TMCnet Feature
August 15, 2022

How Machine Learning Can Improve Your Google Search Campaigns

Writing ads was a tedious task that most people found too difficult to overcome before AI became more popular. When you just have a few hundred characters to work with, it can be challenging to create a compelling ad that will get people to click, let alone convert.

Since 2021, Google (News - Alert) Ads has undergone several significant improvements, with the AI integration being by far the most revolutionary.

Now, it’s easier for people to get started with Google Ads. Moreover, it’s easier to succeed.

Additionally, Google has been introducing an increasing number of automated campaign choices that claim to enhance the effectiveness of your campaign and enable better outcomes. The user's location and past search history are just two of the variables taken into account by Google's machine learning system. Without a doubt, you really cannot accomplish all of it manually.

However, some marketers are hesitant to give Google too much authority over their campaigns. They have generally argued, and with good reason, that Google shouldn't be trusted since its goals often diverge from those of the marketer.

1. Flexible Search Ads

These are Google Ads' newest and largest search advertising formats. Additionally, responsive search advertisements give you the following features, making them the most adaptable.

·         Instead of the usual two headlines found in Expanded Text Ads, there are three.

·         Instead of one 80-character description, there are two 90-character descriptions.

·         Google rotates your headlines and descriptions to identify winning combinations in automated testing.

2. Ad Strength

The Ad Strength statistic evaluates the value, standard, and diversity of your advertising material. Having original material that connects with your audience is important. You can enhance the effectiveness and quality of your ads by doing this.

Furthermore, you can see an Ad Strength indication whenever you are generating or revising an advertisement. The dynamic change occurs when you complete the assets.

On the side of Google Ads, there are recommendations that can help you strengthen your ads.

By considering this feedback, you can take the necessary actions to improve Ad Strength, which will ultimately result in stronger, higher-quality advertisements.

3. Optimization Score Metric

The performance of your Google Ads account is measured by the Optimization Score statistic.

This real-time statistic is based on the following variables and ranges from a minimum optimization score of 0% to a maximum optimization score of 100%:

·         Your campaign settings, status, and statistics for your account.

·         Recent recommendations history.

·         Trends developing in your ads.

Google Ads analyzes your campaigns using machine learning and artificial intelligence to see whether they are fully optimized. To improve the optimization score metric, the system recommends actions. The suggestions that have the biggest effect on performance will be given more weight.

Automated Campaigns and Automatic Bidding

As you can now see, AI made it easier to produce successful advertisements. However, this innovation doesn't end there.

Through Ads automation, AI in Google Ads can handle a lot of the labor-intensive activities in your campaign, saving you countless hours that would have been spent on boring, repetitive work.

The following three uses of automation in Google Ads are:

4. Automatic Biddings

You no longer have to be concerned with manual bidding. By making rational bid adjustments, you can use AI to reduce your guesswork and improve your campaign.

The following automatic bidding algorithms are available to choose from:

·         Maximize Clicks

·         Target (News - Alert) Impression Share

·         Target CPA

·         Enhanced cost-per-click (ECPC)

·         Target ROAS

·         Maximize Conversions

Each of these tactics has been carefully created to assist marketers in achieving a specific advertising objective.

5. Smart Campaigns

Do you want to constantly improve your pay-per-click (PPC) campaigns? By using Google Smart Campaigns, you may achieve that goal using AI.

These AI-driven marketing offer many noteworthy advantages:

Automatic upkeep - Set up the account for 15 minutes, then hand over the control to Google. You can concentrate on other things because it will handle keyword selection, bidding, and targeting for you.

Generation of text ads - Just three lines should be written about the good or service you're promoting. After that, copywriting will be handled by Google Ads.

No need to pay third parties - To manage your own Smart Campaigns, work with Google Ads. As a result, you can save time and money by not having to pay agencies or PPC specialists.

6. Automated Ad Extensions

Last but not least, Google Ads provides extensions, which have a significant impact on driving traffic, clicks, and conversions.

