Knowing your audience: AI-powered insights for campaign optimization

Leonard Newnham (chief data scientist, LoopMe) looks at how brands can use AI-powered insights to optimize their campaigns.

The digital advertising industry is embracing artificial intelligence (AI) models so powerful that brands can predict the success of an advertising campaign before spending a single penny. They can accurately forecast not only a consumer’s interests, but how likely they are to make a purchase at any point in a browsing session. This is the era of the AI-dience, the web3 version of audience segmentation.

How AI improved audience segmentation with machine learning

Traditional audience segmentation is the process by which 30-something executives are targeted with luxury brand ads, while expectant parents see baby toys. It is also flawed: serving cat food ads to dog owners and facilitating those annoyingly persistent links to trainers you searched for and bought a week ago. The use of AI puts an end to this, transforming consumer relationships with online ads.

For brands, the prize is high. The total UK advertising market contracted by 5.7% in the second half of 2022, as record inflation squeezed the margins of consumer-facing companies. This means advertisers must make reduced budgets work even harder.

This will only get tougher with the imminent phase-out of third-party cookies. As this part of advertising’s arsenal disappears, the industry needs technology that will sweat its limited reserves of first-party data.

Fortunately, the disappearance of third-party cookies coincides with the increasing adoption of AI. A US trade report values the UK AI market at $21bn, making it the third largest in the world. This provides a potential solution to the challenges of reach and ROI.

AI’s contribution to advertising goes beyond its ability to analyze large data sets; it can model advertising campaign effectiveness before a single dollar of budget is spent.

One of the tools AI uses to do this is machine learning (ML). The ML algorithms can consider thousands of variables, such as time of day, how many ads a consumer has already seen, previous browsing history, their preferred format, etc. They can also process big data at a rate that would have been unthinkable even five years ago. Even better, the algorithms improve over time to constantly optimize results.

ML can be used to create a more powerful version of lookalike audiences – groups of consumers that share similar characteristics to an existing audience that is known to be engaged with a particular brand or product.

Advertisers that use lookalike audiences are betting on ‘more of the same’, which has proven to be a fairly safe wager. However, even better results can be obtained when combining lookalike audiences with AI-created audience segments.

What are predictive audiences and how do they work?

Predictive audiences (AI-diences) are a group of consumers defined by the likelihood of them taking a specific action, such as downloading an app or making a purchase. AI creates these from mixed inputs, including historical performance, a user’s website interactions, and feedback from on-screen user surveys.

Predicative audience modelling is more precise than lookalike modelling as it examines individual behavior rather than similarities to a static seed group. It reaches new customers by spotlighting consumers who are most likely to be interested in a brand’s product.

How AI helps to measure ROI of audience targeting

AI also solves the ROI problem. The ability to optimize audience segments in real-time enables marketers (or rather, their chosen programmatic advertising software) to channel media spend to where it’s most likely to make an impact.

For example, if a website ad placement costs an advertising agency £5, they could serve it to a lookalike audience, but can only guess at probable engagement rates. However, using a predictive model, they can serve it to a narrower audience that is highly likely to engage. This AI-assisted predictive method reduces the risk of wasting investment on ad space that won’t meet goals.

The impact on ROI is persuasive. For example, LoopMe’s PurchaseLoop offering uses this technology to find consumers where they spend the most time on their mobile devices, and measures advertising effectiveness across brand metrics in a quick and scalable way – driving results that are two-to-five times higher than industry standards.

Moreover, using a pool of adverts, AI can tailor the messaging and the format to each audience segment. For example, it might serve a discount link to a group of consumers that it predicts are poised to buy or a brand awareness video to others who are new to a product or service.

AI and first-party data

Finally, AI-dience modelling is powerful enough to work from user-supplied, first-party data without dependence on third-party data. Consumers will retain maximum privacy while seeing ads they actually want to see.

Although much of the narrative around AI’s seemingly sudden proliferation has concentrated on the division of humans and machines, the AI-dience can bring computers closer to understanding ever-evolving human behavior and delivering a personalized consumer journey.

This piece (originally published on 09/18/23) was updated on 02/13/2024 to include a summary standfirst.