So, what’s the real magic of machine learning and artificial intelligence in advertising? For marketers, it’s the ability to deliver unique and personalized user experiences on programmatic platforms. These algorithms allow businesses to work with data better and adjust their advertising strategies in real-time.
Today, AdTech companies are evolving and innovating and smart technologies like AI and ML are an integral part of programmatic platforms, across many processes from bidding to campaign optimisation. These technologies allow advertisers to analyze massive datasets and optimise ad placements and targeting like never before. For example, AI can assess user behavior in real time and make sure ads reach the most relevant audience. This shift to data driven strategies not only makes it more efficient but also more profitable for businesses.
For example, 2024 brought Meta Platforms’ highest ever revenue of $40.59 billion and it was all due to the significant investments in AI and machine learning. The question is—how exactly do these algorithms unlock new opportunities to achieve better campaign outcomes on programmatic platforms? Let’s see.
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What do AI, ML and programmatic have in common?
A decade ago, programmatic was the first in the AdTech industry to adopt ML and AI. Now it’s a tool to help marketers automate their ad campaigns. Thanks to those platforms, marketers can set up campaign budgets, upload creatives and configure campaign details in minutes. But tasks like ad placement, bidding and campaign optimisation are handed over to smart, automated systems (tech platforms). These systems use real-time bidding (RTB) protocols to deliver targeted ads in real-time.
You may ask, what sets programmatic advertising companies apart from others? The answer is they heavily rely on ML, AI, big data and RTB in their operations to get that individual targeting touch that makes advertising more relevant and thus more personal and effective.
The role of AI & machine learning in ad buying and targeting
The last couple of years were the best years for ML and ML adoption. These technologies have reached the peak of adoption and are going to develop further in the near future with companies investing in them. Think about it – in 2024, AI adoption increased and now 77% of businesses use AI and the global ML market grew at 42.08% CAGR between 2018 and 2024.
Programmatic vendors use these technologies to optimise ad buying and targeting. AI tools with ML algorithms analyse vast amounts of data and make real-time bidding decisions in RTB auctions. In simple terms, the algorithms of the programmatic platform quickly analyse whether the ad impression can benefit the advertiser based on the campaign input. If not, the platform will skip the impression. If yes, the platform will bid on it to win the right to serve a targeted impression.
As a result, conversion rates go up and ad spend on non-relevant impressions goes down. Plus, fraud on those platforms is automatically detected and blocked so you don’t waste your ad budget on bots.
AI & Machine Learning in Ad Buying and Targeting
AI and ML have taken off, with 77% of businesses using AI in 2024 and the global ML market growing at a 42.08% CAGR since 2018. Programmatic vendors use these technologies to improve ad buying and targeting, analyzing massive amounts of data to make real-time bidding decisions in RTB auctions.
Here’s how it works:
- AI decides if an ad impression is good for the advertiser based on campaign inputs.
- If it isn’t, the platform skips it.
- If it is, the platform bids on it to get a targeted impression.
The results? Higher conversion rates, less ad spend on irrelevant impressions, and auto fraud detection so ad budgets aren’t wasted on bots.
How AI Automates the Buying Process
AI and ML automate programmatic ad buying by deciding if an ad impression is good for:
- User interests and past behavior
- Device type and location
- Real-time behavioral signals
These algorithms calculate the optimal bid—high enough to win the impression but not overpay. Over time they learn from past performance, improving targeting and budget allocation. Modern programmatic platforms also use AI for creative optimization, audience segmentation and campaign testing.
AI’s Role in Real-Time Bidding (RTB)
Billions of ad impressions are bought and sold through RTB every second. AI-powered RTB systems process massive datasets in milliseconds to determine the best bid price and ad placement for maximum engagement.
How AI improves RTB:
- Automated Bidding: AI optimizes bid prices based on historical performance and user intent.
- Fraud Detection: ML models detect fraudulent traffic, reducing wasted ad spend.
- Ad Placement Optimization: AI ensures ads appear on high quality, brand safe sites.
AI-Powered Audience Targeting & Personalization
Traditional targeting was based on broad demographics. AI allows for hyper-personalization by analyzing real-time behavioral signals like:
- Browsing habits (pages visited, time spent on site)
- Purchase intent (items added to cart, abandoned checkouts)
- Contextual relevance (content engagement patterns)
By segmenting users into micro-audiences, AI ensures ads reach the right people at the right time, boosting engagement and conversions.
