Machine learning (ML) and artificial intelligence (AI) have become a cornerstone of modern AdTech, transforming how advertisers run key programmatic advertising processes, such as audience targeting, media planning, and campaign optimization.
There are many different types of ML and AI models that are used to perform various activities within an advertising campaign.
In this article, we look at the role of ML and AI models in AdTech and programmatic advertising and explore some of their main applications.
What Is Machine Learning (ML)?
Machine learning refers to methods and algorithms that enable computer programs to learn from data. Instead of 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 the predicted and expected outputs. This iterative process helps the model improve its accuracy and generalize its performance to new, unseen data.
What Is the Connection Between Machine Learning (ML) and Artificial Intelligence (AI)?
Machine learning is a subset of the broader field of artificial intelligence (AI) within computer science. AI aims to develop algorithms that can perform tasks traditionally requiring human intelligence, such as learning, problem-solving, decision-making, and perception.
Other AI approaches include rule-based expert systems, evolutionary algorithms that simulate natural evolution, and symbolic methods focused on logical reasoning.
Recently, 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.
What Role Can Machine-Learning Models Play in Programmatic Advertising and AdTech?
Advertising technology (AdTech) platforms need to collect, process and manage large volumes of data and make 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 modern platforms to deliver value to their customers.
Machine learning plays a crucial role in this environment by enabling AdTech platforms to:
- Forecast and predict outcomes, e.g. 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
Looking to develop ML or AI models for your AdTech platform or advertising business?
Get in contact with our team and find out how our experienced development teams can help.
What Are the Main Applications of Machine Learning in Programmatic Advertising and AdTech?
Below are just a few practical examples of how machine-learning models and algorithms can be applied to programmatic advertising to solve problems and improve the performance of ad campaigns.
Example #1: 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 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 applied in popular search engines like Google Search for sponsored search advertising.
Predicting the number of clicks and the CTR a campaign will generate is still an important part of campaign planning and is offered by many popular AdTech platforms as it influences decisions made around bidding strategies in OpenRTB auctions and the ranking of advertisements or sponsored products in search and e-commerce platforms.
These predictions are based 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 factors such as available data, expected performance, and 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 such as app installations, subscriptions, account creations, and purchases.
Example #2: Predicting Campaign Outcomes
Another crucial application of machine learning in programmatic advertising is predicting the outcome of a campaign before it’s launched, which is used to power campaign planning.
In this example, predictive analytics is used to predict the number of reachable impressions, unique users, clicks, and conversions the campaign is expected to generate.
Machine-learning models for regression tasks use data from past campaigns with similar conditions to forecast future outcomes.
Common algorithms for this task include linear regression, decision trees, random forests, and gradient boosting.
Example #3: Audience Segmentation and Look-Alike Modeling
Audience segmentation and identifying look-alike user groups are effectively addressed using 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 behavior, 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 high-value segments enhances campaign performance and outcomes.
Example #4: Campaign Optimization
In programmatic advertising, the types of data that AdTech platforms collect, process and manage change every second.
Shifts in user behavior, the context in which ads are displayed, targeting criteria and budgets for campaigns, ad content, and market competition all require real-time and rapid optimization to achieve the desired outcomes of an ad campaign.
Reinforcement learning algorithms are particularly well-suited for navigating such 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.
What Is the Process for Developing a Machine-Learning Model?
The development of a machine learning solution involve several key steps:
- Data Ingestion and Preparation: Initially, data must 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 the 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. It is typically exposed through a simple HTTP API or embedded directly into the consumer application.
- Monitoring and Maintenance: Continuous monitoring of the deployed model is crucial for success. While a model might perform well during training and testing, its effectiveness can 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 re-training 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 from a team, including data engineering, data analytics, machine learning, and operations, along with creativity, curiosity, and patience.
What Are the Key Business Considerations to Take Into Account When Developing a Machine-Learning Model?
When developing solutions involving user data, it is essential to adhere to privacy policies and regulations, such as the GDPR and CCPA.
The platform implementing such a solution may need additional features, including obtaining user consent, advanced data governance, and mechanisms for data anonymization.
In some cases, model interpretability is also a crucial requirement.
Model interpretability refers to the ability to inspect and explain why a model produced a specific output. This capability is important for regulatory compliance and for building trust with users.
Processing large volumes of data and training complex models are resource-intensive tasks, demanding significant time and infrastructure investments. The availability of data, including its volume and the number of variables, influences model selection and performance.
Despite its advanced capabilities, machine learning is not a “magic box,” and expectations must be managed. Models do not always provide accurate predictions or guarantee success.
Machine learning is an example of advanced technology whose performance depends on numerous factors.
What Does the Future Hold for Machine Learning in Programmatic Advertising and AdTech?
Observing current trends in the field, we can anticipate continued advancements in the performance of tasks already addressed by machine learning.
As concerns about user privacy grow, we should expect solutions that address these issues, such as 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 to more nuanced and detailed targeting.
The use of generative models may further expand personalized advertising, potentially leading to highly tailored ad generation.
With the increasing popularity of chat applications and other platforms powered by large language models, we might soon see the emergence of new advertising channels, such as in-chat ads guided by conversation content.
Additionally, future developments could include innovative user interfaces that enable customers to interact with platforms using natural language, facilitating configuration setups and providing key insights from reports and metrics.
