Think of the rise of Big Data as the “Big Bang” of digital marketing. Before it, digital marketing and its analog counterpart were the sums of gut feelings, psychology textbooks, and only a broad understanding of “what happened before.”
Then, BANG! Online commerce arose…and there was more customer data than we could ever interpret. The solution to the flood of data? Artificial Intelligence.
Of course, it seems obvious now. Despite the ghost stories being told about AI, entrepreneurs and data scientists recognize AI as a quick, useful way to process data at a rate that no team ever could.
AI allows marketers to understand customers by looking far beyond buying trends and stereotypes. It offers access to customer and customer-search contexts, allowing for better experiences and more personal, persuasive marketing.
Without AI: “Personalization” would come only in the form of Dear [First Name].
Pre-AI, personalization was the stuff of permission marketing, of offering opt-ins and assuming that those who didn’t opt out were willing and eager to hear what we had to say. While innovative at the time (and still a better idea than sending unsolicited e-blasts), the whole effort relied on the premise that the respect we showed our customer segments would magically translate to sales/qualifications. It was an “if you build it, they will come” mentality, and it wasn’t the magic sales tool many hoped it would be.
Now, with AI processing, we’re able to understand who and where our audiences actually are, and we can go get them. We now know how old they are, what they like, how they prefer to be advertised to, what social media they share, who they influence or are influenced by, and we can use this information to create a perfectly tailored experience. That way, when we ask for permission, we’re asking in exactly the right lingo and tone in exactly the right place at exactly the right time for the person in question.
Without AI: Predictive analytics would still be a game of gut instincts.
Market forecasting and predictive modeling have long been assets of serious marketing departments, but they were hit or miss. For market forecasting to send sound strategies off in the wrong direction, all it took was overlooking a single trend, or basing predictions on the wrong model.
AI has altered that forever, offering us processing engines that help point us internally to our own customer and market data, allowing us to look at all the variables we’d identified as indicators of where to turn in the future. Our hunches about customer experience, for example, can be confirmed or denied by using tools like Clarabridge that employ machine learning to interpret millions of data points – from call center calls, social interactions, email campaigns, etc. – evaluate current effectiveness, and predict future results. Then, these same tools can be deployed to track progress against the projection, refocusing if we got off-track or redirecting if we were forced to pivot.
Without AI: Programmatic ad buying would be a thousand monkeys on a thousand typewriters.
The process of programmatic ad buying is extremely complex and relies solely on a machine’s ability to make decisions. When the decision’s right, the consumer (complete with whatever preference data about them is stored on a computer) executes a particular search or lands on a specific page, and, within about 120 milliseconds, is delivered an ad based on who they are, what they’ve searched, keywords associated with their search/history, etc.
To execute that kind of extreme personalization by hand and on a case-by-case basis would be impossible. AI, however, has no trouble at all. Unfortunately, it’s still an imperfect process – there’s still a lot of performance oversight and price tweaking needed to truly make the engine “hum,” and insiders are quick to target best prices which can drive down the quality of return. That said, AI-driven programmatic ad buying shows big promise for finding the right person at the right time with the right message for a fair price.
Without AI: Ad delivery would be billboards on the Internet.
Much like the billboards of yesteryear, pre-AI ads were part of the interrupt advertising strategy, designed to distract and conquer. Unfortunately, these efforts translated to little more than contextual guesses about what consumers may want to buy given their location (McDonald’s billboards on the highway, for example) or what might distract them enough to take real notice of a brand.
Unfortunately, these “notice me” purchase trends missed the mark by failing to consider the context of individuated customer searches. AI, however, doesn’t forget this. It looks to Google’s Ad Network, scans customers’ cookies, and the AI knows, for example, that customer has been looking at Canon DSLR cameras and delivers ads relevant to their search. Used sparingly (because too much retargeting can be upsetting and/or creepy, given current consumer social mores), these methods boost sales dramatically and help us spend marketing dollars with a much greater impact than otherwise.