How To Split Test For a Successful Facebook Ads Campaign
Have you ever had a hard decision in your business or life & wanted a clear answer? You may write up a list of pros & cons. Maybe you decide to get another pair of eyes to give you their opinion.
Well, in this blog, we discuss how to decide between 2 different yet similar variables. It’s called A/B testing.
What Is A/B Testing?
What is it & why do marketers use A/B testing to achieve exceptional results?
The idea of A/B tests is simple. It’s been used in data science ever since we coined the phrase Data Science.
Facebook A/B testing (or split testing) is the best way to test different campaign variables. You can test based on ad sets and ad levels to determine which of these variables generates better results (whether it’s more sales, leads, or an increase in website traffic).
How To Create Facebook A/B Test Ads
1. Choose Your Variable
You decide what variable you want to A/B test, which will be advertised to a pre-selected audience & given a daily budget. Then after a few days/weeks, you will gather enough data to determine a clear winner.
The range of variables you can split test on Meta are:
- Audiences – Location, Age Group, Interests, Gender, Language
- Placements – Facebook / Instagram / Audience Network / Messenger
- Age group
- Creative – Format, Copy, Content
- Bidding strategy
Once you determine which main variable you want to change, you will create multiple variations of that – which Facebook calls A/B testing. These will each receive different metrics such as Impressions, Clicks, CTR, CPC, and Conversions. Based on this data, you can see if variation A had higher clicks or conversions than B, C & D. However, you would only get a clear result if you also keep your other variables fixed.
For example: When testing new creatives, if you change both the content & copy of an ad. Then how would you know if it’s the copy or imagery that had the impact? If you were to keep the copy the same while only changing the content, however, then you could clearly define one creative as more successful than the other.
So always remember to keep everything else the same across ad sets/ads beside one variable when you’re looking to A/B test the ideal combinations of your new successful campaign.
Here are some examples of A/B testing variables…
Creative A/B Testing
If you’re split testing out creatives, copy or format at the ad level, you want the audience, placement & budget the same. This is easily done as you usually test each creative within one ad set.
Facebook has an innate feature called A/B testing, which helps ensure that your audiences will be evenly split and statistically comparable, while informal testing can lead to overlapping audiences.
It’s also worth noting that when testing creatives, sometimes every creative won’t work with every piece of copy. It’s good to go with your gut & create a personalised version of copywriting which is directly relevant to that specific piece of content.
A lot of the time, when it comes to determining the age, gender & location. It may be a better idea to look at data from Google Analytics or your website platform. This will give you access to more data & hence clearer results. However, if you don’t have a large amount of data as you’re a new company, split testing these elements can still be easily done.
Simply create several ad sets with different demographics while keeping the creative & campaign the same. Here’s an example of how you can set up a campaign A/B testing age.
- Ad Set 1: 18-25
- Ad Set 2: 25 – 35
- Ad Set 3: 35 – 45
- Ad Set 4: 55-65+
Once these audience demographics are clear, you want to keep them fixed as there are usually lots of people who fit those categories of Age, Gender & Location.
2. Select Campaign Type
The recommended time for split testing results is 5-7 days. However, depending on the type of campaign you are running (ABO or CBO), results may differ.
If you are running a split test on the ad set level (audiences, bidding strategy & placements), using the CBO feature allows for Facebook to automatically give more budget to the winning ad set without you deciding where the budget goes. It’s a very fast way to scale a campaign. This is a great handoff approach that takes your campaign off the ground & within days, it brings back a clear winner. However, there are more benefits to controlling the daily budget in ABO (Ad Budget Optimization).
When using the ABO strategy, you control the budget at an ad set level, so ideally, you’ll want to set the same budget fixed throughout your ad sets in order to balance the amount spent per variable tested.
If you’re spending £500 on one variable but £80 on the other – how could you compare those two variations? The more data you have & most importantly, the more accurate & unbiased you can make it, the clearer the result you’re measuring.
With ABO campaigns, you also increase or decrease the bid manually, so making changes is slower as you need to wait for a certain amount of time to pass before you can make changes. This is due to the learning phase of the ad set.
Top Tip: It’s vital that you don’t touch your campaigns during the learning phase until they’ve exited, as results will be inconsistent.
If you’re looking to grow a campaign fast & determine a winning ad set – then CBO is best practice. Meta’s algorithm has so much data to influence your campaign, so they predict which one will be best & then give most of the budget to the most successful variable that you’re testing.
This is great. However, if you saw that several variations in your testing didn’t have much budget & never exited the learning phase, then you may miss out on potential winners that just needed more budget for Facebook to learn more & adjust the targeting based on what’s working.
I personally prefer to set up the campaign as an ABO campaign. Then, after gathering up enough data – turn off the losing variations & turn on CBO if you’re A/B testing at an ad set level. This way, Facebook already has enough data to make the decision & split the budget accordingly with the remaining winning variations.
3. Decide Campaign Budget & Duration
A good amount of time to determine a winning ad set is 7 days, as the ad set will be in the learning phase for the initial few days. Depending on your daily budget, we need enough data to determine a winner.
Top Tip: It’s important to look at how much budget you have & how many people within the audience size you can realistically target within your testing time frame.
If your daily budget for a A/B testing campaign is £50 / day, then Facebook estimates you’ll reach 2,300 – 6,500 people.
So after 7 days, you’ll reach 16,100 – 45,500 people per ad set. If your audience size is large (1m+), then you’ll gather data of 1% of that target audience. It’s important to make sure that each variable has enough budget to reach enough people to make your judgment.
Lastly, always remember that as digital marketers, we set the parameters of a campaign, but 80% of the work is actually the algorithm which holds data of 3 billion users.
We must give the Facebook algorithm enough budget to show it to enough people to make the result. As well as enough time to let the algorithm exit the learning phase & then optimise your campaign.
After reading this blog, you should have a clear explanation of what A/B testing is, the best methods when using the Facebook ads platform & what to look out for to scale that same campaign that started as an experiment – into a massively profitable case study you’ll boast to your friends & colleagues about.