What Is Programmatic Advertising?
Programmatic Advertising is a form of digital marketing that uses algorithms to automate the process of buying and selling ad space. It’s often used in real-time bidding () to purchase and place various forms of ads on websites and apps.
Programmatic is growing in popularity because it allows for more efficient and targeted ad buying. In the past, advertisers would have to buy ad space through traditional means of person-to-person negotiations and orders. This process was time-consuming and often resulted in overspending. With programmatic ad buying, all of this is done automatically through software. Advertisers can specify their target audience and budget, and the software will do the rest. Real-time bidding is the most common type of programmatic ad buying. It involves an auction-based system in which advertisers bid on ad impressions in real-time. The highest bidder wins the ad impression, and their ad is displayed to the user. Another less common form of programmatic ad buying is private marketplace deals. These deals are negotiated directly between the publisher and advertiser, without going through an auction.
The Four Pillars Of Programmatic Advertising
Programmatic advertising can be broken down into four main components, each of which plays its own unique role in facilitating the media buying process.
Demand Side Platforms (DSPs)
Demand Side Platforms are software platforms that are used by advertisers to buy ads from publishers in real time. DSPs give advertisers the ability to target specific audiences and track campaign performance. They also allow for automated bidding on ad impressions.
Supply Side Platforms (SSPs)
Supply Side Platforms are software platforms used by publishers to sell ad inventory in real-time. SSPs help publishers manage inventory, set prices, and track performance. They also allow for the automated buying of ad inventory.
Ad Exchanges are online marketplaces that act as a middleman between publishers and advertisers, facilitating the exchange of ad space.
Data Management Platforms (DMPs)
Data Management Platforms are software platforms that collect, organize and in some cases analyze data from multiple sources. DMPs help advertisers target specific audiences and track campaign performance. They also allow for the management of first-party data.
What Is Machine Learning?
In a broad sense, machine learning refers to the development and use of software systems to predict future outcomes via historical data. Otherwise abbreviated as ML, it focuses on teaching computers to interpret, learn from and take action on the information they’re given without being explicitly programmed to do so. Machine learning algorithms are powered by data: the more data they have, the better they can perform.
Machine learning is mainly used for two tasks:
Classification: This is when an algorithm is given a bunch of data points and has to sort them into classes. For example, you could use machine learning classification to automatically group emails as spam or not spam.
Regression: This is when an algorithm is given a bunch of data points and has to predict a continuous value. For example, you could use machine learning regression to predict the price of a stock based on historical data.
Machine Learning and Artificial Intelligence
Machine learning is a subset of artificial intelligence (AI), which encompasses any system that can analyze and draw predictions from data. But while AI involves making a computer system that can learn and work like the human brain, ML only entails teaching a computer system how to learn and draw conclusions from data. We’ll get more into the similarities and differences between the two below.
Machine Learning vs AI – What’s The Difference?
So what sets ML and AI apart? Well, as we already mentioned ML is a subset of AI. That means that all machine learning is AI, but not all AI is machine learning.
AI systems are designed to simulate human intelligence – they’re able to reason, make decisions and solve problems. Machine learning, on the other hand, is a way of achieving AI. Machine learning algorithms are used to automatically detect patterns in data and then use those patterns to make predictions.
The following are some key differences between ML and AI.
● Machine learning is mainly focused on prediction, while Artificial Intelligence has a wider scope that includes prediction and also decision-making.
● Machine learning is mainly based on numeric data (such as numbers, equations, etc.), while Artificial intelligence I can use non-numeric data as well (such as images, natural language, etc.).
● Machine learning algorithms are mainly powered by statistics and probability, while AI systems can also use other methods such as rule-based systems.
● Machine learning is mainly focused on finding hidden patterns in data, while AI is also concerned with the interpretation and explanation of those patterns.
● Machine learning algorithms are mainly run on data that is already available, while AI systems can also be used to generate new data.
Is Programmatic Advertising Machine Learning?
Programmatic advertising is the process of using computers to automate the buying and selling of online advertising. Machine learning is a method of teaching computers to learn from data, without being explicitly programmed. So, is programmatic advertising machine learning?
The short answer is yes. The programmatic advertising system relies on machine learning algorithms to make decisions about which ads to show, when to show them, and how much to charge for them. It’s not just a matter of using machine learning, but programmatic ads couldn’t exist without it.
How Machine Learning Is Used in Programmatic and General Digital Advertising
Here’s a more detailed look at how machine learning is used in programmatic buying, and why it’s so essential to media buyers administering whole process.
Identifying Patterns: The first and most important way that ML is used in programmatic advertising is to analyze data and help identify patterns in user behavior. This data is then used to make predictions about what users are likely to do in the future.
For example, if a user has searched for “running shoes” on Google, it’s likely that they’re interested in buying running shoes. This customer data can be used to show them relevant ads for running shoes when they visit other websites.
Predicting Outcomes: Another important use of machine learning algorithm technology in programmatic advertising is to predict the likelihood of potential customers taking desired actions, such as clicking on specific Google Ads or making a purchase. This information can be used to determine which ads are more likely to be successful and worth showing.
Bidding on Ads: One of the most important decisions in programmatic advertising is how much to bid on an ad. If you bid too low, you’re unlikely to have your ad shown. But if you bid too high, you’ll end up spending more money than necessary. Through its ability to read big data and predict outcomes, machine learning can gauge the likelihood of a user clicking on an ad creative and come up with an optimal bid amount.
Predictive Analytics: Finally, machine learning is also used to constantly improve the results of programmatic campaigns. By analyzing metrics from past campaigns, machine learning can identify which targeting strategies, pieces of ad content and campaign targets are working and which aren’t. This information can then be used to optimize future campaigns, making them more likely to be successful.
The Advantages Of Using Machine Learning With Programmatic Ads
Machine learning is a powerful tool that can generate lots of positive outcomes for media buyers. Here are some of the main advantages of using machine learning in programmatic buying:
One of the biggest advantages is that it allows for better targeting of ads. By using data to identify patterns in customer behavior, programmatic advertising can show relevant ads to the right people at the right time. This leads to higher click-through rates and more conversions.
Machine learning also makes programmatic ads more efficient. By automating the process of buying and selling digital ads, machine learning can save you a lot of time. And by constantly improving the results of campaigns through predictive analytics, can help you get more out of your ad spend.
Lastly, machine learning also improves the accuracy of the programmatic system. By making decisions based on data, machine learning can avoid the mistakes that humans are prone to make. This leads to more successful campaigns and a better return on investment.
Programmatic advertising is still relatively new but is already revolutionizing the way that digital advertising is bought and sold. It’s efficient, cost-effective, and provides a level of targeting and customization that was previously impossible. And with the help of machine learning, it’s only going to get better.