Machine Learning and AI Models in AdTech: Applications, Development, and What's Next

Machine learning (ML) and artificial intelligence (AI) have become central to modern AdTech, reshaping how advertisers approach audience targeting, media planning, and campaign optimization. There are many different types of ML and AI models in active use across programmatic advertising, each suited to distinct tasks and performance objectives.

This article examines the role of ML and AI models in AdTech and programmatic advertising, and walks through their main practical applications.

What Is Machine Learning?

Machine learning refers to methods and algorithms that enable computer programs to learn from data. Rather than being explicitly programmed for specific tasks, a machine learning model learns by identifying patterns and making predictions based on the data it processes.

During training, a machine learning algorithm iteratively adjusts the model's parameters to minimize the difference between predicted and expected outputs. This iterative process helps the model improve its accuracy and generalize its performance to new, unseen data.

The Relationship Between Machine Learning and Artificial Intelligence

Machine learning is a subset of the broader field of artificial intelligence (AI) within computer science. AI aims to develop algorithms capable of performing tasks that traditionally require human intelligence — learning, problem-solving, decision-making, and perception.

Other AI approaches include rule-based expert systems, evolutionary algorithms that simulate natural selection, and symbolic methods centred on logical reasoning.

In recent years, the development and popularization of large language models (LLMs) and other generative models have led the public to increasingly associate the term "AI" with these specific types of machine learning — though the broader field encompasses much more.

What Role Can Machine Learning Play in Programmatic Advertising and AdTech?

AdTech platforms need to collect, process, and manage large volumes of data while making real-time decisions to deliver performance, generate revenue, and maintain user satisfaction. The programmatic advertising landscape is crowded, fast-paced, and highly competitive, presenting significant challenges for platforms trying to deliver consistent value.

Machine learning plays a crucial role in this environment by enabling AdTech platforms to:

• Forecast and predict outcomes, such as click-through rates (CTRs) and bid amounts
• Optimize campaigns and inventory in real-time
• Create high-quality audience segments
• Interpret and utilize contextual information
• Personalize advertisements
• Protect against fraud
• Ensure brand safety
• Reduce operational costs

Main Applications of Machine Learning in Programmatic Advertising

Below are practical examples of how machine learning models and algorithms are applied in programmatic advertising to solve problems and improve campaign performance.

Predicting Clicks and Click-Through Rates

One of the most common applications of machine learning in programmatic advertising is estimating the probability of a click and the click-through rate (CTR) for a given impression opportunity.

The challenge of predicting whether a user will click on an ad dates back to the early 2000s, when it was first applied in search engines like Google Search for sponsored search advertising.

Predicting clicks and CTR remains an important part of campaign planning and is offered by many AdTech platforms. These predictions directly influence bidding strategies in OpenRTB auctions and the ranking of advertisements and sponsored products in search and e-commerce platforms.

Predictions draw on data about the user, the display context, and the ad content. Machine learning solutions for this task range from traditional models like logistic regression to more complex deep-learning approaches. The choice of method depends on available data, expected performance requirements, and computational resources. Simpler models can capture first-order linear and non-linear patterns, while advanced deep-learning networks can uncover deeper interactions within the data.

Although clicks are a frequently discussed metric, machine learning can also predict subsequent conversion events — including app installations, subscriptions, account creations, and purchases.

Predicting Campaign Outcomes

Another important application is predicting the outcome of a campaign before it launches, which powers campaign planning tools. In this context, predictive analytics is used to forecast the number of reachable impressions, unique users, clicks, and conversions a campaign is expected to generate.

Machine learning models for regression tasks use data from past campaigns with similar conditions to project future outcomes. Common algorithms for this task include linear regression, decision trees, random forests, and gradient boosting.

Audience Segmentation and Look-Alike Modeling

Audience segmentation and identifying look-alike user groups are effectively addressed through clustering machine learning algorithms.

Clustering is an unsupervised learning technique that finds patterns in data and groups elements based on similarities, without prior information about target groups. A widely used algorithm for this purpose is k-means clustering.

