AI in Advertising: What It Is, How to Use It and Companies to Demo
In 2018, Lexus released what it called the first advertisement scripted by artificial intelligence.
Lexus used IBM Watson to analyze 15 years of “car and luxury brand campaigns that have won Cannes Lions awards for creativity, as well as a range of other external data,” according to Variety.
Watson was able to identify which elements from the dataset would resonate with viewers.
AI-inspired PR stunt? Most definitely.
Yet it also points to a deeper, more important story:
Artificial intelligence is taking over the world of advertising.
Artificial intelligence isn’t just creating ads, though commercially available platforms exist that use AI to create ads without human involvement.
No, AI is transforming what is possible in the world of advertising at every level, from ad creation to audience targeting to ad buying.
Andrew Ng, who built AI at Google and Baidu, highlights online advertising as a major use case for artificial intelligence in his excellent AI for Everyone course. In fact, machine learning—a core AI technology—is used everywhere in major ad platforms to determine if you’ll click on an ad, and that’s just one use case.
But it goes even further than that.
Brands today are beginning to use commercially available artificial intelligence to intelligently identify and segment audiences, build ad creative, test variations, improve performance, and optimize spend—automatically, in real-time, and at scale.
This has profound implications on both a brand’s competitive advantage when it comes to digital advertising and the careers of marketers who plan and run ad campaigns.
In this article, we’ll walk through a working definition of AI as it relates to advertising.
Then, we’ll look at some of the top AI use cases for advertising, so you get a better idea of how AI can actually create value for brands. (You’ll also better understand why not looking into it could present a competitive threat.)
Finally, we’ll show you some top companies that actually offer AI solutions in various areas of advertising, so you can start demoing tools.
What Is AI for Advertising?
The term “artificial intelligence” means different things to different people, and covers a lot of technologies. AI is an umbrella term that generally describes technologies like: machine learning, computer vision, natural language generation, and dozens of others.
For a great primer on AI, check out Karen Hao’s article, Is This AI? You can also see Karen present at the Marketing Artificial Intelligence Conference (MAICON), July 16 - 18, 2019!
AI technologies are worth diving into deeply, but you don’t need a full technical understanding of AI to take advantage of it as a marketer and advertiser.
The biggest point to understand is that AI excels at analyzing enormous sets of data better than humans and at scale. The most advanced AI learns from this data and improves its analysis, predictions, and recommendations over time.
From this fundamental superpower, many outcomes are possible, especially in the world of advertising.
AI is able to analyze huge sets of data about online advertising, then create ad copy and creative based on what it predicts will work best. It is able to inhale data about how consumers interact with advertising, then use those insights to optimize your campaigns to perform better. And AI can react to data from consumers and other ad buyers to vastly streamline and optimize your ad spend and strategy.
Make no mistake: AI is probably already being used to regulate the ads you buy—or create or optimize the ones you click on. And if you’re not paying attention, your competitors might just start using it against you.
Read on to learn just a few of the top use cases for AI in advertising.
Check out the Artificial Intelligence in Advertising 101 Workshop at the Marketing AI Conference (MAICON), July 16, 2019 in Cleveland, OH.
Use Cases for AI in Advertising
AI is critical to the infrastructure that underlies advertising products on many platforms, though you may not always see it. Modern programmatic platforms often use AI to manage real-time ad buying, selling, and placement.
Entire books could be written on that process alone. But, to make this article as practical as possible, we’ll focus here on AI use cases marketers can take advantage of through third-party tools, not the AI functionality that powers ad auctions.
(Just remember that, even if you’re not using AI tools in your advertising, the ad platforms themselves are—so it helps to pay attention to the technology.)
Performance and Spend Optimization
Performance optimization is one of the key use cases for AI in advertising. Machine learning algorithms are used by commercially available solutions to analyze how your ads perform across specific platforms, then offer recommendations on how to improve performance.
In some cases, these platforms may use AI to intelligently automate actions that you know you should be taking based on best practices, saving you significant time. In other cases, they may highlight performance issues you didn’t even know you had.
In the most advanced cases, we know of at least one commercially available tool that automatically manages ad performance and spend optimization, making decisions entirely on its own about how best to reach your advertising KPIs.
In another case, there exists at least one platform that allocates ad dollars automatically across all channels and audiences, so human beings can focus on higher-value strategic tasks, rather than manual guesswork about what works and what doesn’t.
AI-powered systems exist that will actually partially or fully create ads for you, based on what works best for your goals. This functionality is already present in some of the social media ad platforms, which use some intelligent automation to suggest ads you should run based on the links you’re promoting.
