Imagine a world where you would know what the best ad placements for your creatives are, who the best audience for your product is and when to bet (or not) on specific websites to increase your ROI. All this, without losing precious hours analysing data with the hope of finding patterns and predicting outcomes. Well, this world exists: it combines machine learning and programmatic advertising.
Machine learning (ML) plays a crucial role in advertising; that’s why a large number of advertising platforms use this technology in 2025: Demand-Side Platforms, Supply-Side Platforms, AdTech stacks, etc.
Learning the basics
What is machine learning?
Machine learning describes the process of analysing data, understanding patterns and predicting outcomes through technology. Software and systems learn from the data and input they receive. They adapt to new data in real time to offer solutions and solve difficult problems.
Machine learning is used in almost all types of industries: healthcare, retail, advertising, marketing, communication, logistics, etc.
Machine learning examples
Here are some machine learning examples:
- Recommendation systems: streaming platforms like Netflix use ML to suggest movies and series to users, based on the data they received previously. Social media platforms, such as Instagram, Facebook and LinkedIn, also use machine learning to show relevant posts in users’ feed.
- Spam filtering: email providers like Google use machine learning to filter out spam emails. They analyse data and learn patterns with ML to improve UX. That’s why some emails are directly sent to the spam folder.
- Advertising: ML is used to show users ads that have the highest chances of conversion, based on their age, preferences, location, etc.
- Customer service: when people contact customer service, the support team asks them a series of questions. Depending on the answers the support team collects, ML can indicate the best service for each customer. It’s a gain of time for both customers and companies.
Artificial intelligence vs machine learning
Artificial intelligence (AI) defines any programs created to mimic humans and automate complex actions. AI uses logical operators and values and mathematics to simulate problem-solving reasoning used by humans to make decisions or perform specific tasks.
Machine learning (ML) is considered a component of artificial intelligence. ML allows systems to automatically detect patterns to improve their performance. Some might consider machine learning as artificial intelligence’s brain.
Programmatic advertising overview
Programmatic is a type of advertising where buying and selling ad placements is done in an automated way through software and advertising platforms: SSPs, DSPs, ad exchanges, etc. Programmatic advertising uses data-driven algorithms, AI and ML to help publishers and buyers sell and buy ads more efficiently.
Machine learning plays an important role in programmatic advertising. This essential technology allows for campaign optimisation, fraud detection, forecasting, audience targeting, ad curation… on the best demand-side platforms on the market.
Machine learning in Programmatic
Campaign optimisation
By analysing the data of millions of programmatic campaigns, machine learning is able to detect the best parameters for each campaign, each line, each ad, ran on a DSP, for a specific time or day. ML can boost the delivery of a campaign during peak hours, slow it down when click or conversion rates are very low, and much more.
This amazing feature saves advertisers and account strategists a lot of time; they can focus on more urgent tasks and optimise their campaigns on a deeper level. By doing so, they are able to deliver the best-performing ads on the best domains, at a time where the conversion rate is higher and CPA lower, for example.
Some DSPs even offer optimisation in real time thanks to ML and AI: Yahoo DSP Blueprint is a great example.
Predictive audiences
ML is used in Programmatic to create predictive audiences from first-party data. These premium audiences target users who are most likely to perform the same actions as the original audience.
Want to learn more about predictive audiences? Click here.
Fraud detection
Companies like IAS and MOAT use machine learning to identify ad fraud and protect businesses from bots and fraudulent users. Algorithms analyse traffic patterns, abnormal CTR and unusual locations in real time to detect fraud.
If something is outside of the ordinary, these companies detect fraudulent activity thanks to ML. They can then take necessary actions to protect brands.
Insights and forecast
Machine learning is absolutely essential to learn from previous data and input, optimise programmatic campaigns in real time and predict actions thanks to pattern recognition. Many advertisers use ML to predict the reach of future campaigns.
They can set a target CPM, for example, for a specific date range, in a selected country, and the system will show them an estimate for the delivery. Really handy, isn’t it?
Audience targeting
When targeting several audiences for the same campaign, the algorithm can push delivery on the best-performing audience lists thanks to ML. The system is able to learn which audience performs the best in real time for a specific placement, website, or day of the week. Again, this is possible thanks to pattern recognition.
Machine learning can also highlight audiences that advertisers wouldn’t have chosen at first if the software recognised an optimisation opportunity.
Contextual targeting
Contextual targeting, and more specifically semantic targeting, uses artificial intelligence and machine learning to understand the meaning of webpages’ content. Contextual targeting allows companies to deliver their creatives in a cost-effective way and to show them to the people who are most likely to convert: it’s also called contextual advertising.
A/B testing and creative optimisation
ML is used in A/B testing and in creative optimisation. Or advertisers can analyse the data from A/B ad testing and try to find patterns and learn from then, or they can let machine learning do it for them.
When creative optimisation is done dynamically and in real time, we use the term DCO: Dynamic Creative Optimisation. It’s a type of programmatic optimisation that creates and delivers personalised ads based on real-time data. As you probably know, personalisation increases conversion rate in advertising.
Curation
Machine learning is used in programmatic curation: it scans and analyses inventories and data non-stop to find the best curated inventory for each advertiser and for each campaign. Curation is one of the latest targeting trends in advertising, used by the best programmatic experts like Ted Jordan.
Challenges
Despite bringing a lot of value to advertising, machine learning faces some challenges. For example, when launching a new campaign, ML might take several days or weeks to gather enough data and learn from it. This exploration phase is sometimes frustrating for account strategists, and even more during short campaigns.
Another challenge is data availability and the quality of predictions created with the help of ML. ML software and algorithms require a lot of accurate data to learn from it; depending on a campaign’s budget and its targeting, inaccurate predictions might be created by the system.
Overall, the role of machine learning in advertising is indubitably growing every year. This is due to the evolution of new technologies and to the perfection of programmatic platforms. Programmatic advertising without the use of ML is now unthinkable.
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