Data on user behaviour — including past events and performance metrics — is valuable for identifying meaningful segments. By combining effective segmentation with other techniques, high-value user segments can be identified and matched with new users. Targeting these segments enhances campaign performance and outcomes.

Campaign Optimization

In programmatic advertising, the data that platforms collect, process, and manage changes every second. Shifts in user behaviour, the context in which ads are displayed, targeting criteria, campaign budgets, ad content, and market competition all require rapid, real-time optimization to achieve desired campaign outcomes.

Reinforcement learning algorithms are particularly well-suited for navigating these dynamic and unpredictable environments.

Within reinforcement learning, multi-armed bandit algorithms are designed to balance exploration of options with exploitation of the best-known choices. These models facilitate real-time decision-making and continuously learn from the results of their actions.

The ML Model Development Process

Developing a machine learning solution involves several key stages:

Data Ingestion and Preparation: Data must first be ingested into the system, cleaned, and normalized to ensure it is ready for use.

Exploratory Data Analysis (EDA): This phase involves analyzing the data to understand its structure, characteristics, and initial patterns. It helps identify suitable methods and approaches for the task at hand.

Feature Engineering: This step focuses on transforming data, selecting relevant features, and creating new features that enhance signal quality for the task.

Model Training and Validation: Various models are trained, tuned, and tested against performance metrics. This phase culminates in selecting the best model and its configuration for full-scale training.

A/B Testing (if applicable): In some cases, multiple models are deployed to conduct A/B tests in a real-world environment to determine the most effective approach.

Deployment: The trained model is added to a repository and deployed — typically exposed through a simple HTTP API or embedded directly into the consuming application.

Monitoring and Maintenance: Continuous monitoring of the deployed model is critical. A model that performs well during training and testing can see its effectiveness diminish with new data or over time. Effective monitoring metrics should align with desired business outcomes. Decreased performance should prompt analysis and re-testing against new data. Solutions may include retraining the model on updated data or revisiting earlier stages — such as EDA or feature engineering — if significant data changes have occurred.

This process requires a diverse set of skills across a team, including data engineering, data analytics, machine learning, and operations, along with creativity, curiosity, and patience.

Key Business Considerations for ML Model Development

Privacy and Regulatory Compliance

When developing solutions involving user data, adherence to privacy regulations like the GDPR and CCPA is essential. Platforms implementing ML solutions may need additional capabilities, including user consent mechanisms, advanced data governance, and data anonymization features.

Model Interpretability

In some cases, model interpretability is a critical requirement. This refers to the ability to inspect and explain why a model produced a specific output — important both for regulatory compliance and for building trust with users.

Resource Requirements

Processing large volumes of data and training complex models are resource-intensive tasks, demanding significant time and infrastructure investment. The availability of data — in terms of volume and number of variables — influences model selection and performance.

Managing Expectations

Despite its advanced capabilities, machine learning is not a "magic box." Models do not always provide accurate predictions or guarantee success. Machine learning is an example of advanced technology whose performance depends on numerous factors, and expectations must be managed accordingly.

What's Ahead for Machine Learning in Programmatic Advertising

Current trends point to continued advancement in areas where machine learning is already active. As privacy concerns grow, solutions designed to address these challenges will become more prominent — including methods for training models on aggregated and noisy data.

Federated learning, in particular, is a promising approach for enhancing privacy while still leveraging data insights.

Enhanced analysis of context and ad content could significantly improve contextual advertising, moving beyond simple category matching toward more nuanced and detailed targeting.

The use of generative models may further expand personalized advertising, potentially leading to highly tailored ad generation.

With the growing popularity of chat applications and other platforms powered by large language models, new advertising channels — such as in-chat ads guided by conversation content — could emerge in the near term.

Additionally, future developments may include natural language interfaces that allow platform users to configure settings and retrieve key insights from reports and metrics through conversational interaction rather than traditional dashboards.