AI vs. Machine Learning in PPC: What’s Real?
Artificial intelligence is rapidly changing pay-per-click advertising.
Google Ads and Microsoft Advertising now use AI across bidding, targeting, campaign optimization, creative development and reporting. At the same time, PPC advertisers have access to powerful generative AI tools for research, analysis, campaign development and creative production.
But “AI” has become an umbrella term covering several very different technologies.
Understanding the difference between PPC machine learning, generative AI and other PPC automation isn’t just an exercise in terminology. These technologies are good at different things — and understanding what each one does is important to understanding how ads behave today.
PPC Machine Learning Is a Type of AI
PPC Machine learning isn’t an alternative to artificial intelligence. It’s a branch of it. Machine learning allows computer systems to identify patterns in data and make predictions without someone explicitly programming a rule for every possible situation. That’s particularly well suited to PPC advertising.
An advertising platform has enormous amounts of historical data and must repeatedly answer narrowly defined questions:
- How likely is this particular click to convert?
- What might that conversion be worth?
- How much should we bid in this auction?
- Which ad or asset is most appropriate?
- Which searches or audiences are most likely to produce additional conversions?
These are prediction problems, and machine learning is good at them.
Generative AI — including large language models and image and video generation systems — tackles a different class of problems. It’s particularly useful for working with language, images, ideas, analysis and unstructured information.
Both may fall under the umbrella term “AI”, but they’re different.
PPC Machine Learning Transformed PPC Bidding
One of the earliest and most important applications of machine learning in PPC was automated bid management.
Not too many years ago, PPC managers spent considerable time manually adjusting keyword bids. Those bids had to account for competition, devices, locations, time of day, conversion rates, seasonality and many other variables. PPC machine learning changed that.
Google’ and Microsoft’s Smart Bidding strategies can evaluate the circumstances surrounding an individual auction and determine an appropriate bid at that moment.
Current strategies include:
- Maximize Conversions
- Target CPA
- Maximize Conversion Value
- Target ROAS
Instead of assigning one static bid to a keyword, the bidding system can evaluate numerous signals and combinations of signals for each individual auction.
That’s an ideal machine-learning problem: enormous amounts of data, repeated decisions, measurable outcomes and continuous feedback. But bidding was only the beginning.
AI Is Now Influencing More Than the Bidding
Today’s advertising platforms increasingly use machine learning across the entire campaign.
Performance Max is the clearest example. Automation can influence bidding, targeting, placements, creative combinations and where advertising appears across Google’s inventory.
AI Max extends some of those capabilities into Search campaigns, including broader search-term matching, asset optimization and landing-page selection. That represents an important evolution in PPC automation.
Originally, advertisers largely defined where they wanted to compete, while automated bidding helped determine how much an individual click was worth.
Increasingly, advertising platforms can help determine both.
That can uncover opportunities an advertiser might never have identified manually. It can also give the platform considerably more freedom to explore.
The practical question therefore isn’t whether automation is good or bad. It’s which decisions we want to automate, how much freedom we want to provide, and how we’ll measure whether the additional automation is actually improving results.
That’s also why we don’t necessarily move a new campaign immediately to automated bidding. Establishing a performance baseline first can provide useful information before deciding when an ad campaign should move to automated bidding.
Generative AI Has Added an Entirely New PPC Toolset
The arrival of generative AI has created another major change — this time on the advertiser’s side of the account. This is where the last few years have become particularly interesting.
Generative AI can help with:
Campaign development. Keyword research, campaign structures, audience ideas, competitive research and landing-page analysis can all be accelerated with AI.
Ad development. AI is excellent for brainstorming headlines, descriptions, offers, messaging angles and variations that can then be refined and tested.
Creative production. Image and video generation are reducing the time and expense required to develop advertising assets.
Data analysis. Large search-term reports, campaign exports, product-level results and other datasets can be analyzed much faster, allowing advertisers to investigate patterns that previously might have required hours of manual work.
Reporting and diagnostics. AI can help identify anomalies, compare time periods, summarize changes and direct attention toward areas that deserve deeper investigation.
The important benefit isn’t simply that AI can perform tasks we previously performed manually.
It allows us to do more analysis, explore more alternatives and test more ideas with the same amount of time.
That’s potentially much more valuable than simple labor savings.
Better Data Makes the Platforms More Useful
Whether we’re talking about machine learning or generative AI, the quality of the output depends heavily on the quality of the information available to it. That’s especially important in automated bidding.
If Google is told that a submitted form is a valuable conversion, its bidding system will attempt to generate more submitted forms. But what if many of those forms are spam, job seekers, existing customers or prospects outside the company’s service area?
The bidding algorithm may be doing exactly what it was asked to do. The problem is the information it was given!
The same applies to eCommerce. Revenue is useful information, but two products generating the same revenue may have very different margins, return rates or customer lifetime values.
This makes accurate conversion tracking increasingly important as PPC becomes more automated.
Call tracking can add another valuable source of conversion information for lead-generation campaigns. Offline conversion imports can tell the advertising platforms which leads actually progressed through the sales process. eCommerce advertisers can provide increasingly detailed information about transaction value.
The more accurately we communicate business outcomes back to the advertising platforms, the more useful their machine-learning systems can become.
Smaller Accounts Need to Test Automation Carefully
Machine learning doesn’t require a giant advertising account to work. But the volume of data volume does matter.
When conversion activity is limited, there’s less information available from which to distinguish persistent patterns from random variation. That makes both learning and performance evaluation more difficult.
This becomes particularly important when automation is allowed to expand targeting as well as manage bids. A smaller advertiser can spend a meaningful portion of its monthly budget while the system explores new traffic.
Our approach test automation; it’s to test it. First we establish a useful baseline, then introduce meaningful changes deliberately to test it against that baseline. We give the system enough time and data to evaluate the result fairly. Then look at what actually happened to leads, sales, CPA, ROAS and ultimately business results.
AI Is Making PPC More Interesting
The development we’re most excited about isn’t simply that advertising platforms can automate more campaign tasks.
It’s that advertisers now have access to many of the same kinds of computational advantages.
Machine learning can make millions of small optimization decisions that would be impossible to manage manually.
Generative AI gives us new ways to analyze accounts, investigate performance, develop campaigns, create advertising assets and work with increasingly large amounts of data. Those capabilities complement each other.
The result isn’t PPC without people. It’s PPC practitioners equipped with much more powerful tools.
And we’re still near the beginning. The most interesting question for advertisers isn’t whether AI will become part of PPC advertising. It already has.
The question now is how much better we can become at advertising by learning how to use it.
