What Is the Role of Machine Learning in Paid Search Marketing?

Machine learning is now incorporated into the algorithms of all modern ad platforms. Successful campaign management necessitates an understanding of the machine learning in each ad network.

This Ask the PPC question from Chhote Lal in New Delhi is crucial for account managers and those who report to them:

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

This column will teach you:

What exactly is machine learning?
What role does machine learning play in paid search campaigns?
How to optimize for machine learning in paid search.

Because the question was specifically about search, we’ll concentrate on search-first applications.

What exactly is Machine Learning?

Machine learning is used to train algorithms to process data. The more data it has, the faster it will figure out what to do with it.

The algorithm can assign different weights to different data points. It is critical to comprehend how data points are valued.

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

Knowing what you have control over is critical to your success when collaborating with ad network machine learning.

The learning period is another critical factor (and that the algorithm is given enough time to process the data points).

What Role Does Machine Learning Play in Paid Search Campaigns?

Almost every aspect of paid search is affected by machine learning. Any significant change can have an impact on how the algorithm processes your campaign.

Among the modifications are:

Bidding and Budgets: Significant changes to budgets or bidding strategies.
Audiences: Changing or eliminating targets
Creative: Changing or adding creative generates a new version of the ad that does not have access to the old ad’s statistics.
Campaign status: When campaigns are paused, the learning period is reset.

It’s important to note that manual campaigns aren’t as affected by these changes; however, running purely manual campaigns is becoming increasingly difficult.

Running a manual campaign means foregoing the 60+ signals that ad networks use in their smart bidding.

These signals are used to adjust bids based on the bidding strategy chosen and the budget provided.

Furthermore, while the jury is still out on whether expanded text ads (ETAs) or responsive search ads (RSAs) perform better, RSAs tend to capture a larger share of the impression share.

Machine learning isn’t always a conscious decision. Keyword matching and audience tagging are carried out in the background and are based on historical data.

Native audiences (in-market, affinity, etc.) are based on an algorithm learning that people who complete one action are more likely to complete another action/have other traits in common.

When you ask the ad platform to find “similar” audiences to an uploaded list/website visitors, you’re using the seed audience to help the ad platform understand which prospects are valuable to you and which aren’t.

The likelihood of profitable outcomes, as well as real-time user behavior, influences keyword matching, and close variants.

The algorithms are now smart enough to recognize if a user is bilingual and will allow ads to be displayed in their other language.

How to Optimize for Machine Learning in Paid Search

When one has empathy for paid search machine learning, it is much easier to optimize.

The most important mechanic is to respect learning periods and to avoid accidental resets.

If you need to scale a campaign, for example, allow two weeks between major budget increases.

If you need to slow (or stop) your campaign, lower the budget instead of pausing to avoid resetting the learning period.

Negative keywords and audiences can assist ad platform algorithms in determining which concepts and behaviors to allocate budget to (and which to avoid).

This is the most effective method of influencing machine learning and should be included in all paid search accounts.

Machine learning tools such as conversions and conversion values are underutilized. They are the simplest way to communicate with the paid search algorithm and allow you to observe user behavior without requiring the ad channel to assign a value to the action.