But it also exists in some third-party tools, which actually use smart algorithms to write ad copy for you. These systems leverage natural language processing (NLP) and natural language generation (NLG), two AI-powered technologies, to write ad copy that performs as well or better than human-written copy—in a fraction of the time and at scale.
Your ad targeting matters just as much as, if not more than, your ad copy and creative. Thanks to platforms like Facebook, LinkedIn, Amazon, and Google, you have a seriously robust set of consumer data with which to target audiences. But manually doing so isn’t always efficient.
AI can help here. We know of at least one AI system that looks at your past audiences and ad performance, weighs this against your KPIs and real-time performance data coming in, then identifies new audiences likely to buy from you.
AI Companies with Advertising Applications
Adobe Advertising Cloud is billed at a demand-side platform that unifies advertising data from digital and TV campaigns. As part of the platform, Adobe Sensei, the company’s AI product, offers “predictions on how to get the highest conversions for the lowest costs,” according to the company’s website.
Albert is another key player in the AI-powered advertising space. The company’s AI platform analyzes data across your ad accounts and customer databases, then uses sophisticated machine learning to target, run and optimize your ad campaign.
One Albert customer we profiled, travel company RedBalloon, saw a 3,000% return on ad spend by using the platform. Albert also identified new customer audiences to target that RedBalloon didn’t even know were interested in their products.
Lucy, a platform from Equals3, applies AI to marketing data in a number of novel ways, including to automate the media planning process. According to Equals 3 managing partner Scott Litman and chief product officer Rahul Singhal:
“Lucy solves the problem of the allocation of dollars across all media types and for all audiences. Consider this challenge for a brand marketer: They buy search, social, programmatic, Spot TV, Radio, etc. This media is managed in silos by experts skilled in a given channel.
Those same experts report back with metrics that show how each of the channels is working and why the marketer should spend more. What if the current media mix may be great in some markets but not in others? Lucy solves these problems by leveraging predictive models and forecasting how well a media plan will perform. She then provides re-allocation strategies across media types at a level that can be as local as tactic and zip code.”
IBM Watson Advertising, formerly The Weather Company (acquired by IBM in 2015), uses IBM Watson’s AI capabilities and (presumably) data from the acquisition to boost advertising results for brands. The company cites examples like using machine learning on Subway’s advertising to “process weather, sales and behavioral data to find the optimal mix of signals that drive consumers in-store.” Another example is the work IBM did with Hulu. IBM helped Hulu “exceed industry interaction benchmarks and generate valuable insights by leveraging Watson Ads to engage consumers in a 1:1 dialogue, uncover viewing preferences and serve up relevant content.”
Phrasee uses AI to tackle ad creation. One of the tool’s main capabilities is that it automatically writes email subject lines better than humans—but that same AI-powered functionality has now been adapted to automatically write Facebook ads and push notifications.
The platform’s AI is tailored to your brand’s marketing language and tone, so it sounds exactly like what your human copywriters would write. Then, it produces a huge number of ad copy variations at scale, thanks to sophisticated algorithms designed to increase clicks and engagements.
The result may sound like it puts ad copywriters out of business, but it actually frees up copywriters to work on longer-form and higher-value projects—and drive even better performance when promoting copy and content—instead of spending weeks working to get ad creative created, approved, and launched.
What Do You Do Next?
You have a better understanding of AI’s potential, its use cases, and some examples of actual companies that use it in advertising.
This article and our blog are great places to start.
But reading isn’t enough.
It’s absolutely critical that anyone running ads go from theory to practice as fast as possible if they want to develop a competitive advantage with AI—and avoid getting left behind.
If you choose to take action, I hope you’ll join us in Cleveland, Ohio, July 16 - 18, 2019 for the inaugural Marketing Artificial Intelligence Conference (MAICON).
MAICON brings together top authors, entrepreneurs, AI researchers and executives to share case studies, strategies, and technologies that make AI approachable and actionable for marketers and salespeople.
In addition to relevant speakers and main tracks, the event also has a dedicated workshop for advertising professionals, and 40+ sessions covering topics such as: advertising, analytics, content marketing, email marketing, ethics, robotics, sales, strategy, voice and more.
Click below to learn more.
About Mike Kaput
Mike Kaput is the Director of Marketing AI Institute and a senior consultant at PR 20/20. He writes and speaks about how marketers can understand, adopt, and pilot artificial intelligence to increase revenue and reduce costs. Full bio.