By Anastasiia Lushyna
Artificial intelligence and machine learning have become essential tools for any marketer. Why? These technologies enable advertisers to reach their goals in a more effective way and stand out from the crowd – this is crucial since users are exposed to hundreds or even thousands of ads every day (note that being exposed to ads and noticing them are not the same things).
But what are the specific applications of artificial intelligence and machine learning in online advertising? What are the benefits? What key strategies to consider?
Keep reading to find answers to these questions.
Machine Learning and Artificial Intelligence in Advertising
The terms “machine learning” (ML) and “artificial intelligence” (AI) are often confused with each other, however, ML is a subset of AI. It enables the system to learn and self-improve thanks to the gained knowledge. Utilizing algorithms, machine learning analyzes loads of data, gains insights, and then makes informed decisions.
By leveraging ML for advertising, marketers can analyze large amounts of data more promptly, increase the quality of data analysis, adapt to changes faster, and automate various routine tasks. For instance, ML can help predict sales, churn rate, and customer lifetime value, perform customer segmentation, deliver more relevant ads to users, optimize campaigns and website content, etc.
Moreover, technology keeps evolving. While ML and AI are already widely used in different aspects of marketing, we can expect new applications and benefits to enter the stage in the near future.
Considering this, it is crucial to keep an eye on the trends. To help you do this, we created an e-book explaining 9 programmatic advertising trends to pay attention to in 2025.
Benefits of Using Machine Learning for Advertising
Now, let’s explore the key advantages of utilizing machine learning algorithms for advertising in detail.
More Effective Data Analysis
Effective advertising relies on data: your users’ search queries, locations, purchases, devices, social media activity, etc. Other things like, for instance, seasonal fluctuations and even weather forecasts must also be kept in mind. Gathering all this information is not enough – you should also structure and thoroughly analyze it.
Applying ML in advertising can help simplify this task and speed it up. While AI-powered systems can capture, structure, and analyze the data, ML algorithms can then be leveraged to identify audience segments, figure out how users interact with your ads, make predictions, and so on.
However, it is important to understand that human intervention is still required here. Yes, AI and ML can help you analyze vast amounts of data quickly, but you should still review every decision suggested by the technology.
Enhanced Personalization
Segmenting the audience with ML algorithms is also a way to tailor your advertising content more effectively and deliver personalized experiences to every user group. Thanks to this, it will be easier for your ads to draw attention – modern users often have banner blindness but using machine learning for display advertising (and other types of marketing) enables you to overcome this challenge thanks to greater personalization and higher relevancy of your ads.
Unique customer experiences lead to increased sales, customer lifetime value, and loyalty.
Automation of Repetitive Tasks
In terms of ML and AI in advertising, automation of routine tasks is one of the most obvious benefits – however, it is still significant. Technology can help you automate data collection and analysis, set up advertising campaigns, manage social media, and perform other tasks.
Additionally, the probability of human error is significantly reduced, which is especially important when it comes to data analysis.
Ad Buying and Campaign Planning Optimization
Thanks to the predictive capabilities of machine learning, media buying and campaign planning can be performed more effectively. For instance, ML can provide you with forecasts regarding the campaign outcomes and help you identify hidden weaknesses and opportunities. Besides, you can leverage ML to test your marketing campaigns before launching them.
In this way, your entire approach to marketing becomes more data-driven, which is a way to enhance your strategy.
Increased Quality of Content
While generative AI tools can help you create content faster (you must still review the generated texts, images, etc.), ML in marketing enables you to improve the quality of your content. By analyzing the performance of the previously created content, ML algorithms can provide you with insights on how to optimize a new piece so that it will be more relevant and interesting to your target audience. As a result, it will be easier for you to draw the attention of your existing and potential customers and drive sales.
Use Machine Learning Models in Advertising
Now that the benefits of machine learning in online advertising are clarified, let’s explore how you can leverage it for your business.
Programmatic Campaigns Optimization
Utilizing machine learning in programmatic advertising allows you to make the most of real-time bidding (RTB) and header bidding auctions and ensure the right performance of your campaigns.
For instance, Google offers a so-called Smart Bidding mechanism. Algorithms can predict the impact of different bids on conversions and conversion value and deliver signals regarding bid optimization. Additionally, you can set performance targets and customize these settings, as well as collect deeper insights into your bidding performance – Smart Bidding is equipped with reporting tools.
