How AI is improving the digital ad experience for consumers

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 – 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Register today!

We’ve all experienced it: the ad that runs half a dozen times during our favorite TV show, or the online ad that follows us everywhere. We search for something once, and suddenly there are ads for it all over our social media feeds.

As digital audiences have grown, fueled largely by growth in channels like CTV/OTT and streaming audio, advertisers have been pouring buckets of money into delivering their brand messaging to these captive audiences.

While targeting technology has evolved dramatically to provide more relevancy and better personalization, it’s not without flaws. Oversaturation is still a problem. And automation can sometimes over-optimize for a specific, perhaps unintended, trend.

The need for a human touch in advertising

Part of the reason ad delivery sometimes misses the mark is that technology doesn’t understand the nuances of human behavior. In fact, AI should be, by design, devoid of biases and influence. But when it comes to advertising, there’s a lot of intuitive information that must be considered, especially as it relates to human behavior. 

That’s why, despite AI technologies making a big impact to improve the ad experience, it still takes human touch to interpret and inform the model. Here’s how marketers can leverage AI to deliver a better consumer experience. 

Identify and respond to trends at scale

Certainly, analysts could look at ad performance data to figure out what’s resonating and use that insight to optimize campaigns. But doing it at the speed and scale necessary is impossible. Effective performance measurement requires multiplatform, real-time analysis — how ads are performing across multiple channels examined together — and real-time optimization to be effective. By using AI to analyze and optimize, marketers can eliminate repetitive, annoying or misplaced ads.

Leverage multi-touch attribution

Digital marketing has traditionally relied on first- or last-touch attribution, meaning the “credit” for the buy, web visit or download is attributed to the first or last impression the consumer was exposed to. But in reality, it’s more likely that a waterfall effect drove the action — multiple touchpoints in a specific placement, strung together in a series — and that experience is infinitely different across every consumer’s journey. AI can analyze this dynamic journey, learning the specific touchpoints and cascade across multiple channels that drive efficacy and delivering that just-right experience to influence buyer behavior.

Manage volume across platforms

AI-based ad platforms are optimized for performance. But to a machine, high performance means getting the most ads in front of the largest, most valuable audience. That can have a decidedly negative firehose effect, not to mention blow through the budget in no time at all. It’s akin to turning on the sink spigot full blast without adjusting flow or temperature. That is why it is important to adjust variables to manage the volume of ad delivery, including setting frequency caps that span multiple platforms, so consumers aren’t bombarded at first and then ghosted. 

Deploy smarter contextual targeting

Beyond just making ads relevant to the viewer based on known interests or intent, AI can also make them relevant based on the context in which they appear. For example, if an advertiser has set up a weather trigger to sell their latest winter coat, they may not want to have that ad run during a climate change discussion. But what if it’s a weather segment about a change in the climate this week — a drop in temperatures, for example? AI can tell the difference and deliver the ad appropriately. 

Include attention metrics

Marketers have traditionally used length of play to measure ad effectiveness — the longer a viewer lets it play, the more interested they must be. But this only tells part of the story. How many times have you gotten up and walked away from the TV or put down the device to grab a snack during an ad? With AI, we can optimize for attention metrics, which typically means getting our message out within the context of higher-quality, more compelling content — content audiences are less likely to turn away from. AI helps brands to do that in real time, but again, it requires human insight to know what’s gripping and will keep people’s attention. 

AI also needs a human touch

Of course, AI is certainly not without risk. In fact, without proper input and tuning, it can start to make poor decisions. For example, if we see that performance of a specific creative is starting to dip, AI may want to pull out of that buy and shift spending elsewhere, especially if CPM is going up as the audience shrinks. But it could be that the campaign is just reaching further down the funnel to the more engaged, high-value customers. The cost may be higher, but so is the return on ad spend because it’s a more valuable audience. Human guidance is key to preventing AI from optimizing incorrectly. 

In a world where privacy is an ongoing concern, it’s important for adtech vendors to understand how to reach people in a way that’s meaningful and addressable without being annoying or interrupting their experience. Using AI, backed with human intuition, to optimize targeting and delivery provides a much more curated experience that adds value for the consumer. 

TJ Sullivan is EVP of sales at Digital Remedy.

Originally appeared on: TheSpuzz