Ross Shelleman is the CEO of Aisle Rocket, a full-service performance marketing and creative services agency.
In many ways, technology has transformed advertising. The ability to use data and analytics to inform marketing decisions has had profound effects on the industry. Programmatic media buying and obviously the platforms of Facebook and Google have introduced scale and user data that have changed the marketing landscape forever. With technology, not only do marketing campaigns have the ability to be more personalized, but integrating them has become more seamless and targeted.
Unfortunately, the promise of marketing technology that powers this ecosystem does not always live up to the hype, and in many cases fails to deliver at all. Too much money has been raised and even more money has been wasted on trying to solve marketing problems with technology. Nowhere is that more apparent to me than with the latest shiny new object in ad tech: the customer data platform (CDP).
The core issue that CDPs are trying to solve revolves around unifying customer data. Once marketers deeply understand who their customers are, the rest of the marketing equation becomes easier. With varying definitions of what a CDP actually is or does, a multitude of technology solutions have been built claiming to have the ability to unify data sources and provide marketers with a holistic view of their customers. Furthermore, these solutions often tout the ability to be fully automated and make decisions on media buys and other important variables.
The problem is that there is still a huge amount of human oversight needed for these platforms to function properly. Just like what happened in the ERP (enterprise resource planning) boom in the 1990s, technology is useless without first solving for people and processes.
The challenge is that in today’s world, all customers cannot be treated equally. Think about somebody who buys one item vs. five. Somebody who shops once and then never shops again.
The question becomes less about personalization and more about how a marketer should communicate to and value a specific consumer. Unifying data from multiple sources that’s accessible to other systems and can self-optimize as more data is generated is the core promise of a CDP. Beyond that, there’s not a lot of agreement on what a CDP actually does or should do. Should it generate segments and new audiences? Should it automatically test and optimize creative?
Moreover, a CDP is only as good as the outcomes it’s optimized for. A DTC brand that’s looking for traction within a specific market with an eye toward future funding will require entirely different parameters than a legacy brand that’s trying to reach new audiences. Is a marketing team compensated for new customers or on lifetime value?
The Human/Computer Hybrid Team
In 1996 the IBM supercomputer Deep Blue beat World Chess Champion Gary Kasparov; it was the first time the best computer had ever beaten the best human at the activity that many believed to be the height of human intellectual ability. It was a turning point in the history of technology, and everybody assumed that computers would outperform humans at any similarly technical task for the indefinite future.
The computer science world soon learned that a team consisting of a computer combined with the judgment of a human player could beat the best computer operating in isolation. Human judgment still counted for an unquantifiable variable that gave the hybrid team an edge.
In marketing, there are simply too many variables that a CDP will never solve for. Marketing is, in many ways, an art form, no matter how much technology and data are used. Marketing is like walking into a casino — while data, analytics and technology simply allow marketers to count the cards, they do not guarantee that you will win the bet.
Only with skilled human judgment can technology — however powerful — optimize for the right marketing outcomes. Before believing software and platforms will solve all of their problems, marketers need to step back and define clear business goals and objectives. In many cases, using a great analyst and ad-hoc technology will be much more economical and probably lead to better outcomes.
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