Imagine you could find out which images and text are more appealing to your lookalike audience for a Facebook ad campaign, or a marketing email, without having to guess at random.
Conect uses machine learning trained from images previously seen by humans in different contexts. This helps the model to identify which tags are positively correlated, and which are negatively correlated with human engagement.
Connect is also fantastic for designing Facebook Ads, because you can use it to optimise and improve both images and copy.
The PRO version (coming soon) will also allow users to pick up images and text that are a better fit for different personality types.
Just think of the capabilities of this! For example, if you know that your target audience are predominantly introvert, you might be able to use AI to tailor every aspect of your ad creative to that type of personality.
And rather than guessing that pictures of people on their own will work better for your introverted audience, you’ll be able to back this up with data.
Connect for Facebook in action
The team at Datasine ran an experiment to see if AI could interpret beauty and predict engagement levels by pushing six images through the Pomegranate platform.
The AI then broke down each of the images into tags before assessing the tag’s positive or negative appeal.
Images of warmer seasons were ranked higher, whereas images with wood and more concrete features were seen as less appealing.
After these images had been analysed, the AI ranked them from most to least appealing and a Facebook test was run to see how accurate the predictions were.
The results? The two images that were ranked the highest by Connect also performed the best!
Read the full list of other AI tools for Facebook ads on Growthtribe.io.
So, on to the technical stuff…
How does AI identify engaging marketing images?
Being able to predict which images will convert customers the best is one of the most useful AI features for marketers using Facebook Ads.
As humans, we typically can’t tell what images our audiences will like – we’re subject to bias. We might like specific colours, patterns or designs – but these biased options may not perform the best when actually placed in front of an audience.
Computer vision is a scientific field that enables computers to interpret and understand images in a similar way to how human vision does.
Predictive algorithms can use computer vision to decide which image elements are most likely to be appealing to specific audiences, segments and individuals, and therefore select the best options for advertising campaigns – the options that are most likely to convert.
Bayesian inference is a method of statistical inference that some AI tools use to optimise your target audiences. Put simply, it updates the probability for a hypothesis as more information becomes available or is updated. It’s a bit like educated guessing – in the same way, we learn new information and update our predictions about an outcome, Bayesian Inference allows an algorithm to do the same to your Facebook Ads.
For example, let’s say that we decided to race two swimmers, Megan and Emma, four times. 3 out of 4 times, Megan won.
You’d predict that Megan would win the fifth race.
But what if, the one time that Emma won, the pool was cooler than usual. In the upcoming fifth race, you know that the pool will be cool again. What would you predict this time?
This is the type of problem that Bayesian inference attempts to solve, and it’s the core of many AI prediction models. You can read about it in more detail here.
So, there you have it! A complete AI toolkit to get you started with intelligence Facebook advertising.
Go forth and optimise those campaigns!