From algorithms to advertising: 7 steps to introducing AI to marketing

From algorithms to advertising: 7 steps to introducing AI to marketing

Artificial intelligence — once the rarefied domain of big-name, ambitious projects like Google’s self-driving car or IBM’s Watson — is now finding its way into everyday business. In advertising and marketing specifically, brands might not be completely overhauling their existing ad tech and martech stacks to make room for AI just yet, but many are getting a feel for it by experimenting with single-touch AI solutions that focus on isolated tasks, like recommendations, ad buying and optimization.

The coming wave of AI in marketing will be defined by the automation of complex, multi-step processes — not just one-off aspects of a larger campaign. For brands, this will mean relinquishing control, trusting the technology to come in and quickly understand processes comprised of numerous tasks, channels, people and procedures, without messing things up.

Before handing over the reins, it’s helpful to understand how AI works — and how entire human thought processes are converted into algorithms. For all its complexity, here’s a simplified look at seven steps to introducing an AI that can automate holistic digital marketing programs from start to finish.

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Creating artificial intelligence for “self-driving” marketing technology is not so different from creating AI for a self-driving car. In the case of the car, it must know how close it is to other cars. It must know how to make a turn and ensure that it’s in the right position at the end of the turn, when to hit the gas pedal to go faster, what the road conditions are like, and so on — all without the driver telling it what to do.

Like driving a car, many of the thought processes that go into the day-to-day execution of marketing programs also happen automatically and largely on the subconscious level. Transforming these subtle processes into a tangible series of algorithms means isolating logic and reasoning that humans often aren’t even aware they’re engaging in.

This begins with the acute observation of marketers and account managers as they execute each step of a process, over and over again. Often, things that seem trivial — like determining which image and headline combo work best for Facebook, how much budget to spend where, or picking keywords for a search campaign — are critical parts of a larger process.

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The AI doesn’t just need to know what steps to take; it ultimately needs to understand why each decision was made, whether it was based on experience, logic and reasoning or simply knee-jerk instincts.

This requires asking marketers and account managers to describe their decision-making process, which can be difficult considering that, as we’ve already discovered, they have no idea what motivated them half the time: Why did you keep these words and ditch those ones? How did you decide your bid size? Say you see a keyword doing well and you increase it by 20 percent — how did you choose 20 percent? What is the best time of day to send stuff to that person? Okay, what about that other person?

Data in the form of words and numbers are unquestionably the domain of AI. So, what happens when technology is asked to process and make decisions that are more creative in nature?

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For a human, understanding why certain images and text make more sense as a first interaction with a consumer rather than as a secondary or final interaction is almost second nature.



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