There aren’t many industries left where robots don’t coexist with humans. We like to believe that we’re still somehow always ahead of robots. While they may be smarter and faster, human traits like creativity and empathy are features that machines have only had in sci-fi.
But if our human mind is grown from the process of learning and experiencing, then theoretically, it might be possible for machines to learn as well. In the advertising industry, we’ve been using ‘robots’ to deliver personalization better than humans can for the past few decades.
So if our digital intelligence grows and artificial intelligence takes over marketing activities, does it mean we’ll all be replaced by robots?
Artificial intelligence makes a marketer’s life easier. Companies offer AI-based instant logo design (LogoJoy), robots that write PR materials (PingGo) or even ones that produce an agency’s showcases (Saatchi & Saatchi). A recent announcement by Coca-Cola also indicates that they want to use bots to create music for ads, write scripts, post a spot on social media, and buy media – implying that the AI advertising revolution seems closer to reality than ever.
But the scale of changes that AI has brought to digital advertising can seem like it’s taking away our human workforce. When robots already outperform humans, all that we have left is our knowledge and experience to make us competitive against AI and robotics. Soon, they’ll be smart enough to learn, adapt and grow just like the human mind.
Is it finally time for technology to take over our jobs?
Analysts vs. self-learning algorithms: Recognizing unpredictable sales peaks
Data is everywhere, but often it is only a resource for success if used correctly. The process of analyzing data can be extremely time-consuming and doesn’t promise any results. The more data we have, the harder it is for analysts and marketers to understand, process and make conclusions.
But nowadays, machines can be taught to read data, learn from images and video, or analyze almost any kind of data streams to find patterns. By using these tools, a marketer’s life becomes simpler.
Let’s take a look at the e-commerce industry. There are obviously recognizable sales peaks which humans can predict. We know that Black Friday brings in incredible sales growth (campaigns perform over 100% better than the average on Black Friday). We know also that Tuesdays and Wednesdays have higher conversions than other days of the week (even 40% more than on Saturday).
This general knowledge helps us to plan advertising campaigns and set specific parameters when we decide to pay more for an impression or prepare special creatives.
But real audiences don’t work under these simplistic constraints all the time. Their buying patterns can be extremely specific and combine multiple criteria. That’s why digital marketing today is all about the ‘segment of one’ and how ads are marketed to an individual with more personal interests and desires in mind.
This is when we get into unpredictable sales peaks that humans just can’t predict: shopping for a loved one’s birthday present or planning a family event. Humans are not able to notice the changes in an online buyers’ behavior, but robots are not only aware of these patterns – they can be trained to spot them immediately.
In personalized retargeting, algorithms based on deep learning – a highly innovative branch of AI methods that imitates the human brain – can recognize sales peaks like humans do, but they also notice hard-to-predict patterns and react quickly to better achieve goals. Moreover, machines don’t sleep, which allows them to observe the market 24/7 and adjust activities to even the smallest change out there.
The most powerful algorithms are already capable of answering millions of requests per second, which also includes the complex process of request analysis and bid estimation. This is obviously much more than any human could ever analyze.
As more and more tasks are run by computers instead of analysts, this gives marketers the time to innovate and grow their brand, rather than worry about how to analyze the data and make a decision that will influence millions of customers at a time.
Media planners vs. algorithms: Quickly reacting to users’ needs
The process of creating media plans hasn’t fundamentally changed for years, but the number of indicators media planners need to analyze has exploded. Today, 2.5 quintillion bytes of data is produced every day and according to IDC, less than 0.5% of that data is ever collected, analyzed and used.
Meanwhile, AI tech is revolutionizing the planning and buying process, doing work in digital media, as well as traditional broadcast and out-of-home media. Day-to-day activities that form the backbone of any media agency: reporting, auditing, spot-checking, etc., can be fully automated to help specialists focus on strategy and creativity.
With a paradigm shift into machine intelligence, we can derive insight from, and act upon, rapidly expanding data sets we collect. In personalized retargeting, inter alia, decisions about products that should be shown on ads are typically made in less than 10 milliseconds, faster than it takes to blink a human eye.
Incorporating self-learning algorithms has made it possible to analyze people as individuals, rather than ordinary segmentation into predicted groups. It allows marketers to buy media that can appeal to a user’s behavior proactively, instead of reactively. There is also no more the matter of where a particular ad is placed, but to whom the banner is shown.
Machines can follow a target group and tailor ads to the behavior and preferences of a user in an ultra-precise way that humans just can’t achieve.
Creative directors vs. algorithms: Producing video ads
Artificial intelligence is also stepping up to tackle the world of creativity. If you haven’t seen it yet, check out Saatchi & Saatchi’s film – conceived, edited and directed by machines.
McCann Erickson Japan took AI to the next level, in a creative battle by pitting the the world’s first robot Creative Director, called AI-CD β, against a human counterpart, creative Director Mitsuru Kuramoto. Both were given the task of creating an ad spot which people judged by voting.
Although the computer was able to give creative direction for commercials or draw from a database of tagged and analyzed TV commercials from the past, it appeared humanity had triumphed, as Kuramoto won 54% of the vote compared to his AI counterpart: 46%. But that number is awfully close to being even, and one could easily assume that one day the figures may be swapped.
For now, it shows that in the creative landscape, human interaction and empathy might still be what prevents us from being indistinguishable from (and perhaps replaceable by) machines.
The AI apocalypse is actually a hot subject of literary and philosophical debate. One instance of the future was imagined by computer scientist, Eliezer Yudkowsky, in a paper from the 2008 book, Global Catastrophic Risks:
“It would be physically possible to build a brain that computed a million times as fast as a human brain… If a human mind were thus accelerated, a subjective year of thinking would be accomplished for every 31 physical seconds in the outside world, and a millennium would fly by in eight-and-a-half hours.” Yudkowsky thinks that if we don’t get on top of this now it will be too late. “AI runs on a different timescale than you do; by the time your neurons finish thinking the words ‘I should do something’ you have already lost.”
To add reality to this stark future, a new PwC report has found that 38% of US jobs will be replaced by robots and artificial intelligence by the early 2030s.
Time will tell if AI can learn to be even more creative and effective than human minds and how it will influence workplaces. But for now, it will drive the growth of many new jobs – including some entirely new categories.
According to the World Economic Forum, 65% of children entering primary school today will end up in jobs that currently don’t exist; some roles will become extinct, others will be created.
What we already know about the marketing industry is that when algorithms are able to learn from data, it makes it easier for brands to understand customers on a larger, more global scale rather than just as separate, local entities.
What we humans do with this newfound power remains yet to be seen.