Artificial Intelligence (AI) has been around for a long time, so why are we only just now exploring its applications for PPC (Pay Per Click)?
So far, it has been discussed the roles humans will play when PPC management becomes nearly fully automated and six strategies agencies can take to future-proof their business. In this final state of AI in PPC, we’ll cover the technology of AI.
AI has been around since 1956, and PPC has existed since the late 1990s. So why did it take until now for AI’s role in paid search to become such a hot topic in our industry?
It’s because we’ve recently hit an inflection point where, due to the exponential nature of technological advances,
we’re now seeing improvements that used to take years happen in weeks.
What’s driving this is the exponential growth explained by Moore’s Law, the principle that computing power doubles approximately every 18 months. The outcome of exponential growth is hard for humans to grasp and because computing speed has already doubled 27 times, every extra doubling leads to new capabilities that are beyond imagination.
What exponential growth means for PPC
So, if we’ve reached the point of #PPC automation today where humans and computers are about equally good, consider that the pace of technological improvement makes it possible for the machines to leave humans in the dust later this year. That’s why it’s worth thinking about the roles humans will play in the future of PPC.
The tools we used to manage AdWords a few years ago may no longer be the ones that make sense for managing AdWords today.
Just like you want to know what your employees are capable of by interviewing them before hiring them, you should understand a technology’s capabilities (and limits) before adding it to your toolkit.
So let’s see how Artificial Intelligence works in PPC.
a.PPC intelligence through programmed rules
Before the advent of #AI as a research field in 1956, you could make a machine appear “intelligent” by programming it to deliver specific responses to a large number of scenarios. But that form of AI is very limited because it can’t deal with edge cases, of which there are invariably many in the real world.
In #PPC, this would be akin to using Automated Rules to write rules for every possible scenario an account might encounter. Rules are great for covering the majority use cases, but the real world is messy, and trying to write rules for every scenario is simply impossible.
b.PPC intelligence through symbolic representations
Between the 1950s and 1980s, #Artificial_Intelligence evolved into using symbolic systems to be able to take heuristic shortcuts like humans do. By framing problems in human readable form, it was believed the machines could make logical deductions.
Here’s a PPC problem: you’re adding a new keyword, but you don’t know the right bid to set because there is no historical data for it. By teaching the machine concepts like campaigns and keywords and how these relate to each other, we are providing it with the same heuristics we use to make reasonable guesses.
So the system can now automate bid management and might set a similar bid to other keywords in the campaign because it knows that campaigns tend to have keywords that have something in common.
c.PPC intelligence through statistical learning methods
The type of AI that is responsible for a lot of success in PPC today is based on statistics and Machine Learning to categorize things. Quality Score (QS) is a great example; Google looks at historical click behavior from users and uses machine learning to find correlations that help predict the likelihood of a click or a conversion.
By having a score for how likely it is that each search will translate into a conversion, automated bidding products like those offered inside #AdWords can “think” through many more dimensions (like geo-location, hour of day, device, or audience) that might impact the likelihood of a conversion than a person could.
Thanks to the massively increased computing power available today, these systems can also consider interactions across dimensions without getting “overwhelmed” by the combinatorial nature of the problem.
AI systems getting a lot of attention today, like AlphaGo Zero, are no longer dependent on structured data and can become “intelligent” without being “constrained by the limits of human knowledge” .
The team created the AlphaZero algorithm using reinforcement learning so that it could learn to win other games besides AlphaGo.
Reinforcement learning uses massive computing power to run lots of simulations until it starts to recognize actions that lead to desirable outcomes. It can be applied to games because there is a clear outcome of “winning” or “losing.”
When Google figures out what it means to win or lose in the game of AdWords, we’ll surely see a huge acceleration in improvements of their automation tools.
There are a lot of tools available to automate your PPC work, and multiple third-party vendors are starting to use #AI and #ML (Machine Learning) to provide stronger recommendations. But there are also many free tools from AdWords that are getting better every day thanks to advances in AI.
For those willing to invest in connecting their own business data to AdWords and AI, prototyping solutions with AdWords Scripts provide a lot of customizability without requiring a lot of engineering resources. Unfortunately, simple scripts you write will fall into the weakest category of AI, where PPC intelligence is achieved through hard-coded rules.
But when you get a bit more advanced in your scripting abilities, you can use Google Cloud Machine Learning Engine to start enhancing your own automations with modern machine learning techniques.
The benefit of an out-of-the box solution like this is that you don’t need to learn many types of different models. But that’s also the downside because you won’t get total control over how you set criteria and thresholds to get results that are usable.
We believe there are three pillars for being a successful PPC marketer in a world where AI takes over: