How Google Ads Uses Machine Learning To Improve Ad Targeting and ROAS

Artificial intelligence and machine learning continue to be one of the main topics of conversation in tech as new consumer-friendly AI tools like ChatGPT, Zapier and others show the world the power of machine learning. 

But outside of these tools, AI and machine learning are already deeply ingrained into our digital ecosystem, and more specifically, platforms programmatic advertisers use to target potential customers.

Google Ads is one of the most popular platforms that programmatic advertisers use to deploy campaigns. The platform uses a variety of machine learning techniques to deliver highly personalized ads to users and help businesses target the right audience at the right time. Machine learning algorithms analyze vast amounts of data to identify patterns and trends. This helps advertisers access their target audiences and optimize their ad campaigns for maximum ROI. 

Here are some ways that Google Ads uses machine learning to improve the ad targeting and delivery process:

Ad Rank and Quality Score

Ad rank and quality score are both key factors in determining which ads are served to which individuals. Both of these scoring mechanisms rely heavily on machine learning. 

Ad rank determines the position of an ad on the Google search results page, while quality score is a metric that measures the relevance and usefulness of an ad for a particular search query. To calculate ad rank for a given ad, Google Ads uses machine learning algorithms that analyze factors like bid amount, ad relevance, landing page experience, and more. Quality score of an ad is also determined by machine learning models that evaluate the historical performance of an ad and its relevance to the search query.

Audience targeting

Google ads also utilizes machine learning to help advertisers target the right audience for a particular ad campaign by applying ML to its existing datasets, including user search history, demographic data, and online behavior to create custom audience segments. Machine learning algorithms analyze these data sources and identify patterns and trends to help businesses reach their target audience more effectively.

While Google Ads produces its own custom audience segments, many advertisers find better return on ad spend (ROAS) when they import their own audiences or build custom segments themselves. To learn how to build custom audience segments, visit to explore our identity resolution and custom audience offerings.

Smart bidding

When deploying campaigns, programmatic advertisers set a bid amount for the campaign – which defines the maximum amount of money they’re willing to pay for each click on an advertisement. While historically, bid amounts are set in stone until an advertiser adjusts them, machine learning can improve ad click through rates by adjusting those bid amounts based on an ad’s performance.

This is called “smart bidding” which is a machine learning-based bid strategy that Google Ads uses to help advertisers optimize their campaigns by adjusting the bid amount for ads in real-time as they’re being served to users. Smart bidding relies on machine learning models to adjust bids for ads based on factors like the user’s location, device, and time of day. This process ensures that the bid amount for a given ad is optimized for maximum ROI and conversions.

Ad copy and creative optimization

Google Ads also uses machine learning to optimize ad copy and creative elements like images and videos. Machine learning algorithms analyze the historical performance of different ad variations and identify the ad copy and creative elements that perform best and deliver the highest click through rates. This enables businesses to create highly personalized and effective ads that generate more conversions and ultimately drive sales.

Google Ads has, and will continue to rely heavily on machine learning and AI to deliver effective ad campaigns and help advertisers improve their ROAS. But it is also important to keep in mind that many data service provider (DSP) platforms in today’s ecosystem leverage similar technology to help drive performance. 

BDEX uses similar AI and ML technology to help its clients build smarter and more effective audiences by bringing together data from all over the internet and more than 100 sources, while Google only uses data it has captured from its search activity. Leveraging AI and ML to improve Ad targeting and ROAS is a much broader trend in the ad industry. BDEX is working at the heart of all of it and is poised to help facilitate improved performance and targeting for brands and agencies of all sizes.

As machine learning technology continues to evolve, we can expect to see even more advanced and effective targeting and optimization techniques in Google Ads and from similar DSPs.

To learn more about how Machine Learning is changing the advertising industry, visit our website.