How does Facebook decide which ads to show people?
If you’re looking for a more in-depth article, check out our VP of Operations Aren Johnstone explaining the entire process in detail in this blog post.
But in a nutshell, Facebook determines which ads to show people based on two main factors: audience targeting selected by advertisers and the results of our ad auction.
As your marketing partner, Franchise Ramp chooses your target audience through Facebook’s self-service tools. Audiences are created based on categories like age and gender, as well as actions people take on our apps such as liking a Facebook Page or clicking on an ad. Franchise Ramp can also use the information we have about your audience, like a list of emails or people who’ve visited your website, to build a custom audience or a lookalike audience.
Next, when determining which ads to show someone, Facebook’s system gathers ads that include your potential customer in the chosen audience. These ads move to the auction stage.
For ads that enter the auction, Facebook selects the top ads to show to a person based on which ads have the highest total value score — a combination of advertiser value and ad quality. Facebook finds advertiser value by multiplying an ad’s bid by the estimated action rate. This is an estimate of how likely that particular person is to take the advertiser’s desired action, like visiting the advertiser’s website or installing their app. Facebook then add the ad quality score, which is a determination of the overall quality of an ad. Facebook uses machine learning to inform this process.
What is machine learning and how does Facebook use it to inform ad delivery?
Machine learning is a system that learns as it receives new data, without being explicitly programmed, to carry out complex tasks quickly and efficiently. Facebook uses machine learning to generate the estimated action rate and the ad quality score used in the total value equation.
To generate an ad’s quality score, Facebook’s machine learning models consider the feedback of people viewing or hiding the ad, as well as assessments of low-quality attributes (like too much text in the ad’s image, sensationalized language or engagement bait).
The advertiser’s bid, the estimated action rate and the ad quality score are combined to calculate the ad’s total value score in the ad auction.
How does machine learning improve ad delivery?
Over time, as more people view an ad, share feedback on it or click through to make a purchase on an advertiser’s website, Facebook’s models get better at predicting the estimated action rate and ad quality. Since billions of people use our apps and engage with ads each day, the system gets lots of information to help improve its calculations.
According to Facebook, “Ads with the highest bid don’t always win the auction. Ads with lower bids often win if our system predicts a person is more likely to respond to them, or finds that they’re higher quality. This allows businesses of all sizes to compete in the auction and reach customers on any budget.”
What controls are available to people to help determine what ads they see?
Ad Preferences page– A place for users to review and update their ad settings so they can take more control over what information we use when deciding what ads to show them.
Why Am I Seeing This– Shows users information about the detailed targeting options the advertiser chose to reach them.
Off-Facebook Activity– Some businesses send Facebook information about users’ activity on their sites and Facebook then use that information to show them ads that are relevant to them. Off-Facebook Activity lets users see a summary of that information and clear it from their account if they want to.
What are common misunderstandings about Facebook Ads?
According to Facebook: