Influencer Orchestration Network

What Google’s AI Offerings Mean For Influence Marketers


AI is letting marketers unlock the value in Big Data to gain insight, not just to market based on demographics.

Google has adopted an “AI first” mission statement, according to the company’s I/O 2017 keynote. The tech giant introduced the second version of its Tensorflow Processing Unit (TPU) called the Cloud TPU. This upgrades the hardware it uses for Tensorflow, an open-source machine learning software for developing AI-powered tools and apps.

So why should influence marketers care? Three words—data, data, data.

Market research and advisory firm Ovum estimates that the big data market will grow to $9.4 billion by 2020. That’s 10 percent of the overall market for information management tooling. AI is helping marketers make better creative decisions, including a focus in influencer marketing.

With the entire internet at your fingertips, finding the right social media influencer to partner with your brand can be daunting or downright impossible without this vital data. Dr. J. Galen Buckwalter, a behavioral scientist and the original chief scientist of eHarmony teamed with Ayzenberg to apply matchmaking criteria and machine-learning to the marketing process—resulting in the technology used by ION.

“What we did at eHarmony was pretty much exactly what we’re doing here [at Ayzenberg], at least on the psychometric perspective,” Buckwalter told Ad Age.

Data for marketers is so much more than a creator’s number of followers or even key words in posts. The criteria for a “brand soulmate” is based on common ideals, creative voice, authenticity and of course—saying the right things for the right reasons. AI like the technology used here at ION can empower marketers to gain deeper understanding of their audiences and the social media creators with whom they work.

Not all AI is created equal, of course. Some tech offerings billed as “AI” may just be simple training of machine-learning models, and are not necessarily built by people who understand what is behind the data. “It’s a little frustrating for somebody coming from the science side, to be honest,” Kai Mildenberger, chief technology officer at Ayzenberg and told Ad Age. “We just didn’t come up with, ‘Oh, here’s a buzzword, let’s see if we can apply it.'”

Google’s Cloud TPU is significant because while Amazon and Microsoft offer GPU processing via their own cloud services, they don’t offer bespoke AI chips for both training and executing neural networks. For now, Google has cornered the market, but not for long. Several companies, including Intel and a long list of startups are now developing dedicated AI chips that could provide alternatives to Cloud TPU.

“This is the good side of capitalism,” Chris Nicholson, CEO and founder of a deep learning startup called Skymind told Wired. “Google is trying to do something better than Amazon—and I hope it really is better. That will mean the whole market will start moving faster.”

AI systems are really good at parsing and crunching massive volumes of data from disparate sources. They can take information from a variety of inputs, find relationships, connect dots and make predictions in ways that are not humanly possible. A recent study conducted by the National Business Research Institute (NBRI) for Narrative Science found that 38 percent of US business executives considered this type of data crunching and predictive activity to be the most important solution provided by the whole field of AI.