The idea of “always be testing” is a popular one in paid media marketing, but it’s only truly beneficial when your tests are designed to make your campaigns more successful. Simply experimenting without a clear purpose is unlikely to yield positive results. It’s crucial to approach A/B testing with a well-thought-out plan and a strong hypothesis. This ensures that when you see positive changes, you understand the reasons behind them and can replicate and build upon that success.

This article will guide you through some of the most effective PPC A/B testing examples and provide tips to help you design impactful tests for your own campaigns.

Table of contents

• A/B testing hypotheses examples
• A/B testing examples for any business
• How to measure success for any A/B testing example Not sure what aspects of your PPC accounts need A/B testing? Our free Google Ads Grader can quickly identify optimization opportunities!

PPC A/B testing hypotheses examples (+tips)

Before diving into specific A/B testing examples, it’s crucial to emphasize the significance of a strong hypothesis. It’s not enough to simply state that you believe a new ad will perform better. You need to articulate what you aim to achieve with the test. A well-defined hypothesis, incorporating specific details, generally leads to more effective testing and actionable results.

Let’s illustrate this with an example. A typical A/B testing hypothesis might be: “I want to experiment with automated bidding to determine if it’s more effective.”

While this might seem like a valid test, the term “work better” lacks specificity. A strong hypothesis should outline the desired outcome in detail. Consider it a statement directed at your superiors who need a clear understanding of your objectives without delving into the daily intricacies of your work.

A more effective version of the hypothesis would be: Hypothesis: “We anticipate that automated bidding will result in lower CPAs for our primary conversion action.”

To get you started, here are a few A/B test hypotheses examples for potential experiments:

• “We believe that incorporating cost comparisons in our ad copy will differentiate us from competitors and enhance our visibility.”
• “We hypothesize that expanding our targeting to a new state will increase our market share at a cost comparable to our current geotargeted locations.”
• “We predict that a landing page enriched with additional relevant content will result in more engaged prospects and a higher conversion rate.” For additional guidance on hypothesis development, consider exploring the use of the 10% or 10x Philosophy? With a clear hypothesis in place, we can now move on to the practical steps of implementing the test. Our user-friendly growth strategy template makes it easy to craft a winning business growth strategy.

3 A/B testing examples every advertiser should try

Paid media platforms like Google Ads and Microsoft Ads offer various methods to test your hypotheses. Some platforms even provide dedicated A/B testing tools.

While there are no inherently incorrect approaches to testing a hypothesis, it’s important to consider the advantages and disadvantages of different PPC A/B testing examples.

1. On/Off, sequential testing

This method is arguably the most straightforward. You begin by collecting data from your current setup. Next, implement the changes aligned with your hypothesis and run the campaign for a predetermined period. Finally, compare the results of the two periods.

For instance, you could analyze four weeks of data with your existing ad copy. Then, pause those variants and launch new copy focused on cost-related messaging for another four weeks, followed by a comparative analysis.

This testing approach is simple to execute and only requires monitoring your campaign for significant performance fluctuations.

However, a notable drawback is the lack of simultaneous comparison between the variants. Factors like seasonality, budget constraints, external events (news stories), or other campaign adjustments during the testing period can influence the results, making it difficult to isolate the impact of your tested variable.

While not perfect, sequential testing can still provide valuable insights.

2. Geolocation testing

In this method, you maintain your existing campaign structure but create a separate experimental variant targeting a different location. This could involve expanding to a new market or testing within a subset of your current target area (e.g., testing changes only in a few states while your campaign targets the entire United States).

To ensure accurate results, it’s crucial to maintain exclusivity between your control and experiment groups, preventing overlap. This can be achieved by setting up new campaigns and excluding the experimental locations from your control campaign.

Unlike sequential testing, geolocation testing allows you to run variants concurrently, enabling a direct comparison of results. Any external factors influencing performance should impact both locations equally.

However, a significant limitation is the inherent dissimilarity between regions. It’s challenging to determine why a cost-focused message might resonate more in one state compared to another or why automated bidding yields better results in one time zone over another.

