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The headlines say ad tech is dying or even proclaim that it’s dead.
Consolidation is inevitable, no doubt, but I suggest that ad tech is evolving. Anyone who says that ad tech is dying is simply looking in the wrong place.
Sure, buying and selling ads in squares and rectangles is becoming commoditized, and users are boycotting them. And why not? It’s 20-year-old format; you can argue that it’s lived past its lifecycle.
But it’s important to look past the crowded landscape and see what’s ahead — there are lots of new and exciting solutions in advertising technology. Artificial intelligence is the area where I see incredible possibilities. Here’s why.
As digital advertising matures, artificial intelligence (AI) will become ubiquitous. However, there are gaps in the evolution of AI and advertising solutions that will require a whole new set of service providers creating solutions to fill specific voids.
From my point of view, ad tech isn’t dying, it’s simply morphing to meet the needs of today’s marketer.
I recently discussed how marketers are currently leveraging artificial intelligence (AI) with Ritesh Soni, SVP of data science, analytics and engineering at SapientNitro.
I asked him to name the biggest challenges involved in doing his job and handling an AI practice and here’s how he answered:
“The hardest thing that people are trying to wrap their heads around is being able to imagine ways in which AI can be applied and customized for their business. And Hollywood hasn’t helped.”
“However,” he went on to say, “once you speak to people on the ground like marketers and demystify what AI can do, it opens up a whole new dimension around understanding consumers and increasing relevancy in messaging.”
Here are three key areas where marketers are leveraging AI today.
Behavior-driven segmentation
It’s no secret that personalized messages drive higher conversion rates than those aimed at a broader audience. When MailChimp measured stats “across all segmented campaigns,” segmented campaigns performed much better than their non-segmented counterparts.
Email opens were 14.31 percent higher than with non-segmented campaigns, unique opens were 10.64 percent higher, and clicks 100.95 percent higher. Meanwhile, bounces, abuse reports and unsubscribes were all lower compared with non-segmented campaigns.
Essentially, this is personalizing marketing through analytics sitting on top of a campaign. It allows for top-down segments to evolve and be enriched with bottom-up data-informed signals, making creative execution clear and more relevant.
While behavioral-based targeting and expected returns has been a widely researched and published domain for well over a decade, it has surprisingly low penetration in its adoption within marketing operations in Fortune 100 companies.
Propensity modeling
Propensity, or predictive, modeling helps marketers identify the propensity that their customer will do something. This could be buying a related product or service. It could also be identifying if the consumer who purchased something in one product category will buy another item from another category.
We can add a level of scoring to actions which increases the data sets (why you need the AI) but allows for much richer actions.
[Read the full article on MarTech Today.]
Some opinions expressed in this article may be those of a guest author and not necessarily Marketing Land. Staff authors are listed here.
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