Wherever it deems necessary, Google Ads will automatically add extensions. This platform can add an extension enhancing the likelihood that someone will click on your ad if it is activated for a specific keyword.

The best part is that you only have to pay for these extensions when customers click on them.

How to Optimize Your Google Ads Account for Machine Learning

Say you've decided to start machine optimizing your Google Ads campaigns. What steps would you take to achieve this?

The fascinating aspect of this situation is how some current recommendations go against accepted best practices.

Hence, for that reason, we've compiled a list of the most noteworthy recent best practices:

1. Simplify Your Account Structure

Previously, the advised best practice called for a granular account structure. Based on themes, we would divide campaigns and keywords into smaller categories. In truth, single-keyword ad groups, often known as SKAGs, weren't unusual.

However, Google currently recommends that you streamline your account organization and incorporate keywords into bigger ad groups and campaigns. The machine learning algorithm needs data to work with, which is the main cause of this. Additionally, it might produce better results the more data it has.

Ad groups and campaigns with low impressions, clicks, and conversions will probably not be optimized effectively as Google examines some data at the ad group and campaign level. Google advises that an ad group receives at least 3,000 weekly impressions, and a campaign receives 30 conversions per month.

2. Remove Unnecessary Splits

Similar to the previous point, many advertisers divided campaigns and ad groups based on several principles. As an example, they would divide ad groups or even campaigns for various match kinds (wide, phrase, exact) and use separate desktop and mobile campaigns.

Since the machine learning system should proactively identify the best-performing ads and optimize in accordance with them, many conventional splits are now seen as unnecessary.

3. Do Not Avoid Using Broad Match Keywords

The ideal technique up until recently was to either use a broad match modifier or stay away from broad match keywords whenever possible.

In fact, a lot of advertisers wanted to limit their targeting to the precise search queries they had selected. These users have expressed disappointment with Google's removal of more precise search selections or the fact that exact matches are no longer accurate. Additionally, using broad match keywords carelessly might become problematic, particularly in English-language ads.

On the other hand, 15 percent of user searches in 2017 had never been seen before, according to Google. These new search terms wouldn't even be reachable with precisely studied exact match keywords.

4. Try Dynamic and Responsive Ads

It makes sense that some advertisers still feel uneasy about handing Google control of their ad material.

Essentially, dynamic advertisements have been around for a while, but some advertisers haven't found much success with them. Dynamic ads, for instance, sometimes simply eat up traffic from other campaigns.

Conversely, responsive search ads are a more recent development, and it appears that Google may eventually make them the standard ad format. We're starting to look into how to use them more effectively because it appears that they'll need a slightly different approach than standard search ads.

AI is Changing the Game with Google Ads Automation

Despite widespread fears about artificial intelligence, businesses and consumers benefit from it when it comes to advertising. With the help of this technology, companies can engage with existing consumers and draw in new ones more easily.

The challenge for marketers rises as digital marketing develops, markets change, and there is more competition. Google Ads is a challenging channel to grasp.

In a constantly changing environment, AI allows businesses to remain responsive and agile. You can even turn a little budget into a large success by embracing change and experimenting with the machine learning-based features in Google Ads.


Personalized and relevant advertising is essential in the realm of PPC marketing. You need a ton of technical expertise and experience to use Google Ads to its fullest potential.

Automation and artificial intelligence change that. In the intensely competitive digital advertising industry, these technologies let you maintain flexibility while reducing human labor.

Google advertisements aren't an exception to the growing trend of optimizing digital marketing campaigns with machine intelligence. Additionally, Google also possesses one of the world's largest data sets and most advanced technological platforms.

Because of this, the effective use of Google's campaign automation tools is essential for success. In fact, we think those who wait to automate campaign optimization will likely fall behind.

However, because new ideas can diverge considerably from tried-and-true best practices, it's wise to test them out separately to see what works for your company.

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