As part 1 in the follow up series to our Digital Predictions 2017 eBook and Webinar, we are kicking it off with one of the most cutting edge marketing discussions: Artificial Intelligence
Popular movies and TV shows like Ex Machina and Westworld portray Artificial Intelligence (AI) like a frightening future world that will only lead to some kind of human-versus-robot scenario. People are in a state of panic, fearful they’re going to lose their jobs to robots or AI systems. But for marketing professionals, AI isn’t something to be afraid of at all. Granting marketers a new level of efficiency, AI is set to be one of the groundbreaking marketing tools of 2017.
Artificial Intelligence Vs. Machine Learning
If you’re even slightly aware how AI impacts marketing professionals, chances are you’ve heard the term Machine Learning (ML) also. While AI and ML are part of the same family, the two terms shouldn’t be used synonymously. Let’s define both AI and ML to set the record straight.
Artificial intelligence is a term used to describe a system that’s designed to think like a human, without any explicit programming. Machine Learning is a bit different. As defined in our 2017 Digital Predictions webinar, “machine learning is the subfield of computer science that gives computers the ability to learn without explicitly being programmed.” In other words, Machine Learning is a building block of Artificial Intelligence.
How AI Will Be Used In 2017
For marketers, AI will mean a new level of ease. Large companies now have access to all sorts of user-data thanks to cross-device, cloud-based connectivity. Anytime a person makes a purchase on any device, that information is logged. Eventually, an AI system will be able to compile all that data and predict a user’s needs.
The information provided by AI tools also grants a new level of insight into ad performance. Growth can come from what may be a traditionally “underperforming” ad, and an AI tool can point out these opportunities so marketers can identify and capitalize on them.
But that doesn’t mean you should be letting AI do all of the work. An ad campaign informed by AI-collected data that is infused with some human creativity will spell success for advertisers. In fact, “Google cites case studies in which more than 16% of enterprise campaigns are Dynamic Search Ads (DSA)… [which are] examples of both AI targeting AND creative,” points out Manny Rivas, CMO of Aimclear, in our eBook.
Marketing automation tools aren’t for every company, though. Take machine learning, for example. To successfully use an ML tool, you need lots of data. Only with a large amount of inputs will a machine learning tool be able to create successful outputs, followed by ongoing learning to become more effective automatically.
Expert Predictions for Marketing Automation
Even if you’ve just learned what AI (and ML) is, that doesn’t mean you can’t get ahead of the curve. Below we’ve assembled some predictions from experts in the industry on how AI and market automation will be used in 2017 and beyond.
AI Will Become A Butler For Users
- “Players like Google and Microsoft are focusing heavily on AI and applications that will act as dedicated personal servants of one form or another” -Andrew Goodman, President of Page Zero Media.
Human Creativity + AI Are The Ultimate Combination
- The raw data provided by AI tools can (and will) help marketers see how ads are performing beyond simple click-through rates.
Third-Party ML Tools Will Be A God-Send For Smaller Companies & Advertisers
- Don’t assume you need to create your own ML tool. For many smaller companies, a third-party tool may be the best route.
Smaller companies typically measure fewer data points than say an enterprise company with multiple brand touch-points on an international scale. Since ML tools are as smart as the data they receive, marketers may think that large companies therefore have an advantage over smaller ones when it comes to leveraging AI and machine learning tool. This isn’t necessarily the case. Acquisio’s Bid and Budget Management (BBM) Technology is already on-the-scene, designed with small-scale businesses and advertisers in mind. While the data small business can enter into BBM may still be limited for a machine learning tool, that barrier is overcome when combined with the data of many advertisers.
Acquisio’s Bid & Budget Management
As we’ve already described, ML learns from available data, creates an output, then learns to get better and better. When used for bid and budget management (BBM), an ML tool will constantly reassess data to make the best bid and efficiently use the campaign’s overall budget.
That’s exactly what Acquisio’s BBM does, using more than 20 algorithms at once to get the most out of your campaign’s budget. BBM makes multiple decisions a day.
For just one account, Acquisio’s BBM can make about 157 decisions or more in a single day. To put things in perspective, that’s the same as one person making a decision every ten minutes in a 24-hour day. Obviously, smaller companies don’t have that kind of time their hands, which is what makes Acquisio’s BBM so effective.
The numbers prove it. When using Acquisio’s BBM, you’re 3.6 times more likely to spend your daily budget and your average cost-per-click can decrease by up to 40%. Best of all, the more you use our BBM tool, the more the machine will learn and the better your campaigns will perform.
See? AI isn’t so scary and can really give your ad campaigns a serious boost in performance! Well, not so scary until AI ‘bots take all our jobs…kidding!
Feature Image: Geralt / Pixabay
Screenshots by Chandal Nolasco da Silva. Taken January 2017.