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The Future of PPC Lies in Machine Learning: Adapt or Be Left Behind
Written by Jeff Baum, Director of Services
Last year, I conducted a webinar with Marc Poirier, CEO of Acquisio. In that webinar, we discussed the impact of machine learning on paid search campaigns. Since then, Google has announced the roll out of new ‘smart’ features such as in market audiences for search and enhanced its ‘smart bidding’ functionality by rolling out a new bid strategy called ‘maximize conversions.’ Couple these features with current features like Universal App and Smart Display campaigns and one thing is clear: The future of PPC lies in machine learning, the powerful insights it’ll deliver, and the automation benefits it provides.
This whitepaper discusses how you can adapt to this new dynamic and make you aware of a few of the new machine based features that are available in AdWords now or will be available soon.
Evolve from PPC Tactician to PPC Strategist
First off, I believe machines will not make the PPC professionals job obsolete. However, the arrival of machine learning and its ability to automate key tasks will change the nature of the role. The value PPC professionals bring going forward will be tied directly to their ability to be ‘strategic insight brokers’. Those who can see the big picture, fully understands the marketplace of the businesses they represent operates in, uncover new information that provides competitive advantage, and is able to connect PPC to the overall growth of their organization’s entire digital marketing program will win the day.
Machines can crunch data, spot trends, and execute routine tactical tasks faster than we ever could. I recently used a technological solution that automates optimizations. Within an hour, the system uncovered 1500 keywords that could be added to my account, identified close to $1,500 of spend that could be negative matched, identified hundreds of bids that could be raised to better position my keyword list, and automatically paused underperforming ads.
Although these routine tasks were completed by a machine in minutes vs. hours didn’t mean I was any less busy. What it meant was that I could focus on higher level priorities like data analysis so I can better understand overall performance and translating those learnings into strategy, and planning the next set of tests I wanted to run, and investigating new platforms to use to expand my client’s PPC program.
Here’s a few tips that can help you both become more strategic and take performance to the next level:
- Have a firm grasp on the metric most important to your stakeholders and focus like a laser beam on it. For instance, I have an account where both lead volume and good CPA appear to be of equal importance. However, when I push my client to define the single most important metric to them, they’ll always come down on the side of growth.
- Understand all obstacles that could impede meeting or exceeding your stakeholder’s most important metric or goal. Is it competition? High CPC’s? Low conversion rates? Having a firm understanding of the obstacles will help you plan for those contingencies and allow you develop strategies to help you overcome these obstacles.
- Intimately know the underlying drivers of performance so you make more informed strategic decisions. Leading indicators such as interaction rate and conversion rate are great leading indicators of whether you’re going to achieve the right results.
Throughout my career, I’ve been prone to focus on the mechanics of operating a PPC account at the expense of taking a step back to see the bigger picture. Machine learning and the automation it provides has helped grow my accounts because I have more time to focus on strategy. In the process, I’ve increased the value I bring to clients by being able to provide better advice and more new ideas to the table.
New Machine Based Features:
Below are some AdWords machine learning features that can be taken advantage of both now and in the near future.
Google Smart Bidding: Google smart bidding consists of the target CPA, target ROAS, maximize conversions, and enhanced CPC bidding strategies. The advantage of machine based bidding is the ability to analyze hundreds of ‘signals’ that can help better understand your audience and their potential to convert. Google has vast amounts of data at its finger tips that’s processed in real time. The AdWords system optimizes bids based on these factors simultaneously to place advertisers in auctions most valuable to them.
The key when using smart bidding is to not give up on it too soon. The system needs time to learn, which means in the beginning, performance can be a bit inconsistent. As the system learns, you should start to see performance improvements and eventually growth in volume and efficiency of CPA. If your tests are well thought out and controlled, you should be able to test these new strategies out with minimal risk to your account.
Smart Display Campaigns: This type of display campaign is completely controlled by machine learning. Simply enter in your account’s target CPA and budget, provide ad assets such as a headline and images and the system learns and optimizes towards your goals. Like smart bidding, it’s important to let the system learn by letting the smart display campaigns accrue enough performance and traffic data. The more data the campaign has, the easier it is for Google’s machines to optimize and grow performance.
In-Market Audiences: This feature has been around for a couple of years for the display network and will soon roll out for search. Once again, Google identifies signals such as prior search and conversion history, and the content being consumed to identify those most likely to convert or are in the final stages of the buying cycle. The only way to identify these types of users is through machine learning and the machine’s ability to crunch vast amounts of data in real time on an auction by auction basis.
Ads: Machine learning will forever change ad copy. Because of machine learning, the PPC ad copywriter will evolve into an ‘ad messaging strategist’. Machines will get smarter at multi-variate testing, meaning we’ll just need to provide ‘concepts’ for headlines, body descriptions, etc. The machines will mix and match these ‘concepts’ into readable copy. Our job will be to determine what assets drove successful outcomes and which ones didn’t. Based on a proper determination of the outcome, the ‘ad messaging strategist’ will need to build and iterate strategies that capitalize on what assets worked and what didn’t. There won’t always be clear cut answers so us mere mortals will need to determine the way forward, not the machine.
This is an exciting time for us in the PPC industry. Machine learning has afforded the opportunity to learn more and execute faster. However, the one thing machines can’t do as well as humans is translate all the data derived from PPC and turn it into coherent strategies.
Taking a more strategic approach will increase your value. The ability to take the big picture and connect it to results will help increase your influence and help you progress in your career.