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We’ve all experienced it: the commercial that runs half a dozen times during our favorite TV show, or the online commercial that follows us everywhere. We’re looking for something and suddenly there are ads for it all over our social media feeds.
As digital audiences have grown, driven primarily by the growth of channels like CTV/OTT and audio streaming, advertisers have poured buckets of money into delivering their brand messages to this captive audience.
While targeting technology has evolved dramatically to offer more relevancy and better personalization, it’s not without flaws. Oversaturation is still a problem. And automation can sometimes be over-optimized for a specific, perhaps unintended, trend.
The need for a human touch in advertising
One reason ad delivery sometimes misses the mark is that technology doesn’t understand the nuances of human behavior. In fact, AI should be inherently free from bias and influence. But when it comes to advertising, there is a lot of intuitive information to consider, especially when it relates to human behavior.
Therefore, although AI technologies have had a major impact on improving the ad experience, it still requires human touch to interpret and inform the model. Here’s how marketers can use AI to provide a better customer experience.
Identify and act on trends at scale
Certainly, analysts could look at ad performance data to see what’s resonating and use those insights to optimize campaigns. But it’s impossible to do this at the required speed and scale. Effective performance measurement requires cross-platform, real-time analytics – how ads are performing across multiple channels studied together – and real-time optimization to be effective. By using AI to analyze and optimize, marketers can eliminate repetitive, annoying, or misplaced ads.
Use multi-touch attribution
Digital marketing has traditionally relied on first- or last-touch attribution, meaning that the “credit” for the purchase, 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 propelled the action — multiple touchpoints in a given spot, strung together in a row — and that each consumer’s journey experience is infinitely different. AI can analyze this dynamic journey, learn the specific touch points and cascade through multiple channels that increase efficiency and deliver just the right experience to influence shopper behavior.
Manage volumes across platforms
AI-based ad platforms are optimized for performance. But for a machine, high performance means placing the most ads in front of the largest, most valuable audience. That can have a decidedly negative firefighting effect, not to mention blowing the budget in no time. It’s like turning the sink faucet on full without adjusting the flow or temperature. That’s why it’s important to adjust variables to manage the volume of ad delivery, including setting frequency caps that span multiple platforms so consumers aren’t first bombarded and then ghosted.
Use smarter contextual targeting
AI can not only make ads relevant to the viewer based on known interests or intentions, but also make them relevant based on the context in which they appear. For example, if an advertiser set up a weather trigger to sell their latest winter coat, they might not want that ad to run during a climate change discussion. But what if this week’s weather segment is about a change in climate — say, a drop in temperature? AI can tell the difference and switch the display accordingly.
Include attention metrics
Marketers have traditionally used playback length to measure the effectiveness of ads – the longer a viewer lets them play, the more interested they must be. But that only tells part of the story. During a commercial, how many times have you gotten up and walked away from the television or put the device down to get a snack? With AI, we can optimize attention metrics, which typically means delivering our message in the context of higher quality, more compelling content—content that audiences are less likely to turn away from. AI helps brands do this in real-time, but again, human knowledge is needed to know what’s exciting and catches people’s attention.
AI also needs a human touch
Of course, AI is certainly not without risk. In fact, without proper input and alignment, it can start making bad decisions. For example, if we see the performance of a particular creative starting to decrease, the AI may want to back out of that purchase and shift spending elsewhere, especially if the CPM is increasing while the audience is shrinking. But the campaign might just reach the more engaged, high-value customers further down the funnel. The cost may be higher, but so will the return on ad spend since it’s a more valuable audience. Human leadership is key to preventing AI from misoptimizing.
In a world where privacy is a constant concern, it’s important for adtech vendors to understand how to reach people in a meaningful and engaging way without bothering them or interrupting their experience. Using AI, aided by human intuition, to optimize targeting and delivery offers a much better curated experience that delivers value for the consumer.
TJ Sullivan is EVP of Sales at Digital Remedy.
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