Automation layering: How PPC pros retain control when automation takes over

The PPC track at SMX Advanced kicked off with a keynote by Ginny Marvin where she considered the future of an industry where automation is increasingly doing more of the work humans used to do. Her message was that we can’t escape automation so we must find a way to coexist with the machines.

The topic of automation in PPC comes up a lot but I suspect that when our industry talks about the impact of automation, what is considered are mostly automations built by the likes of Google and Microsoft… disruptive (but not necessarily bad) capabilities like Smart Bidding, close variant keywords, responsive search ads, etc.

But nobody ever said that advertisers can’t be disruptors too. They too can build automations to change the game and give themselves a competitive edge.

Having to build your own automations may sound daunting but remember that they don’t have to be cutting-edge like machine learning in order to be useful. In this post, I’ll explain an easy way to get started with your own automations using the principle of “automation layering.”

Automations from the engines are better with human help

In my new book, Digital Marketing in an AI World, I explain that humans plus machines usually perform better than machines alone. This is not a new concept and one most of you have probably come across in some form or other. One specific example I used to share in presentations came from Wired in 2014 and said that, “The National Weather Service employs meteorologists who, understanding the dynamics of weather systems, can improve forecasts by as much as 25 percent compared with computers alone.”

Because of the potential for better results, PPC pros want to remain involved. They have knowledge about the business that could meaningfully impact results. Sometimes there simply is not enough data for a machine learning system to come up with the same insight. So it’s generally agreed upon that humans + machines can outperform machines alone.

Generally, we tend to translate this concept into the PPC world by saying that account managers need to work together with automations from the engines.

Automations from the engines are better with automations from advertisers

Then I started thinking about the role human PPC managers need to play for the premise to be true that humans + machines outperform machines alone. I realized that the humans in that equation could actually be replaced by machines as well, but in this case, machines that are controlled by the PPC pro and not the ad engine. PPC pros could benefit from the control (since they define the automation) and the time savings (because they don’t need to exert control manually).

So we should try to replace some forms of human control with new layers of automation and see if that delivers the same benefits as humans + machines. If we can write down the steps we take, we can teach a machine to do those steps for us. And it can be a simple rule-based approach which is much simpler to create than something based on machine learning.

The concept behind automation layering is not a new idea. In engineering, solutions can be broken down into systems that can themselves be connected to other systems. Each system accepts inputs and returns outputs and so long as there is agreement over the format of inputs and outputs, many systems can be strung together and work seamlessly together to solve more complex problems.

Likewise, an automation could interact with other automations. In PPC, let’s call this principle “automation layering.”  This is an important concept because it’s the next evolution of what PPC pros have been doing for years: using their own insights to control what Google does. But just like Google is getting ever-more automated, our control over it should also become more automated.

Let’s look at why automation layering makes sense in PPC.

Escaping automation is not an option

The reason humans worry about automations created by the engines is that we can’t escape these. They are launched at the engine’s discretion and whether we like it or not, we have to spend time figuring out how they impact our work. Given how busy the typical PPC manager is, this extra work is not something to look forward to.

Despite promising great things, the truth is that success with new automations depends on experimentation and reskilling, both tasks that require time to do well. To take an example from aviation, cutting corners with reskilling when new automations are launched can lead to disastrous results as seen with the 737-Max. Luckily in PPC the stakes are not as high, but I believe the analogy is relevant.

Automation layering for close variants

Some new automations cannot be turned off so they force us to change how we work with Google Ads. Close variants are a recent example of this type of change. In September of last year, they redefined what different keyword match types, like “exact match” mean.

Some account managers now spend extra time monitoring search terms triggered for exact match keywords. This would be a great form of human control to turn into automation layering where the PPC manager turns their structured logic for how they check close variants into an automation that does it automatically.

There are two specific ways I’ve shared to layer an automation on top of Google’s exact match keywords to keep control when they expand to close variants with similar meaning.

The first way is to simply check the performance of the close variant to that of the underlying exact keyword. If a user-defined threshold for performance is met, it can automatically be added as a new keyword with its own bid, or as a negative keyword if the performance is significantly lower. Note that close variants when used in conjunction with Smart Bidding should already get the appropriate bid to meet CPA or ROAS targets, but regardless it can’t hurt to add your own layer of automation to confirm this.

The second way is to use the Levenshtein distance calculation to find how far the close variant is from the exact keyword. It is a simple calculation that adds up the number of text changes required to go from one word to another. Every character added, deleted, or changed adds one point. Hence going from the correct spelling of my company name “Optmyzr” to the common typo “Optmyzer” has a Levenshtein distance of 1 (for the addition of the letter “e”). Going from the word “campsite” to “campground” on the other hand has a score of 6 because 4 letters need to be changed and 2 need to be added.

With a Google Ads script, we could write our own automation that turns these manual checks into fully automated ones. Because it’s an automation that we can define, it’s as powerful as the more manual human control that we used to have to put in to get the benefits normally associated with humans + machines.

Automation layering for Smart Bidding

Other automations like Smart Bidding are optional but with their pace of improvements, it’s just a matter of time before even the most ardent fans of doing PPC manually simply won’t be able to make enough of a difference that they can charge a living wage for their manual bid management services.

The machines are simply better at doing the math that predicts future conversions and using this expected conversion rate to turn an advertiser’s business goals around CPA or ROAS into a CPC bid that the ad auction can use to rank the ad against all others.

That said, remember that Smart Bidding is not the same as automated bidding. Part of the bid management process is automated, but there’s still work for humans to do. Things like setting goals and ensuring measurement is working are just two examples of these tasks.

Besides needing to dial in adjustments for seasonality, special promotions and figuring out how to connect these limited controls to business goals like acquiring new customers, driving store visits or driving higher repeat sales, there’s still the point that most companies care about profits. Despite what we may think after hearing of Uber’s $1 billion quarterly loss, the reality is that most companies don’t have hordes of cash from VCs and a recent IPO so profits are what helps these businesses grow. Curiously, Google Ads doesn’t really have a Smart Bidding strategy geared towards profits.

So it’s up to the human PPC pro to bridge that gap and perhaps add some automation layering. One way to drive towards profitable PPC is to take margins into account when setting ROAS goals.

More profitable items (the ones with higher margins) can have lower ROAS targets. Remember ROAS in Google is “conv value/cost” (i.e., conversion value divided by ad costs). Assuming the conversion value is the cart value of the sale, for an item with a better margin more of that cart value is the product markup. So a lower ROAS can still deliver a profit whereas for items with low margins, less of the cart value is the markup and hence a higher ROAS is needed to break even.

PPC pros could manually assign different products to different smart shopping campaigns with different ROAS targets but that would be tedious and time consuming, especially if the margins for existing products were to change due to promotions and sales events. A smarter solution would be to apply automation layering and use a tool or script that sends products automatically to the right smart shopping campaigns where Google’s automations could take over.

Conclusion

The engines are automating many things we used to have lots of control over because we used to do them manually: from finding new keywords, to setting better bids, to writing ads. But when the people behind the businesses that advertise on Google get a say, results can be better than if the engine’s automation runs entirely on its own.

Just like Google is adding automations, so should you. Use the concept of Automation Layering to your advantage to retain the level of control you’re used to while also saving time by letting the machines do your work.