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.

[Read the full article on MarTech Today.]

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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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About The Author

Tomer Naveh leads planning and development of Albert™, the first-ever fully autonomous artificial intelligence marketing platform, created by Adgorithms. Tomer’s approach to developing Albert was to “reverse engineer the logic and intuition” of marketers, account managers, and all others responsible for day-to-day digital advertising and marketing campaigns. From there, he and his team built an AI solution that eliminates the silo’d approach characteristic of a multi-person or multi-technology digital marketing program. Instead, Albert approaches campaigns from a holistic vantage point where insights gained on one channel or device, are autonomously applied to other channels or devices in real-time — all with little to no intervention from marketers, aside from providing their upfront creative, KPIs and targets. Tomer has 23 years of experience in software development, and over 20 years of big data and machine learning experience, including 16 years in consulting and executive management.

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Author: Tomer Naveh

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