In this article, we will discuss the ways artificial intelligence is changing marketing and why this marks a positive change. This article will also discuss how metadata can be more revealing than event data itself when collected and analyzed in aggregate, and why making all this data functional is the main strength of AI technology.
David Mihm to Mike Blumenthal: As for our Halloween topic, a spooky good SEO, Scott Hendison, tweeted a link over the weekend that I found fascinating: https://crowdsource.google.com. Even for those of us who are used to these kinds of initiatives coming from Google, it’s the most brazen public effort we’ve seen to train their machine learning algorithm via user contributions across a whole range of data types.
Mike: It is certainly brazen. There is NO attempt to bury this as an activity within some other program like their Captcha. It’s a gamification of their ML plain and simple, and if I know Google, the reward will be either insignificant or worse: a discount on some “premium product” (i.e., an ad).
Google’s Knowledge Graph ambitions are expanding to include obviating heavy reliance on secondary sources like Wikipedia and being able instead to classify and cross-reference information as a native, self-sustaining activity on web pages themselves. That’s what makes a recent patent filing different from the evidence of the Knowledge Graph we’ve already seen in the wild.
While this more ambitious way of surfacing information about entities is not yet standard, in researching Google’s new interface for hotels, I think I’m seeing evidence of a real-world example.
Damian Rollison: Among hundreds of sessions, exhibits, and demos, one theme came through clearly at IBM Think this month in San Francisco: for large enterprises especially, the AI-driven future for which we’ve been told to prepare is already here. In fact, enterprise companies are using IBM’s Watson technologies today to address a myriad of challenges inherent in the scale of those businesses.
The pitch is that today’s marketers with omnichannel inspirations need a machine learning-driven platform that will not only assess the success of campaigns across several media but also point them toward paths for future success. That’s an expensive technical infrastructure to create in-house, and conDati’s betting its solution is worth the spend.
Google announced on Tuesday a suite of new machine learning-backed ad tools that promise to keep its brand partners happy at a time when digital advertising faces unprecedented brand safety concerns. Among the tools is one explicitly designed to maximize foot traffic.
I want voice (control of everything), everywhere, but I have strict requirements for how it should be designed, engineered, and implemented. Here are six requirements for a reasonable deployment of voice everywhere.
Leveraging artificial intelligence and machine learning algorithms, vendors are finding ways to streamline some of the most complex operations—such as estimating the number of attendees and anticipating how many products each attendee will need—in live event organizing.
“We optimize the entire customer lifecycle journey,” says Artsai’s CRO Erik Lundberg. ” We may help someone acquire a new customer on Facebook, then reengage user on programmatic or RTB [real-time bidding], and then help drive the user to make a purchase inside the marketer’s mobile app or landing page.”
With the right data, a brand can keep the relationship with the customer warm while they’re off-property, out-of-town or on a budget. Offering the right deals and communications that are relevant to real interests means that the relationship between man and machine is getting better all the time.