Machine Learning’s Impact on Ad Creatives & Optimization
Did you know 75% of an ad’s impact comes from the creative? AI-powered tools like Adobe Sensei and Persado analyze massive datasets to create high-performing ad variations. ML determines which images, copy and CTAs convert best.
How AI Helps with Ad Creatives:
- A/B Testing at Scale: AI tests multiple creatives to find the winner.
- Dynamic Creative Optimization (DCO): AI adjusts ads in real-time based on user behavior.
- Predictive Performance Analysis: AI predicts ad performance before deployment.
Using natural language processing (NLP), AI also analyzes audience sentiment to write ad copy that resonates, making brand messaging more human-like.
Benefits of AI & ML in programmatic advertising
As we’ve seen, it’s hard to underestimate the importance of ML and AI in programmatic as it optimizes so many processes from buying to creative making. Let’s see what it gives to businesses:
Better campaign performance. With data-driven personalization enabled by programmatic targeting, ads are shown to the right audiences, on the right screens and at the right time so ads are more relevant and thus more effective (brand awareness lift, increased conversions, etc.)
Saving huge amounts of time. With smart algorithms, programmatic platforms are fully automated, minimal human intervention required. They decide what impressions to bid on, what’s the optimal bid and how to optimize the bidding in the future.
Budget optimisation. The more relevant the ad campaign is to the user, the more likely the budget is being used as you’re not wasting money on showing ads to people who will never convert. This applies to ad fraud protection as well – ML and AL algorithms help programmatic platforms protect ad budgets pre-bid and post-bid.
AI & Machine Learning in Ad Fraud Prevention
Ad fraud costs businesses over $100 billion annually (Statista). Fake clicks, bot traffic and domain spoofing are draining ad budgets, fraud prevention is key.
How AI Detects & Prevents Fraud
AI is key to detecting and preventing ad fraud by analyzing vast amounts of data to stop fraudulent activities that waste ad spend. Here’s how AI is used in ad fraud detection and prevention in the ad ecosystem:
Key Machine Learning Techniques Used in Fraud Prevention:
- Traffic Pattern Analysis: AI algorithms look at website traffic patterns to identify anomalies that indicate fraud. This includes looking for suspicious spikes in traffic, unusual geographic distribution and traffic from data centers or known botnets.
- Clickstream Analysis: AI looks at user click patterns on ads to detect invalid clicks from bots or click farms. It looks for patterns like high click-through rates (CTR) with low conversion rates, repeated clicking and clicks from suspicious sources.
- Impression Analysis: AI verifies ad impressions by looking at data related to ad views such as viewability metrics, time spent on page and user engagement. It detects fraudulent impressions from bots or hidden/stacked ads.
- Behavioral Analysis: AI models user behavior associated with ad interactions. By establishing baseline user behavior, AI can flag deviation from that behavior that indicates fraud, such as users who only click on ads and show no other engagement on a website.
- Machine Learning-Based Anomaly Detection: Machine learning algorithms are trained on massive datasets of both legitimate and fraudulent ad interactions. These algorithms learn to identify subtle patterns and anomalies that differentiate fraud from genuine user engagement. As fraud tactics evolve, machine learning models can adapt and improve their detection accuracy.
- Natural Language Processing (NLP): NLP can analyze ad content and website content to identify brand safety issues and detect domain spoofing or ad injection fraud where ads are placed on inappropriate or unauthorized websites.
- Device Fingerprinting and Geolocation: AI looks at device characteristics (device type, OS, browser etc.) and geolocation data to identify suspicious patterns, such as multiple accounts from the same device or location or mismatch between claimed and actual user location.
Real World Impact:
- Google’s AI driven ad fraud detection system blocked 5.5 billion fraudulent ads in 2023.
- Juniper Research projects that global expenditure on fraud detection and prevention services will exceed $11.8 billion by 2025.
The Future of AI & ML in Programmatic Advertising
AI and ML have always been part of programmatic but as they advanced and matured they became a must have. This has made programmatic advertising so popular that brands can now optimise their campaigns and make them personal to every user.
As we see AI and ML do not just enhance targeting, budget allocation and fraud detection but also the overall efficiency of the advertising platform. As these technologies evolve they will continue to shape the future of digital advertising making it smarter, more effective and more impactful than ever before.
