Making informed decisions on Auto-Optimizing or Formal A/B Testing
In the ever-evolving world of digital advertising, making informed decisions is crucial to the success of your campaigns. When it comes to evaluating performance between various creatives, advertisers usually decide between taking the results from the ad platform’s auto-optimization feature vs conducting a formal split test.
Both approaches have their merits and can help you achieve your goals, but choosing the right one depends on various factors. In this blog post, we’ll explore the pros and cons of each method to help you make an informed decision.
But first, let’s discuss why it’s important to test, even with a limited budget. Balancing short-term goals with long-term success can be challenging, especially when there is pressure to deliver immediate results. Yet testing must remain a priority, despite budget constraints:
1. Changes in the industry are happening rapidly. Testing new concepts and strategy will help you stay competitive
2. While the new strategies you are testing may not yield positive results, the learnings you gain from the experiment are still valuable. (At least now you know what doesn’t work!)
3. Testing is an iterative process and small changes can lead to significant improvements over time
In other words, testing is a vital component of growth and should be viewed as an investment in the future.
Formal A/B Split Test
What is it:
A formal A/B Split test requires setting up two separate groups, Cell A and Cell B, each cell should be identical except for the one factor you are looking to test (i.e image, copy, CTA). All other factors, including budget allocation, remain constant. The winner is determined based on your main Key Performance Indicator (KPI), such as Return on Ad Spend (ROAS).
Why do it:
The advantage of a formal A/B Split Test is that it provides definitive results. It reveals the most straightforward and definitive answer to your creative optimization question.
But Formal A/B Split tests aren’t necessarily the right option for all situations due to setup complexity and performance risk. Running a formal A/B split test requires more effort to set up compared to auto-optimization. It involves meticulous planning and execution. Additionally, there is a greater performance risk associated with split testing since budgets need to be equal on both sides, yet one version performs significantly worse but still requires a budget allocation.
Auto-optimizing between Creatives
What is it:
Auto-optimizing is the method that helps manage your ads more efficiently and effectively by using the ad platform’s machine learning system to find high-performing combinations of your creative assets.
Why do it:
The advantage of Auto-optimizing between creatives is that the implementation is easy and there is a lower risk of negatively impacting performance. Auto-optimization is simple because all creatives will remain in the same ad set which eliminates the need for extensive setup. Also, with auto-optimization, the platform’s algorithm efficiently allocates the budget, reducing the risk of wasteful spending.
There are some downsides however in this approach in that it can provide ambiguous insights which makes determining a clear winner difficult. The algorithm’s budget allocation becomes the primary factor in determining a winner, which might not always align with your primary KPI. For instance, a creative that was allocated the most budget may have a lower ROAS than others. But since the ad platform has decided to push more budget towards that creative, you will have to trust that the algorithm has made the correct decision. Conflicting metrics can arise when using auto-optimization, making it challenging to identify a clear winner based on your KPI.
Conclusion:
In many cases, starting with auto-optimization and then transitioning to a formal split test if results are ambiguous is a reasonable approach. After all, testing is an iterative and continuous process which is very much an integral part of growth, therefore it is necessary to keep up with the latest industry innovations.
If you are looking to unlock growth and grow your business, we can help! Let’s chat
About AdParlor
AdParlor, an agency established in 2008 and now a division of Fluent (NASDAQ: FLNT), is a leading provider of data-driven digital media strategy and execution that unlocks explosive growth for world-class and disruptive brands. Through our GrowthFuel framework we offer proprietary performance marketing solutions that have scaled our clients’ programs profitably, with our clients’ success being our sole agenda. Our custom technology solves problems related to creative insights, creative production, real time cross-platform reporting and more. Brands choose AdParlor because our unmatched expertise in ecommerce digital media delivers impactful full-funnel performance campaigns for profitable growth.
(Content generated using ChatGPT using source material from AdParlor staff contributions with the featured image using a base generated by DALL-E & Canva. Showcasing the power of Generative AI)