How can AI be used to improve a company’s marketing?
Recently published articles, which I encourage you to read, provide valuable insights on this topic. However, to get maximum benefit, you should recognize that what it takes to get the most out of AI shares commonalities with what it takes to get the most out of older, more established techniques.
The AI in marketing articles appeared in the July-August 2021 issue of Harvard Business Review. That issue featured a Spotlight section titled “AI-Powered Marketing”, which included three AI articles. The articles are titled 1.) “How to Design an AI Marketing Strategy” 2.) “Why You Aren’t Getting More from Your Marketing AI” and 3.) “Don’t Buy the Wrong Marketing Tech”.
The first article is subtitled “What the technology can do today-and what’s next”. The article advises being realistic about AI. It says, “Despite the hype, AI can still accomplish only narrow tasks, not run an entire marketing function or process. Nevertheless, it’s already offering substantial benefits to marketers.” In my view, being realistic is important since AI can have implementation challenges, which must be dealt with to get the best results.
The second article has a subtitle that provides an explanation for “Why You Aren’t Getting More from Your Marketing AI”. The subtitle is “Chances are, you haven’t asked the right questions.” As someone who, early in my career, held a position as that era’s version of a data scientist, and who also held other positions involving research and analysis not as technical as data science, I can say that asking the right questions is crucial. It doesn’t matter whether you do something technologically sophisticated like AI or you do more basic analysis. For the best results, you need to ask the right questions. In other words, even in days past when AI was not widely used, asking the right questions was crucial. And, of course, asking the right questions is crucial even when doing the qualitative research that has sometimes been used in situations where AI didn’t work well.
Thus, getting more from AI really entails doing what was required to get good answers even when simpler or older techniques were used. In fact, the “Why You Aren’t Getting More…” article discusses this type of issue. It says, “Most marketing AI doesn’t make new decisions; it addresses old ones such as segmentation, targeting and budget allocation. What’s new is that decisions are based on richer amounts of information that are collected and processed by the AI.” I’ll take this a step further and emphasize that age-old ways to get the most out of your data can also drive the success of AI. There’s richer data and newer technology today, but much of what it takes to make sense out of your data remains the same.
Likewise, the third article makes recommendations that are not new, but are being applied to the latest AI technology. The article advises that ”managers need to recognize how interdependent the elements of the martech stack are.” It gives the example of an event registration system that can’t communicate with systems for email distribution and customer databases. Here again, I would say that it doesn’t matter whether you are doing the latest AI, or whether you have a marketing program that uses older technologies. Well managed marketing programs are based on recognizing the interdependence of the various components and coordinating them to work well together. Although that is not new, it does play a role in the success of AI.
In conclusion, a lot of what is required to get the most out of AI applies the same principles that worked well for using older technologies. Whether it’s being realistic, asking the right questions, or recognizing the importance of interdependent parts, ways to get the most out of AI parallel what it takes to get the most out of older methods. Nonetheless, material that discusses these issues, as the three Harvard Business Review articles do, can be a valuable resource for making AI work well.
