with your team, and balance data-driven insights with intuition. Regularly monitor post-implementation performance and be ready to make further adjustments if necessary.

Q: How can OnClickA help me optimize my affiliate campaigns?

A: OnClickA offers powerful tools and expert guidance for effective split testing and optimization. By joining OnClickA, you can collaborate with a community of marketers, gain valuable insights, and stay competitive in the affiliate marketing landscape. Join OnClickA today to maximize your marketing potential!