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What Is A/B/n Testing? 7 Reasons A/B/n Testing will boost your performance

Discover what A/B/n testing is, how it works, and why forward-thinking publishers are turning away from traditional A/B testing to boost Ad Revenue and UX with A/B/n Testing. Dive into the future of ad optimization today with insights from reports and industry experts!

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To achieve optimal ad revenue, UI, and UX, publishers have traditionally relied on A/B testing. However, the challenges with the conventional A/B testing approach have made it difficult for publishers to continue to rely on limited tools for testing, regarding speed, scope, and timing.

Ezioc claims that A/B testing ads can actually lower ad rates, this can be because most of the time publishers can only test two variants, limited to a specific source of revenue, and with a lot of other barriers including the fact that the testing process demands a lot of investment in time and resources, from the Programmatic team, Ad Ops, and specially, developers. 

We know that developers are key resources and every company should wisely use this valuable resource. For data-driven organizations A/B Testing process is a repetitive and continuous task, it never ends.
That's why Advertisers, agencies, and DSPs have proven that A/B/n testing is the best practice and crucial to elevating customers' experience, and user lifecycle while generating more revenue. Moving forward, innovative publishers are now A/B/n testing, but what does that mean?

The CEO/Founder of World History Encyclopedia, Jan van der Crabben, in an exclusive Interview with Assertive Yield, stated “We complement our in-house A/B testing, (which primarily focuses on optimizing page layouts) and subsequently, integrate this data into an A/B/n testing platform, merging it with its multivariate testing capabilities. This approach enhances our agility in the testing process, as we can now experiment with multiple elements and variables concurrently without the concern of muddling the test results” 

What is A/B/n Testing in Simple Terms?

A/B/n is an advanced method used to compare and analyze multiple variants of a website or application simultaneously. Unlike traditional A/B testing, which tests only two versions (A and B), and can lead to traffic loss or a drop in conversions should your testing not yield positive results.

A/B/n testing allows publishers to experiment with multiple variants. Each variant represents a different ad format, layout, placement, content, or even different vendors, countries, and more.

Moreover, there are only one or a few cutting-edge ad tech solutions tailored for publishers that not only facilitate A/B/n testing but also enhance the efficiency and scalability of programmatic operations, ad operations, revenue operations, and, notably, developer teams.

By enabling publishers' developer teams to establish a personalized code repository with diverse variables, this code can subsequently be utilized by any team to execute A/B/n tests, eliminating the necessity for developers during the testing process. 

This is particularly advantageous as developers are a finite and invaluable resource, rendering their involvement in repetitive tasks counterproductive. That way, the dev team will have one less task to worry about. No dev is needed to create, deploy, or revert.

So, publishers don’t have to stop at just A/B testing, they’re taking it a step further, with A/B/n tests, performed with high scalability, and agility, both holistically and autonomically.

How does A/B/n Testing Work?

In the past publishers were merely performing A/B tests to improve their product performance, by creating A/B tests in Google Optimize. Although this product will cease to exist after September 30, 2023, it’s advisable you explore Google Optimize Alternatives for Publishers: A/B Testing and Monetization Solutions

Today, new technologies allow them to perform multiple tests simultaneously in a day, and analyze results in real-time.

Since A/B/n testing operates on the principle of controlled experimentation, publishers create multiple variants of their setup, and visitors are randomly assigned to one of the variants. Real-time data is collected and analyzed in minutes to determine which variant performs the best based on specific metrics.

The winning variant can then be deployed either to the entire audience or to a small % of traffic to test if the new version is performing better or worse than the current version of traffic.

If it’s performing worse, you can quickly roll back to the current version, but if it’s performing better, then you can deploy to 100% across the entire set-up leading to improved/uninterrupted user experience and higher ad revenue. 

7 Reasons Why You Should A/B/n Test

For publishers looking to get more out of their adstack, A/B/n testing is an innovative way of maximizing their revenue and performance, by improving the setup of their adstack according to country, campaign performance, key metrics performance, and user experience. 

This is because, once a website has accumulated sufficient traffic to execute the test, the data gathered from each variation is scrutinized. That way, this analysis not only identifies the most effective design but also has the potential to uncover which elements exert the most significant positive or negative influence on a visitor's interaction. 

According to “Digital Experimentation and Startup Performance: Evidence from A/B Testing” a report by Harvard Business School, despite A/B testing being associated with a persistent 5-20% increase in page visits after adoption, roughly only 8% of startups use an A/B testing technology. Why is this? 

This hesitation can be attributed to quite a lot of cons associated with traditional A/B testing, from the cost of testing many features separately and burdening the dev team with performing repetitive tests, to the delay in test results, loss of traffic due to negative UX, muddling the test results, and more.
Due to these shortcomings, it’s no wonder that publishers are turning to cost-effective A/B/n testing.

“What's particularly impressive about  A/B/n testing functionality is its ability to facilitate testing, whether we're making minor tweaks or substantial changes. Moreover, it allows us to run multiple variations simultaneously, extending beyond a 2 version comparison. This enables us to easily track the performance of each version in terms of generating RPMs” 

an ad banner of world history encyclopedia customer success journey with Assertive Yield

What are the Pros of A/B/n Testing?

  • An All-in-one Single Source of Truth: The right A/B/n tool unifies and empowers all publishers’ teams with more than 11 solutions in one platform, such as IVT, Web Analytics, Tag Manager, Content Analytics, and more in real-time.

    This way your team has the agility and autonomy to create, measure, and manage multivariate tests all in one place. Compared to using 10+ various solutions to get all these analyses with the traditional A/B test, it’s more cost-effective and seamless to use the right A/B/n testing tool that consolidates all of them in one.

  • Faster & Optimized Tests: With A/B/n testing, there’s no need to run a lengthy series of sequential A/B tests. You can test them all at once, giving you the opportunity to get faster holistic results.

  • Reduces Risk: A/B/n testing with the right tool allows you to test various idea versions on a small % of your webpage and get the traffic results instantly before fully deploying or reverting. 

  • Data-Driven Decision Making: A/B/n testing allows you to make decisions based on concrete data rather than gut feelings or assumptions. By comparing different versions of a webpage or feature, you can objectively identify what works and what doesn't.

  • Optimize User Experience: When publishers employ the right tool to A/B/n test, it can help them improve the user experience by experimenting with different SSP partners, resellers, ad formats, ad placement, countries, layouts, and more.

  • Minimal Dev Resources: A/B/n Testing with the proper tool will give other publisher teams the full autonomy to run tests using preset custom code without necessarily burdening the dev team to run all tests.

  • Reduce Bounce Rates: A/B/n testing can help reduce bounce rates by refining landing pages and ensuring they meet user expectations. A better user experience can encourage visitors to stay longer and explore further and more.

    a word-cloud of the 11 tools assertive yield consolidates into its yield manager product

What are the Cons of A/B/n Testing?

Multivariate testing is good for large-scale projects and thus often used by large-scale publishers as it requires a higher amount of traffic to achieve statistical significance than A/B tests simply because there are more pages to test.

However, it's no science rocket to deduce that the pros of A/B/n testing far outweigh the cons, so you should read more on Boosting RPM: The Challenges Publishers need to overcome in A/B testing to recover from ad spend slowdown in 2023?

Check out the associated AY solution

Written by

Angela A. Oladipupo

Content Analyst

Angela is a wordsmith who skillfully transforms complex concepts into engaging content, making significant contributions to the advertising industry. Leveraging her wealth of expertise as a content writer, she crafts compelling narratives that seamlessly bridge technology and communication, whether with her pen or keyboard.

Frequently asked questions

What is ABN testing?

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A/B/n testing is a methodology where multiple variants of a webpage are tested simultaneously, allowing publishers to experiment with various ad formats, layouts, and placements to identify the most effective combination.

How does A/B/n testing work?

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In A/B/n testing, publishers create different versions of a webpage element, with visitors randomly assigned to these variants. Performance data is collected in real-time to determine the most effective version based on specific goals.

How is A/B/n Testing Different from Multivariate Testing?

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Unlike multivariate testing, which examines the interactions between multiple variables, A/B/n testing focuses on comparing multiple variants of a single element or a few elements, making it simpler and more suitable for less complex tests.

Benefits of A/B/n Testing

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Benefits include the ability to test multiple variants simultaneously, faster optimization, reduced risk by trialing changes on a small traffic percentage, data-driven decision-making, improved user experience, minimal developer resource requirement, and potential reduction in bounce rates.

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