How AI Builds Better Outcomes

Published on December 19, 2018
blog, insights
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Artificial intelligence is exceptionally well-suited to help programmatic advertising fulfill its promise and drive real outcomes for brands.

Its capabilities surpass the ability to find “better qualified users" and improve targeting— both of which agencies, publishers, and tech platforms agree AI can do.

When the many components of AI are used in concert, they can elevate programmatic to drive deeper and more ambitious business goals than simple marketing metrics such as demographics, CTR, or CPM. And they can add up to a significant transformation in digital advertising strategy.

Programmatic advertising is, after all, about handling massive amounts of data and then using those data to drive real results. That meshes perfectly with AI, which is about intelligently handling massive amounts of data to ever-greater effect.

Asking the Right Questions

Achieving those higher goals requires a keen focus that usually starts with correctly articulating the advertiser's imperatives.
Let's take a typical, high-end brand to illustrate the point. It's safe to say the brand's strategists typically know how much the sale of a core product is worth in ad spend, as well as new customers' lifetime value.

Armed with that information, data scientists and engineers can focus on using AI to optimize a programmatic campaign to the ratio of sales achieved against the cost of marketing to achieve those sales.

They can refine and extract further value through machine learning, which can find solutions a human might reflexively avoid, such as when a higher volume of less expensive clickthroughs leads to better business outcomes than more targeted and costly impressions.

Real-World Examples of AI Driving Outcomes

The use of AI to achieve business outcomes in programmatic media is not hypothetical. Here are some real examples:

Cutting Time and Add Effectiveness | A telecommunications company needed to optimize more than 2,000 campaigns it was simultaneously running in a programmatic advertising platform. They used Xaxis' AI engine, Copilot, to simplify setup, then implemented its “Segment Recency" strategy to optimize retargeting spend towards pockets of strong performance. The conversion rate more than doubled compared to the control. They also saved time and avoided the risk of error from manual optimization.

Boosting Conversions and ROAS | A luxury retailer had a unique return on ad spend (ROAS) goal for their digital campaigns. A customized regression algorithm was used to analyze log-level data and predict better-performing impressions. Employing Copilot, they used Xaxis' “Predictor Strategy" to look for features with the strongest correlation to performance to drive smarter optimization. Conversions increased 32 percent in one campaign flight, 53 percent in another when compared with human optimization.

Improving Media Buying Cost-Efficiency | A machine-powered bidding strategy was used to extract maximum value by conducting large numbers of real-time tests in very granular increments well beyond the capabilities of any human. To find the right price to win bids between $1.00 and $2.00 in an ad exchange, AI was able to run 10,000 tests in increments of fractions of a penny to hone in on the precise and most cost-effective bid in that range. A feedback loop was created in which the outcomes continually improved in lightning-fast increments, adding up to big results for the campaign. A programmatic specialist alone trying to achieve the same goal would typically test bids in much broader and fewer increments — differentials of, perhaps, 10 or 20 cents — and thereby leave a lot money on the table by finding bids well above the optimal level.

These are just a few of the ways AI is being deployed today, and its capabilities will only improve. AI will soon move from its current phase of skepticism into an “'amazement' phase for what [it] is enabling in general, and specifically in digital marketing," according to Brian Anderson of LUMA Partners.

The most powerful implementations of artificial intelligence come from using it to optimize towards real business outcomes born of key business strategies that drive stronger outcomes for brands.

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