Introduction
Its with good reason that the A/B test is a well known, widespread, generally accepted, and trusted tool in the digital world. The “A/B split” is used to test changes in content (layout, color, look & ads, etc) or creative variations to assess impact on the publisher or advertiser’s KPIs.
Yet, today’s programmatic publishers struggle to properly quantify the impact of many yield decisions and measure the true incremental benefit to their bottom line (e.g.: changes in floor pricing, adding new partners to a header-bidding stack, changing a PMP from open to private, etc.).
Publishers would do well to remember the reliable A/B test to track and monitor today’s modern programmatic stack. Measuring the impact of yield decisions shows similarities with assessing the impact of content modifications – so publishers might already have at their disposal a strong tool they know well that can be used to properly and accurately measure the incremental value of many of their monetization decisions.
Below, you’ll learn about how and why the A/B test should be considered as one of programmatic standard testing frameworks because of its relative ease of use and flexibility to be purposed for content, yield, and operational testing and measurement.
The Benefits of A/B Testing
Other test frameworks exist such as relying on experts, building statistical and/or prediction models, surveys, “before vs after” tests, etc. However, the A/B test framework has several benefits:
- Ease of Analysis: due to the simplicity of A/B testing its easier to dive in and analyze real, factual results quickly. A/B tests generally clearly determine a winner and a loser based on the straightforward metrics that you set out to test.
- Ease of test design. Split tests don’t require a Data Science degree to design or monitor. Someone with average technical skills can decide how many variables to test (another advantage of the A/B test is that you can test several variations at the same time!) then split the available traffic among them.
- Useful in Low-Data-Rate Testing: Generally, more data is better when performing an analysis. The good news is that this is not necessarily the case with an A/B test. With A/B testing, large volumes are not required to see which versions in your test are providing the optimal results.
- Reduced Risks: Like some other frameworks, A/B testing can help you measure the impact of change by examining behaviors and results before committing to major strategic decisions. This framework helps avoid unnecessary risks by allowing you to understand ramifications of a decision before you fully commit.
- Flexibility of Use: The A/B test framework can be applied in an infinite number of ways beyond things like forms, images, and content. Just about anything can be measured in an A/B test. Get your head around what you want to know, then design a way to test it.