The technology behind AI in PPC

I believe artificial intelligence (AI) will be a key driver of change in PPC in 2018 as it leads to more and better PPC intelligence.

So far, I’ve 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 post on the state of AI in PPC, I’ll cover the technology of AI.

Why AI took years to matter to PPC

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, so let me give an example that doesn’t involve computing speeds since those can be a bit too conceptual. Instead, let’s apply this doubling of speed to cars, where we can more easily understand how it impacts the distances we travel and how quickly we get somewhere.

Imagine if the first car, invented by Karl Benz in 1885 with a top speed of about 10 mph, was doubling its speed every 18 months. In 1885, we could have driven that car across a typical town in an hour. After 27 times doubling its speed (the same number of times the microchip has doubled its speed since it was invented), we could have gone to the sun in about 4 minutes. And less than 18 months later, it would take just about 2 hours to travel to Neptune, the farthest planet in our solar system. (Voyager 2 did that same trip in about 12 years.)

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.

And just like the first car is not the right vehicle for a flight to Neptune, the tools you used to manage AdWords a few years ago may no longer be the ones that make sense for managing AdWords today. So let’s take a look at what AI is doing to PPC tools.

The technologies driving PPC intelligence

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.

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.

PPC intelligence through symbolic representations

Between the 1950s and 1980s, AI 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.

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.

What’s next for artificial intelligence

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,” as explained by DeepMind CEO Demis Hassabis.

The team created the AlphaZero algorithm using reinforcement learning so that it could learn to win other games besides AlphaGo. They claimed that by the end of 2017, this algorithm had learned to best humans in other games like chess and shogi in less than 1 day — a huge leap forward in AI.

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, I bet we’ll see a huge acceleration in improvements of their automation tools.

Build your own PPC intelligence

There are a lot of tools available to automate your PPC work, and multiple third-party vendors are starting to use AI and ML to provide stronger recommendations. But there are also many free tools from AdWords that are getting better every day thanks to advances in AI, like Portfolio Bid Strategies, Custom Intent Audiences, optimized ad rotation, etc.

For those willing to invest in connecting their own business data to AdWords and AI, I’m a big fan of prototyping solutions with AdWords Scripts because they 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. Our team at Optmyzr tried several ready-made systems but eventually decided that we needed more power — so we’re building our own AI.

Conclusion

I believe there are three pillars for being a successful PPC marketer in a world where AI takes over and I’ve now touched on each pillar in my recent posts:

1. Be ready for the new roles humans will play
2. Have a plan for your business, and especially focus on having the best process for leveraging AI.
3. Understand the technology so you can spot opportunities faster.

Over the coming months, I will share my own experiences with AI so advertisers ready to take the plunge will have a better understanding of what is involved in building successful companies that leverage the latest state of the art in technology, computation, and statistics.

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Opinions expressed in this article are those of the guest author and not necessarily Search Engine Land. Staff authors are listed here.

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