Dynamic Content Optimization
Modern users expect enhanced personalization, and utilizing dynamic content optimization (DCO) is one of the ways to provide them with highly relevant ads. Algorithms can modify different elements of ad creatives: images, texts, calls to action, etc. The adjustments are done according to user data like demographics and browsing habits.
For example, Meta allows advertisers to use Advantage+ Creative for Catalog. When it is applied, the following elements may be changed to ensure greater personalization: format, description variations for carousel ads, media and creative options for collection ads, product tags, and additional reasons to shop (like free shipping).
More Effective Targeting
Consider using ML for display advertising to ensure more accurate targeting and, as a result, higher relevance of your ads. Analyzing the data with the help of technology will enable you to identify the interests and preferences of your customers (as well as other characteristics) and segment your audience effectively.
Let’s explore another example. Abreva, a company selling cold sore medications, used to target people who were 35 and more years old. The main advertising channel was TV. However, the brand discovered that teenagers also were a large part of their audience and decided to reach them via YouTube instead of TV. Using machine learning, Abreva identified the target audience and inserted the advertising message into viewing experiences seamlessly. While there were only four videos at the beginning, Abreva then created 119 different advertisements with the copy customized according to different interests. Ads were served based on the videos users were interacting with.
What were the results? Abreva managed to achieve a 41% increase in overall ad recall and a 342% increase in search interest among its target audience across YouTube and Google.
Campaign Strategies of Using Machine Learning for Advertising
You are already familiar with the key benefits and applications of ML in marketing. Now, let’s explore the main strategies in the use of machine learning for ads.
Utilize Predictive Targeting Tools
In advertising, each of your decisions should be data-driven, and ML can help you deal with this task. With predictive targeting tools, you can forecast future customer decisions and the likelihood of a user taking action, create more detailed customer personas, etc. With these insights, you will be able to fine-tune your strategy and achieve your goals in a more effective manner.
Besides, machine learning algorithms can recommend where to place your ads and assist you with ensuring a consistent brand image. Such suggestions are based on data, not guesses, and this is exactly what makes them so effective.
Implement Product Recommendations
An efficient ad must be relevant and personalized, so utilize AI-powered recommendation models to suggest products that a user is most likely to be interested in. Such models are built on known customer habits and attributes. Then, when a new customer appears, the products are recommended according to the information the model knows.
Consider using advanced recommendation models – to understand customer intent, they rely on behavioral signals instead of explicit feedback provided by users (for instance, preferred product categories). As a result, your recommendations will be more specific, which can help you drive sales and increase income, as well as customer loyalty.
Consider Multiple Personalization Factors
Personalization is not something you can ignore – in modern digital advertising, it is a requirement. To ensure effective targeting and personalization, you need to consider a variety of factors, such as previous purchases, interests, culture, region, seasonality, and others. Basically, the more factors you pay attention to, the better. In turn, ML can help you organize the data and collect insights.
Ensure Brand Safety
To make your brand look credible, you need your ads to appear in a relevant and appropriate context. Programmatic platforms usually allow you to create white and blacklists of publishers to help solve this challenge. Utilizing contextual targeting can be useful, too.
However, we also recommend you to leverage machine learning algorithms and AI-powered tools to make sure that your ads are delivered to safe inventory that aligns with your brand.
Consider Attekmi Your Trusted Partner
Machine learning and advertising are topics that steadily become essential parts of each other. The technology gets more and more applications in marketing, and using them is a way for advertisers to reach their audiences effectively, drive conversions, and increase sales. In addition, nowadays, machine learning programmatic advertising is virtually a must to adopt. More and more brands are integrating programmatic into their marketing strategies, and neglecting to follow this approach implies missed opportunities and challenges with remaining competitive.
However, mastering programmatic and machine learning advertising can be a complex task. You need not only to select the right platforms and tools – you must learn everything about their functionalities and figure out how to apply them to your marketing strategy. At Attekmi, we are ready to help you overcome this challenge and master ML and advertising.
We offer AdTech and Ad Ops team training, as well as Ad Ops outstaffing services. We can train an expert specifically for your business so that you will be able to focus on your core tasks while your advertising campaigns run effectively and deliver impeccable performance.
Does Attekmi look like the right choice? Contact us now!
