Heard from Google, the biggest trends impacting PPC are privacy and automation

We’re now well past this year’s Google Marketing Live event which was hosted in June in San Francisco. By now you’ve most likely heard about all the latest new betas and product changes – the “what” of what was announced. I myself am most excited about the additional controls that are coming to automated bidding. But what most media did not cover as much is the thinking behind the new launches. In other words, how did Google executives talk about the industry and why the new features address the biggest challenges advertisers face.

I believe taking a moment to reflect on some subtle cues of how the announcements were delivered can help us glean insights into Google’s inner workings. What is the ads team thinking? What might that mean for what they will build next? And what does it mean for us PPC pros as the landscape of Google Ads capabilities is constantly shifting?

I truly believe in the value of reading between the lines because even in my days as a Googler, I simply couldn’t know every detail of what Google Ads was doing, yet during Q&A sessions at conferences like SMX, I was expected to have answers. Often times, I had the answer because I knew how the product and engineering leaders thought, and what motivated them. All I had to do was connect the dots.

So let’s take a look at some of the statements made by Google executives at Google Marketing Live 2019 that I found interesting and what I think they may mean for the industry.

Greatest businesses in the world are founded on user trust

Prabhakar Raghavan, SVP of Google Ads, focused on the need for privacy, a growing concern among regulators. We’re all still catching our breath from the major changes we had to make to our websites in the past year with GDPR in Europe but this may just have been the beginning. In fact, US-based advertisers who didn’t worry about GDPR will almost certainly have to think about the impact of the California Consumer Privacy Act (CCPA) which goes into effect January 1, 2020.

Raghavan said that consumers have redefined their expectations and now expect to be able to seamlessly move across the web and across devices while having a personalized experience and at the same time have their privacy protected.

So Google is working on ways that they can continue to deliver relevant ads while using the least amount of user data says Raghavan. These are difficult problems to solve and at last year’s GML event we got a glimpse into the type of technology Google is building to solve these types of problems. For example, double-blind encryption technology lets multiple parties contribute data into a system that joins it together but where neither contributing party can get any personally identifiable data back out.

Raghavan says that the greatest businesses in the world are founded on user trust and Google obviously wants to be one of the world’s greatest companies.

One of the things you may have heard me repeat more than once is that we can make automated bidding based on machine learning (like tCPA and tROAS) better if we give it better signals about our business. It was summed up really well in a post recapping my session at SMX Advanced where I said something to the effect of: “We must focus on giving the machines the right goals in order to train them correctly.” But business data about conversions is usually about customers so sharing it with a third party like Google requires a lot of care to remove personally identifiable data.

The bottom line on privacy

As privacy concerns mount, and search engines take it more seriously, advertisers will find it more challenging to bring their data about what drives their business into the engines. We already saw customer match being scaled back due to privacy concerns related to unscrupulous advertisers submitting lists of users whose permission they lacked. Without this data, the machine learning can’t learn about meaningful signals and that means results from strategies that rely entirely on the engines will be sub-par to those that have found a way to combine internal ML with that of the engines.

I expect we’ll see more ways to bring our data into the engines through Azure from Microsoft or Ads Data Hub from Google. Unfortunately, it seems unlikely that we will be able to use technology from one engine to inform decisions on another engine (e.g. use Facebook Ads audience data to better target those users when they search on Google). To achieve that, third-party tools will gain importance.

The cloud is dead

To say that the cloud is dead seems like a crazy statement, right? I would have said so myself… after all, everything is moving to the cloud. What is not to like about having a supercomputer at your disposal to do things our own devices simply can’t? Privacy is the answer.

As powerful and useful as Amazon Alexa is, many people simply don’t want to be listened to all the time. And now that Echo devices usually have cameras, the creepiness factor of being watched constantly only goes up. But it’s thanks to the power of the cloud that Alexa can make sense of even my three-year old’s questions.

The bottom line on the future of the cloud

Part of the answer according to Google is federated learning, a way of doing machine learning where the user’s private training data never has to go into the cloud. There’s still going to be a cloud, but new ways have to be invented to give our own devices the capability to do things locally so that all private data can be kept secure locally. We may also see terminals like echo devices and nest devices become more powerful again. Whereas we had a trend towards doing more processing in the cloud, now we may start to see a reversal caused by privacy concerns.

Creating a great ad is hard

This was said by Nicky Rettke, director of product management for YouTube Ads. Creating a great ad is one of the most common challenges Google hears from advertisers. And while she’s talking about YouTube, the same holds for search ads as well. We have an audit tool in Optmyzr (my company) and one of the structural checks it can run on accounts is to look for excessive usage of the same headlines or descriptions across many ad groups. I’ve seen accounts spending well in excess of $1 million per month on Google Ads where thousands of ad groups all use the same headline.

Mike Rhodes, a PPC agency founder and smart friend of mine, said that perhaps it’s because if advertisers ran many different variations across their account, they’d find it harder to update all those ads when a new directive came in from the company’s branding team, or when new promotions were launched.

Regardless of the reason, Nicky’s on to something when she says that creating ads, let alone “great” ads is not usually top of mind for advertisers. Yet when I asked PPC pros during a recent #ppcchat on Twitter what they were least likely to trust to automation, they said it was creating ads. So it’s a task the humans often skip, and they’re not willing to let the machines help them. Quite the conundrum.

The bottom line on writing better ads

Google knows humans are too busy to write great ads at scale. Yet humans don’t believe ML can do that job for them. What we’ll see are more hybrid solutions where the machine provides suggestions and makes it easy for the human to edit and deploy them at scale. RSAs are another good example: the humans provide the machine with relevant options to choose from but the engine’s ML has the freedom to combine those human-suggested elements in whatever way it believes will create the most relevant experience for the user.

Don’t ask ‘if’ automation will disrupt your business, but rather ‘when’

This was said by Todd Rowe, global managing director for Google Marketing Solutions. That same sentiment was expressed by Ginny Marvin during her keynote at SMX Advanced in June. The reality is that ML gets better as it gets access to more data and as computing power continues to rise.

Todd believes there’s about a two-year time frame before new technology, like automation in PPC, will be disruptive. That means advertising professionals have roughly two years to figure out how they will work with a new technology. If they wait longer, that new technology may cost them their livelihoods. Dire, right?

Here’s the thing though… we don’t have to be the victims of automation. We can use it to build better agencies and stronger PPC teams.

Thinking about the impact of automation on PPC has continued to evolve as has my own thinking because part of what PPC pros need to do is create their own automation.

Todd makes a similar point and says that agencies need to think of how to automate their agency process.

The ad engines build incredibly powerful automations using the latest in machine learning. Most advertisers simply can’t compete and build a better automation, so rather than compete, they should determine how to complement the technology. I think the answer is “automation layering.”’

In one example of automation layering, the engine handles bidding using target CPA Smart Bidding and the advertiser layers on their own automations, even simple ones like automated rules and alerts that let them know when Smart Bidding is starting to fail due to some unexpected factors affecting conversion rates, like a flash sale or an outage affecting conversion tracking.

The bottom line on PPC in an automated world

Automation is here to stay and the PPC pro’s role will change in the next two to five years. Even some of the most successful practitioners are delivering great results with simple automations of their own because for every simple but time-consuming task they automate, they gain time to experiment with all the new stuff Google keeps announcing and they get to the head of the pack and become the sought-after thought leaders in PPC.

Conclusion

I learned a tremendous amount at Google Marketing Live and only wish I’d had more time to attend more sessions so I could have shared more in this post. Tools and features aside, the biggest trends we heard about at the event are about privacy, machine learning and how humans fit into this ever-evolving picture.