Predictive and Generative Artificial Intelligence: What is it and what’s the difference?

Artificial intelligence has all but dominated recent news. As technology advances and is incorporated into different aspects of our lives, you may be overwhelmed, or even scared, trying to understand how the technology works, let alone how to use it.

Before you get too overwhelmed, however, consider that AI is already a substantial part of our daily lives, and has been for years now. The personal assistants on our phones, smart speakers in our homes, autonomous vehicles, and customer service chatbots: all artificial intelligence. It’s present in the background of our lives, streamlining, personalizing, and improving some of our more mundane tasks and experiences: social media feeds, eCommerce experiences, online shopping, real-time traffic and weather conditions, purchase verifications and online banking platforms, to name a few.

In advertising, AI reflects a similar pattern. Many media buyers have used predictive AI in their buys for years, whether they realize it or not. In fact, more than 80% of industry experts integrate some form of AI technology into their online marketing activities.

It seems all roads point to even more improvements, advances, and applications of AI in media buying. As such, it’s more important than ever for media buyers to understand how the technology works and ensure they are harnessing the power of its technology.

In this blog post, we’ll cover exactly what artificial intelligence is, the different types, its functions in media buying, and why it’s a necessary technology for the advertising industry.

What is artificial intelligence?

Artificial intelligence is an umbrella term for the concept of reproducing human intelligence and cognition in machines. These machines can then execute activities and streamline tasks, learning and improving as they go. There are two forms of artificial intelligence: predictive and generative.

What is Predictive AI?

Predictive AI refers to artificial intelligence systems that use statistical models, machine learning algorithms, and data-driven approaches to make predictions about future outcomes based on historical data. Predictive AI foresees future events or outcomes based on historical data.

Let’s look at a simple example. Imagine you ride your bike to work every day. Over time, after trying different ways to get to work, you will learn which route is faster or which road or path is better according to the day of the week or based on the weather outside. This is exactly how predictive AI works. You feed the computer or algorithm with large amounts of historical data so that it analyzes and predicts future outcomes based on past learning and applies the learnings to any new data it receives in the future.

What is Generative AI?

Generative AI refers to artificial intelligence systems that generate new content or outputs based on a given set of input data. Generative AI uses machine learning algorithms and deep learning techniques to create new outputs, such as text, images, music, or videos, that are similar to a given training data set. Generative AI models can be used for tasks such as image generation, text generation, and anomaly detection.

This is the type of AI currently making head waves in the news as it’s the basis for companies such as ChatGPT and Dall-e.

How does AI work in programmatic advertising?

Programmatic advertising leans heavily on the use of predictive artificial intelligence. These algorithms can quickly analyze large volumes of data from different sources and draw conclusions from them. When applied to programmatic advertising, this has various use cases.

1. Identifying patterns in consumer behavior

Every touchpoint that a brand makes with a consumer can be turned into a data point for artificial intelligence to sort and analyze. Part of this analysis reveals patterns in customer behavior that can then be used to tailor the ad experience based on specific interests or preferences.

2. Predicting Outcomes

AI can analyze data to identify the likelihood that a consumer takes a certain action. These predictions can then be used to better shape the customer journey. Knowing how consumers act based on search history or previously clicked-on ads enables brands to serve more strategic ads tailored to these typical outcomes.

3. Predictive Analysis

One of the most significant benefits of AI in programmatic advertising is that it can be constantly used to improve future campaigns. Analyzing past metrics, AI can identify which channels, targeting strategies, ad formats, and targets are working well and which are not. This information can then be used to optimize future campaigns, improving your ROI.

Will AI replace human media buyers and planners?

As you were reading the use cases for AI in programmatic media buying, you may have thought, “But, that’s my job.” In short, yes, AI can almost replicate the brain of an experienced media buyer in a machine or algorithm so it becomes capable of predicting, planning, and optimizing media.

Don’t panic. The key word is “almost.”

Though machines can certainly make programmatic advertising more efficient, faster, and easier to implement, there remain many factors that need human input and link AI to an overall media buying strategy. Additionally, when you bring efficiency to the media buying process, media buyers are freed from the more tedious tasks, allowing them to focus on the strategic and creative elements of their jobs.

Why should you use AI in advertising?

AI-backed strategies create a more personalized experience for the customer. Because AI analyzes campaign data to identify patterns, advertisers can deliver more tailored ads based on the customer’s interests and needs. This provides a more personalized, relevant, and positive customer experience. And every advertiser knows a happy customer is of great value as they are more likely to be a loyal, repeat shopper.

Additionally, brands and advertisers can save money and time by completing tasks faster than humans and making fewer mistakes. AI-powered systems enable advertisers to streamline their efforts, make quicker decisions, adjust to consumer patterns, provide better value, and improve ROI.

In summary, AI applications in programmatic provide the following benefits:

• Better personalization
• More relevant ads for customers
• More efficient budgeting
• Reduced costs and decreased ad waste
• Operational efficiencies
• Increased engagement and conversions

How do people and businesses benefit from AI?

AI applications in programmatic advertising will continue to evolve and grow as technology improves. With every new application, your advertising efforts can be significantly enhanced. And, as mentioned previously, customers will continue to benefit from more personalized tailored ad experiences.

Task AI to do all the heavy lifting for your advertising so that you and your team can focus on creating the best possible strategy to reach your audience and improve your ROI.