How Does Machine Learning Work in Paid Search Marketing?

All modern advertising platforms now integrate machine learning into their algorithms. Managing successful campaigns requires an understanding of machine learning in every ad network.

This question Ask the PPC question, of Chhote Lal in New Delhi, is an important question for account managers and those they depend on:

“How does Google’s machine learning work in paid marketing?” “

In this column, you will learn:

  • What is machine learning?
  • How is machine learning taken into account in paid search campaigns?
  • How to optimize for paid search machine learning.


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Since the question was specifically about research, we’ll focus on research-oriented uses.

What is machine learning?

Algorithms are taught to process information through machine learning. The more data he has, the faster he will learn what to do with that information.

Different data points can have different weights in the algorithm. It is important to understand how data points are evaluated.

Data points can be completely objective, subjective, or a hybrid of human interaction and pure algorithmic learning.

Knowing what you can control is critical to your success when you partner with machine learning ad networks.

The other critical factor is the training period (and the fact that the algorithm has enough time to process the data points).


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How is machine learning taken into account in paid search campaigns?

Machine learning has an impact on almost all paid searches. Any major change can influence the way the algorithm treats your campaign.

These changes include:

  • Auctions and budgets: Drastic changes in budgets or auction strategies.
  • Public: Changing targets or excluding targets.
  • Creative: Editing or adding a creative creates a new version of the ad that will not have access to the statistics of the old ad.
  • Campaign status: Pausing campaigns resets the learning period.

It is important to note that manual campaigns are not as impacted by these changes, however, it is increasingly difficult to run purely manual campaigns.

Running a manual campaign means opting out of the 60+ signals ad networks are tapping into in their smart auctions.

These signals are used to adjust the bids according to the chosen bidding strategy and the given budget.

Additionally, while the verdict is yet to be established on the performance of Wide Format Text Ads (ETAs) or Responsive Search Ads (RSAs), RSAs tend to get a higher share of impressions.

Machine learning is not always an active choice. Keyword matching and audience tagging happens in the background and is based on historical data.

Native audiences (in market, affinity, etc.) are based on the algorithmic learning that people taking one action are likely to take another action / have other related traits.


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When you ask the ad platform to find audiences that are “like” a downloaded list / website visitors, you are using the seed audience to help the ad platform understand which leads you find interesting and which ones. are not.

Keyword matching and close variations are influenced by the likelihood of profitable results, as well as real-time user behavior.

The algorithms are now smart enough to know if a user is bilingual and will allow their other language to trigger ads.

Screenshot from, September 2021

How to optimize for paid search machine learning

It’s much easier to optimize when you have empathy for paid search machine learning.


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The most important mechanism is to honor the learning periods and to avoid accidental resets.

If you need to scale up a campaign, for example, be sure to budget for two weeks between each major budget increase.

If you need your campaign to slow down (or stop), reduce the budget instead of pausing it so you don’t reset the learning period.

Keywords and negative audiences can help ad platform algorithms understand which ideas and behaviors to budget (and which to avoid) to budget.

It’s the most powerful way to influence machine learning and should be a part of all paid search accounts.

Conversions and conversion values ​​are underutilized machine learning tools. They are the easiest way to communicate with the paid search algorithm and allow you to see user behavior without asking the ad channel to rate the action.


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Take away food

Machine learning impacts almost every element of paid search, and understanding how to teach the algorithm is crucial for PPC’s success.

More resources:

Have a question about PPC? Submit through this form or tweet me @navahf with the hashtag #AskPPC. See you next month!

Featured Image: Paulo Bobita / SearchEngineJournal


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Brandon D. James