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How is AI used in advertising?
First, let’s see what artificial intelligence and machine learning mean.
Artificial intelligence (AI) is a broad concept that describes the machine’s ability to perform tasks and solve problems creatively — just as humans do. It is also about designing machines that can think, reason, and behave like people.
Machine learning (ML) is a way to apply AI to solve problems. Its main principle is that once a machine accesses data, it can automatically learn how to find the solution without being programmed for each specific task.
That’s all well and good, but what does it have to do with advertisement? Artificial intelligence and machine learning process complex information in large volumes to come up with smart AI-based advertising solutions. Ad platforms and advertisers adopted this technology to:
- Optimize the process of ad delivery
- Take the workload off advertisers
- Minimize chances of human error
- Find the best-performing ad visuals and copy.
It may seem that AI is the new kid on the advertising block. To be fair, it became a buzzword only a couple of years ago. However, AI made its way to the world of marketing in 2014 when programmatic advertising — an automated process of buying ads — became a thing, quite a big thing, actually.
This technology enabled brands to automatically buy ad slots (spaces where ads are displayed) from websites online. Ad delivery to relevant audiences became faster, more efficient, and cheaper since all the manual labor was removed from the ad buying equation.
The future of AI in programmatic advertising
Modern-day marketers may see the advertising methods from the pre-programmatic days as archaic and grossly inefficient. With artificial intelligence under the hood, the speed and performance of today’s advertising are by far more superior to that of several decades ago. This means that in the future, the use of AI in programmatic advertising will become even more indispensable.
The aspects that AI will keep handling in programmatic advertising in the future are:
- Ad personalization by processing vast amounts of data about a user to choose the most relevant ads for them
- Ad placement by analyzing the content on the website and the ad’s copy to understand which ad will suit it best
- Ad spend by automatically analyzing such details as website ranking, relevance, etc., and deciding on the optimal bid for the ad slot to prevent overspending
- Ad analytics by providing predictive analysis based on similariHow do Facebook and Google decide which ads to show?
Facebook Ads and Google Ads were quick enough to spot the potential of machine learning and invest in this technology — and it was a great idea. Here’s how the two platforms use machine learning, and what’s in it for you.
Facebook ML algorithms
Facebook Ads bring value to both advertisers and Facebook users. One of the ways to bring value is by optimizing ad delivery, so users see the right ads and marketers have more conversions and high return on the ad spend (ROAS). After seeing that AI and machine learning started outpacing the marketers’ manual work, Facebook started making the AI-based technology accessible not only to large ad agencies with their army of marketers, but also to small business owners. Here is how Facebook Ads empower eCommerce stores and digital marketers with the machine learning advertising tools:
Power 5
Power 5 is an AI-powered framework that drives the best return on investment (ROI) for advertisers. This framework includes 5 core tactics:
- Auto advanced matching to reach more relevant audiences and increase conversions by giving Facebook hashed (converted into lines of characters) customer details and Pixel events.
- Simplified account structure to let advertising algorithms identify the best-performing creatives and platforms to optimize campaigns in real time.
- Campaign budget optimization to spend the money only on the best-performing campaigns by setting one budget that will be spent on different ad sets.
- Automatic placements to automatically display ads to the relevant audiences across different placements such as Facebook, Instagram, Messenger, WhatsApp, etc.
- Dynamic ads to deliver targeted ads to people based on the content they viewed in the web store and the actions they took.
Campaign measurement
Delivering ads to people is not the only thing that matters in digital advertising. Tracking performance is crucial to understand if the strategy is working, and what to tweak in case it isn’t. That is why Facebook has introduced data-driven attribution (DDA) model powered by machine learning.
DDA is an attribution model that measures the progressive results you receive with artificial intelligence Facebook Ads. It demonstrates how people’s actions on Facebook result in a conversion. Facebook calls such actions “touchpoints”. They include impressions, clicks, and web store visits before the actual conversion.
To make sure that such attribution model is accurate, it is checked for bias. All results received under the DDA are compared with the Facebook conversion lift studies that didn’t use this model. This method ensures that there are no over- or underestimations of the results.
Google ML Algorithms
Of course, Facebook is not the only ad platform that flexes AI muscles. Google, another giant in the advertising market, has been honing the machine learning powered tools for some time now.
The platform offers advertisers AI technology to reach the following objectives:
Delivering highly relevant ads on YouTube
Long gone are the days when YouTube was only a place to watch funny videos. Now, it is a powerful platform that helps people make a purchase decision. For example, every other car buyer watches a video review before deciding on the car they want. Furthermore, every other millennial searches for cooking tips on YouTube before doing grocery shopping.
The task of an advertiser is to catch the person’s attention at the right moment. Machine learning helps them do just that with the Maximize Lift bidding. It delivers ads to people who are potentially interested in your product or brand.
Local campaigns to bring people to the physical store
While eCommerce is booming in the world of social distancing, many people still prefer buying items in a brick-and-mortar store. Thus, 80% of buyers will choose to go to a shop when they need a product right away. The number of “Near me” searches has increased by 3 times.
That is why Google gives store owners opportunities to bring potential customers to the physical shop with the Store visits ad objective. The business provides its address and ad creative, and Google delivers such ads to people looking for related products in their location.
How does machine learning improve ad delivery?
When the ad campaign is running, the stakes are high and the time is limited. In a matter of seconds, Facebook needs to match an ad with the people that are most likely to act on it. Can you imagine how tedious it would be to manually connect users and ads, given that Facebook has over 2 billion users? It would be also nearly impossible for developers to program the platform to make the optimal decision each time.
With machine learning, Facebook delivers relevant AI-powered ads fast and effectively. Facebook ad algorithms automatically analyze such information as the business objectives of an advertiser and the users’ behavior to understand how likely a person is to take the target action — visiting a website, signing up for an event, or making a purchase.
In turn, Google Ads offers responsive ads to automatically meet online users’ needs faster without extra effort. All an advertiser needs to do is come up with 4 descriptions and 15 headlines. Machine learning will then combine them depending on the search query people make. The efficiency increase is quite impressive: businesses that run responsive search ads get up to 15% more clicks.
- ties between ad viewers and existing customers.