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Check out our latest case study on one media company’s first steps in more efficient and effective hyper-targeted advertising. First Steps for Advertisersįor advertisers who have not yet fully embraced the ML and AI wave, it’s most definitely time to get started. If it’s tailored to what I like and visually appealing, even better. If advertisers show me something I’m actually considering purchasing, especially if it contains information I’m looking for, it could feasibly help me make my decision. When it’s extremely well targeted, it’s helpful. When advertising is not properly targeted, it becomes glaringly obvious (and often irritating). And if it's more targeted and therefore more effective, perhaps less prevalent. Or there can be a PR nightmare for ad targeting mistakes, but that’s less of an issue for the customer, in most cases.īut more importantly, ML and AI represent an opportunity in advertising because they can actually in many ways make advertising less noticeable.

But with advertising, the worst that happens is that the customer doesn’t buy something. When we talk about AI dominating in other spaces, like health care, predictive maintenance, banking, or even in larger contexts like smart cities, the question is always: well, what if the machine fails? What if the machine is wrong? Consequences, in these instances, can be grave. The first is that, from the public’s point of view, it’s low risk. ML and AI have been able to flourish in the advertising space for two primary reasons.

Measuring the customer journey across multiple channels.

Attribution (i.e., measuring the precise impact of certain ads).

Intent prediction (i.e., how likely is this person buy item x in the next month?).

Predicting which messaging will resonate with what users.

And for good reason - most of the ML and AI use cases in advertising are centered around eliminating marketing waste, which ultimately means only spending money on those that will buy.Īdvertisers have been using ML for quite some time for a number of use cases, including: If there’s one industry that has swiftly and uniformly transitioned into the world of ML and AI, welcoming it with open arms, it’s advertising. How Advertisers Use Data Science, ML, & AI Yet interestingly, while much of the public’s thoughts around AI seems to be somewhat negative (deeming its implications as too “Big Brother” for comfort), the advertising industry - which historically has a somewhat negative image itself - has slipped under the radar in this regard. These days, of course, ML and AI are hot topics across all industries, not just advertising.

Not just collectively, but down to an individual level - one of its most touted features is the ability to target ads based on the make and model of cars actually driving by in real time. The new billboards are equipped with hidden cameras that gather data from both foot and road traffic and can serve advertisements based on that data. Today, Piccadilly Circus in London is alight once again, now equipped with so-called “super billboards,” bringing the advances of machine learning (ML) and artificial intelligence (AI) in advertising to center stage (or center circus, if you will).