Commentary

How AI Blurs The Line Between Display, Search, Native And Programmatic

To understand how artificial intelligence is impacting marketing, it’s useful to first get a clear picture of what artificial intelligence looks like in marketing.

The truth is that AI influences our decisions many times throughout the day and we never notice it. When we search for “wrangler,” AI decides whether we are referring to the cowboy, the jeans or the Jeep. When we shop on a site and get suggestions for similar items, those suggestions are powered by AI.

When we talk about using AI in display and native, we’re talking about bringing together signals that we can collect from first-party, second-party, and third-party data. AI is the technology that layers these data signals on top of each other -- in a split second -- to deliver the right ad to the right person.

First-party data includes things like website visits, time on a page, purchase history, geography and any other elements you are collecting from customers. Second-party data is data that you may not have directly collected from customers, but is available to you through a relationship with a partner or publisher.

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For example, in-market audiences from Microsoft Advertising, or similar/lookalike audience. Third-party data includes things like financial understanding from a credit reporting agency, or vehicle registration from government records.

AI does what a marketer was never fast enough to do

Now a marketer can use AI to quickly sort through the massive first-, second-, and third-party data sets to produce a meaningful target, and then deliver a meaningful message.

In the example above, AI might determine that the searcher is ready to buy a Jeep Wrangler based on multiple visits to the Jeep website, a household income that aligns with the cost of a Jeep Wrangler, and a vehicle registration that shows this searcher has a 20-year-old car. With this rich information, AI will decide to deliver a display ad that offers a rebate on car purchases in the next week. All this happens in a fraction of a second.

As display makes the shift from a complete placement buy (I want my ad on this site) to programmatic (I want my ad to show up on this site, and show to people who have searched for my product before), to a complete audience-based buy (I want to reach women who have been researching Jeep Wranglers for at least two months, who live in Colorado, shop at Whole Foods and REI and have a full time job), we will see massive savings for advertisers.

AI helps eliminate wasted media spend

While simplifying the overly complicated field of display advertising, AI also ensures more relevance for customers.

At a time when many people are distrusting advertising and marketing platforms, using customer data intelligently and for their benefit is key. This is how we create a credible path for AI in marketing, while simultaneously avoiding the waste that is common when we lack data that can point us to the right customer at the right moment.

A highly relevant ad is the holy grail of marketing because it is more likely to generate customer engagement. Using AI means relevance is automated, and this is how we eliminate wasted ad spend.

AI is only as smart as the signals you train it on

Now that we understand what powers AI in marketing -- it’s your data signals -- we can step back and ask some important questions about those signals. Which signals matter most?

Ideally, your signals will span your customer’s entire day, from first log-in to browsing the news to searches for Spring Break airfare to work-related online activity.

Microsoft Audience Network is one of the few platforms that covers this much ground, combining decades of learnings with plenty of first and second-party data. For examples, the Microsoft Graph can pull in real-time browser activity, search activity and work-related activity via LinkedIn.

With this single platform, you’re gathering the richest signals across a broad span of the consumer journey.

If you’re not using the Microsoft Audience Network, look closely at data that can fill in some of the gaps. For example, association memberships or business license data can get you closer to that incredibly valuable work-time data signal.

AI that’s fresh is best

Finally, when you’re evaluating how you’re using AI to power your display and native advertising, keep in mind that AI is a lot like spinach: Fresh is best.

If your first-party data is outdated, your results won’t be what you’re hoping for. It's the same for any third-party data that you use as well.

The consumer journey is varied and winding, and having accurate signals from your customer can mean the difference between showing up with the right message (“$2500 cash rebate on Wrangler purchases made by June 30”) at the right time (customer has visited Wrangler website four times in the last six weeks) and showing up with the wrong message (“$2500 cash rebate on Wrangler purchases made by June 30”) at the wrong time (customer searched for Wrangler jeans).

The only way to get it right is to have the freshest data at every moment.

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