We’ve all heard of generative AI in marketing—but what about predictive AI?
In recent years, generative AI has really made a splash across every industry you can think of, making quick information and data organization so much more accessible to leaders and individual contributors alike. Already we see tools like ChatGPT becoming part of marketers’ day-to-day toolkit, in addition to being adopted and integrated into larger CEP and CDP tools. We now have the ability to just describe in natural language the data query we want to make to a larger database, the audience we wish to target in a particular campaign, the hero image we want in an email—and generative AI springs to action, writing that SQL code, building that segment, generating that hero image.
This shift in turnaround by itself has resulted in an immense streamlining of processes within marketing. But generative AI is only the tip of the iceberg in terms of the value that AI has to the MarTech space. If we were to zoom out, predictive AI is what we’re starting to see as that whole rest of the iceberg, with abilities so powerful, it has MarTech leaders preparing to see business revenue numbers jump up by $1.1 trillion as a result.
While the ramp up to predictive AI adoption in marketing applications has been slower, it is powerful, capable of integrating millions of data points continuously and providing insights on your brand’s past conversions and future message engagement. Predictive AI has the ability to take your customers’ information and turn it into, not only a personalized engagement strategy, but a detailed prediction of where each user will be in your funnel one, two, three months from now.
For so many brands, data like email click through rates, mobile app sessions, product purchases, and general user profile attributes are disparate, living in different silos, even within a CEP platform. The power of predictive AI lies in integrating all of that data and leveraging it continuously to target users when they are most engaged on their favorite platform, identify best message variants quickly, notify you when users are likely to churn soon, even act as your own product recommendation engine.
For marketers, A/B testing has always been the one true answer to identifying which subject line performs best, which hero image garners the most clicks, which promo code results in the greatest number of purchases. With predictive AI, the landscape of experiments shifts completely. There might not even be a need to find that best performing subject line, hero image, or promo code—all that matters now is what will do best for each individual user. In short, predictive AI bridges the gap between customer data and hyper-personalization seamlessly, with just the click of a button.
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Even though it holds great promise, the MarTech space has barely scratched the surface of predictive AI capabilities. In stark contrast with the bright, colorful splash generative AI made across the MarTech industry, we anticipate the steady tide of predictive AI to slowly seep into our marketing strategies, supporting each personalized message with trends in engagement and conversion data, backing each new remarketing strategy with past purchase data and churn predictions.
MarTech can be such a diverse arena—we would love to hear your thoughts on this push and pull between generative and predictive AI. Where do you see predictive AI fitting into your marketing strategies? We invite you to share this article or open up a discussion in the comment section below.