3. A/B split testing

Split testing is often considered the most effective A/B testing method as it mitigates some of the drawbacks encountered in sequential and geolocation testing. However, true A/B testing can also be the most challenging to implement.

Platforms like Google Ads and Meta Ads no longer distribute impressions evenly among variables. Instead, they employ AI-powered machine learning algorithms that tend to favor specific ad variants based on campaign objectives. This also applies to bid strategies. When testing manual versus automated bidding or different CPA targets, the campaigns are unlikely to enter the auction on equal footing, leading to an unbalanced test.

This is where features like experiments in Google Ads and split testing in Facebook Ads prove beneficial.

These tools allow you to create tests that focus on single or multiple variables while ensuring fairness in auction participation.

For a more in-depth understanding of these tools, refer to these videos that provide comprehensive walkthroughs of Google Ads Experiments and Facebook Ads A/B testing.

Measuring success across different PPC A/B testing examples

Having covered different testing approaches, let’s delve into the specific PPC metrics used to gauge success. Simply aiming for “better performance” isn’t a quantifiable goal.

Begin by identifying your primary Key Performance Indicator (KPI). Is it cost per lead, conversion rate, click-through rate, or impression share? The chosen KPI should align with your hypothesis and the selected A/B testing method. It will serve as the primary indicator of your test’s success or failure. However, it’s important to consider other relevant metrics as well.

Similar to the testing functionality, there are three common approaches to measuring success. For instance, if your objective is to improve cost per lead, consider these success metrics:

• Target performance: The test is deemed successful if the experiment achieves a $60 CPA.
• Percentage improvement: Success is determined if the experiment achieves a 10% reduction in CPA compared to the control group.
• Statistical significance: The test is considered successful if the experiment demonstrates an 80% confidence level of outperforming the control group.

All three methods are valid and should be chosen based on your specific objectives. Evaluate the performance of your key PPC metrics against industry benchmarks using our latest search ad benchmarks and new Facebook ads benchmarks!

Set PPC A/B testing limitations and dealbreakers

While your primary focus might be on optimizing cost per lead, it’s crucial to acknowledge that other metrics are likely to fluctuate as well. You need to determine acceptable levels of change for these secondary metrics.

For example, you might be comfortable with a 20% decrease in click-through rate if it leads to a profitable cost per lead. Similarly, a slight increase in cost per click might be acceptable if revenue remains stable. However, it’s essential to establish clear boundaries.

Here’s a practical A/B testing example: A client aimed to reduce the cost per lead for branded terms by 20% but was unwilling to compromise impression share below 80%. Despite the challenge, an A/B test was implemented using target CPA bidding. However, achieving the desired CPA reduction resulted in Google displaying ads for only 60% of potential impressions, violating the client’s dealbreaker. Consequently, the test was discontinued, and alternative strategies were explored.

When setting up experiments in Google Ads, you’re prompted to define two key metrics and your expected outcomes. Apply this same rigor to your own testing process. Ask yourself: “Are there any potential metric-related dealbreakers that would necessitate stopping the test prematurely?”

Consider your A/B testing timeline

In some cases, A/B tests might conclude without a clear winner. However, it’s crucial to establish a defined testing duration to avoid stagnation and allow for testing other elements.

Conversely, A/B tests require sufficient time to gather enough data for informed decision-making. While very large accounts might be able to draw conclusions within a week, a minimum of two weeks is generally recommended, with a maximum duration of two months. Extending beyond two months can introduce complexities and potentially invalidate the test results.

Therefore, regardless of the chosen A/B testing method, ensure that your test duration allows for gathering sufficient data to confidently validate or refute your hypothesis within a two-month timeframe.

Putting the right PPC A/B testing examples to work for your business

A/B testing is an invaluable tool for marketers and should be incorporated into ad account management strategies. Approach each test with a clear plan, including a well-defined hypothesis, a structured plan of action, and predetermined dealbreakers. This sets the foundation for success, regardless of the outcome. For further A/B testing examples and tailored ideas, explore how our solutions can optimize your testing efforts! Here’s a recap of the top three A/B testing methods to consider for your PPC accounts:

1. On/off, sequential testing
2. Geolocation testing
3. A/B split testing