PPC and machine learning: Where do we draw the line on automation?

PPC and machine learning: Where do we draw the line on automation?


One of the most hotly debated (and least understood) topics in the PPC world, Automation, is a behemoth to take on. Functionally, we have 2 distinct levels of automation that are happening simultaneously on both PPC platforms and in the processes used to manage PPC media. Our industry is at an inflection point, and we will not simply become better marketers by automating everything with blind faith in machines. Rather, we must take an intelligent approach to automation and how we think about the future roles that PPC professionals will need to shift to.

A behemoth of a topic obviously requires some industry heavyweights to take it on at SMX Advanced 2019, so it was no surprise to see Frederick Vallaeys of Optmyzer, Inc. and Brad Geddes of AdAlysis.

Before we dive into the insights shared during this session, it’s important to note one key theme that was prevalent throughout. That theme is the idea that we don’t need to automate everything. While it can be easy to imagine a fully automated future where, at the touch of a button, everything falls into place – that is simply not the reality of the world we are facing. Indeed, the digital transformation is particularly challenging for this exact reason – some things should be automated and some things absolutely shouldn’t – but where do we draw the line?

Insights From Frederick Vallaeys

Hot off the press, Fred just released an excellent new book, entitled “Digital Marketing in an AI World: Futureproofing Your PPC Agency” which I started reading on the flight to Seattle, and honestly, couldn’t put it down! Many of the highlights of his book were featured during his session.

As one of Google’s early employees, Fred has been around long enough to see how the world of PPC has evolved and has, himself, provided a number of functional automation solutions to the wider PPC community.

Fred kicked off his session talking about the key reasons to automate PPC processes, specifically to: 

  • Save Time (Reducing costs to grow the bottom line) 
  • Improve Quality & Reduce Churn
  • Allow for Scale ( in order to make more money)

These reasons, of course, are not exclusive to the world of digital marketing but are key considerations for automation across all industries. The key idea here is, just because we can automate something, doesn’t necessarily mean we should. In order to prioritize our automation work, we need to leverage a Degree of Impact framework. Here’s one that Fred shared during his presentation:

machine learning (ML) based future.

Next, Fred took us to an advanced place as we started to think about the nature of automation layers interacting with each other. This is a key concept to keep in mind with every automation, and we need to ask ourselves:What could the unintended consequences of this automation be, and what could it interact with?

One particular example looked at the relationship between bidding automation and the selected Attribution model applied to the associated goal in Google Ads:

AdAlysis team found that “The average agency spends 8 days a month creating reports” – that’s 26% of our time dedicated to a repeatable task!

Brad kicked off by talking about the various tiers of automation that can exist and their respective core benefits: 

Gartner Hype Cycle specific to the world of PPC Automation, I believe we would be descending rapidly from the Peak of Inflated Expectations, and adjusting to the rapids within the Through of Disillusionment. As mentioned at the outset of this article, we are at an inflection point in the world of PPC. It is now when we set forth towards our ML-driven future and we must all embrace the future of intelligent automation, albeit cautiously and with an understanding of the potentially massive impact of unintended consequences.


About The Author

spoulton.com. Simon was also a finalist for Search Engine Land’s “Search Marketer of the Year 2018”, and was named one of the top 25 Social Business Leaders by IBM and the Economist in 2015.

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