By Rohan Noronha, Facebook Partner Director, Httpool Bangladesh
It’s no secret that machine learning and artificial intelligence have become a massive part of our everyday lives, existing in all shapes and sizes, including the Facebook algorithm – delivering results to advertising campaigns worldwide.
Facebook’s machine learning algorithms influence everything from budget optimization to dynamic text, making it easier and more cost-efficient for any business to identify their ideal consumer on Facebook, Instagram, and Messenger.
And while the system can seem confusing to a newcomer, there are two key factors influencing which ads are shown at any given time. One you can control (target audience, goals, and ad quality), and another is managed solely by the Facebook algorithm (auctioning).
Marketers, through the use of highly targeted demographics, can effectively reduce the number of irrelevant ad placements on Facebook. Essentially, they are given the chance to tell the AI robots exactly what kind of person they wish to view their ads. After all, there’s no point in offering kitchen appliances to those ordering take-out every night! And the targeting possibilities are almost endless – think age, location, gender, interests, activities, and even past behaviour and interactions online. Your target audience can also now be generated from an email list or created as a look-a-like to a previously existing audience.
The campaign goal is completely in the control of the advertiser and needs to be strategically thought through before implementation. And when you know exactly what you want to achieve, enter the Facebook algorithm, assessing the possibility of this happening and evaluating user behavior to provide an estimated action rate.
But, nothing matters more than the quality of your ad. Facebook awards each ad with its very own quality score, determined by another of its machine learning models. For example, if users are seen to be hiding your ad, it can’t be providing much value to your audience. Actions like these lower an ads score and decrease its chances of appearing in the future. On the flip side, ads generating higher levels of engagement, in turn, have a higher quality score and appear more frequently on the platform.
If an ad’s target audience, goals, and quality score are at its heart, then the Facebook algorithm is the brain beating it. The two work together in parallel as the algorithm’s goal is to guarantee advertisers the best price-performance ratio. For example, if machine learning predicts that an individual will ignore an ad, the decision to place it should be rethought as the estimated action rate will be lowered.
When business leaders hear the word “auction,” they automatically assume that the highest bidder wins. But in reality, we’ve seen that offering to pay the most is only a minor factor in the equation of the Facebook auction. If the machine learning algorithm forecasts higher levels of engagement and favorable replies, you may have the lowest bid, but the race is still yours to win.
And as always, we need to make sure that our heart is in the right place, and our brain is stimulated. While we may have no influence over this machine learning model, the preparations we take while planning ad campaigns are critical. Deliver a weak ad and you won’t get very far. So make the most of your planning and strategy, leveraging analytics to improve your ad over time. This way Facebook machine learning models will work in your favor, and your heart will always go on.