How to Profit from Your Brand's Predictive Analytics Goldmine
Richard Boyd knows a thing or two about AI in analytics.
He's used to create increasingly complex award-winning video games as a game technology consultant.
He's deployed it in security and defense applications during a long stint at Lockheed Martin.
He's even used it to create a digital avatar of the late Marvel Comics legend Stan Lee. (On any given day, the avatar can behave online and converse just like Stan would.)
Today, Boyd is CEO of Tanjo.ai, where he applies his deep AI experience from game technology and defense to commercial applications.
That includes using AI to help brands better understand consumers and market to their customers.
In an exclusive interview, he talked with Marketing AI Institute about what brands need to know about "data exhaust," predictive analytics, and AI models.
These excerpts are from our Ask Me Anything series, an exclusive perk of being a member of our AI Academy for Marketers online education platform.
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Brands are sitting on a goldmine...
Boyd recommends brands explore the idea of "data exhaust," or the fact that consumers generate tons of data from their online activities.
By collecting this data exhaust, brands can begin to piece together a composite picture of their prospects and customers.
This gets interesting when you begin to layer third-party data on top of data exhaust, says Boyd. Nielsen purchase data or location services data can be mapped on to online behavior data.
This turns into a deep, rich story about the behavior of individual consumers.
In aggregate, this data can be used across millions of people to build powerful AI models that can segment and predict consumer behavior with incredible accuracy.
But they don't take advantage of it...
Yet most brands don't yet take advantage of data exhaust, third-party data, and robust AI models.
They still spend more than $3 billion per year on traditional market research methods like surveys or focus groups, says Boyd.
These methods are limited. They don't capture the full picture of consumer preferences, and consumers don't always respond truthfully about those preferences.
Boyd offers an example from his own work with a past client:
He worked with a chicken company that had conducted focus groups and surveys to determine if it needed to go fully organic to respond to changing consumer preferences.
Consumers overwhelmingly said they preferred fully organic chicken because they cared about their health and the health of their families.
They even said they were willing to spend twice as much per pound on fully organic chicken.
But the data told a different story.
Boyd and his team looked at different data sources on how consumers were actually spending their money, and applied AI models to extract insights.
One big finding...
When given the option to buy fully organic chicken, most consumers actually bought the cheaper traditional version as long as it was antibiotic-free.
This insight helped the company avoid making a costly mistake in how it allocated shelf space for its product. It also helped the company better serve consumer preferences—all thanks to better, richer data and smarter models.
Because they don't ask the right questions.
Using data exhaust and AI in your marketing comes down to asking better questions, says Boyd.
There is no doubt a serious technical component to getting up and running with better data sources and better models.
But brands often fail to take advantage of these new technologies and capabilities because they don't ask the right questions of their consumers, markets, and data.
Brands, like any people, are subject to bias. This bias—what companies think they know about their consumers—can create major marketing blind spots.
"There's the old adage of 'We know we're wasting half of our market research money,'" says Boyd. "We just don't know which half."
Without better data and better AI models, brands risk staying in the dark.
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