Artificial intelligence (AI) is far from being a new term, but the emergence of ChatGPT over the last few months has firmly pushed it into the mainstream. The hype around the OpenAI chatbot in particular has made generative AI all the rage, but AI technology goes beyond only being able to produce content based on user inputs. It has a lot to offer, for example, for the world of digital marketing. Especially through its predictive capabilities.

Digital marketing presents the perfect environment for the use of AI technology. When you consider the obstacles facing the industry and the vast amount of data available, AI can offer the solution to overcoming the operational challenges, and analyze data in near real-time and in ways that would take a human several lifetimes.

But not all AI is the same. So, what is the difference between generative and predictive AI? And what is their respective value to digital advertisers?

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Generating predictions

Before delving into its variations, it’s worth looking at a broader definition of AI, which can be described as the use of technology to automate tasks via a learning system powered by data.

Generative AI produces content – or data – based on patterns and information derived from previous data, and can be trained to generate text, images, or music. However, there is no benchmark for performance with generative AI like ChatGPT.

Predictive AI meanwhile uses past data to predict future outcomes, rather than producing new content or data. It can be used to predict stock prices, customer behavior, and conversion probabilities in digital advertising, among many other uses. It can also be measured so it can deliver far more business value than generative AI.

In many cases, technologies that are able to combine the creative capabilities of generative AI and the forecasting power of predictive AI are the solutions that will be most effective.

How it stacks up

When choosing the type of AI setup to utilize, it’s important to understand the components of the ad stack, delving into what parts of it are most critical and which of those should be AI-powered.

The three key components of a mature brand’s tech stack are measurement, brand safety, and bidding.

Measurement is imperative to any strategy, because it enables brands to define what they are trying to achieve and identify if ROI is being delivered from their campaigns. Measurement mostly follows a rules-based system, so the technology consistently produces the same output for a given input.

Secondly, brand safety – which is required to help protect brands from content that could be detrimental to the business or lead to fraud – combines the rules-based logic of measurement with AI methods. Ensuring the brand is safe relies on natural language processing and neural networks – categories of AI that focus on building systems that can learn and make decisions based on the data ingested.

Furthermore, brands can utilize computer vision AI to detect potentially harmful video content.

Lastly, bidding – or ad decisioning – is where the use of AI has the biggest impact within digital marketing. It requires brands to make billions of decisions every day within the media buying process, and is where the aforementioned measurement and brand safety components can be valued and activated.

Bidding is where a brand’s performance is defined by calculating the probability of a desired outcome. As such, brands need to be able to use intelligence to optimize their performance, ensuring they can create a competitive advantage and ROI, while achieving the most efficient price for impressions at scale.

In the past, bids were sometimes placed manually by human traders, meaning people were essentially guessing on what a maximum bid should be. However, this was far from ideal, as every impression should be valued differently and priced separately.

Furthermore, brands are increasingly demanding more transparency around their media buys, and wanting to optimize towards quality metrics and business goals, rather than just clicks and standard KPIs. This is why the application of sophisticated technology – AI, in particular – is important to make this laborious process more efficient.

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Custom fit

When custom algorithms are introduced to the bidding process, it becomes entirely AI-based. A custom algorithm is a set of bidding rules executed by demand-side platforms (DSPs), but generated by AI, to deliver advanced media buying outcomes aligned to a business’ goals.

The bidding AI behind these custom algorithms should be purpose-built, customizable, and dynamic, while applying the learnings from past campaigns to subsequent campaigns, and creating opportunities to improve campaign scale. The AI needs to be trained to deliver performance and ensure spend is completely optimized towards achieving the maximum return on ad spend (ROAS).

Custom algorithms are a combination of the AI technology, data, and the talented traders in charge of training them, meaning all three must work together and be aligned to the outcomes the brand is seeking.

There is much more to consider than what initially meets the eye, and the responsibility is with the brands to maximize opportunities. By neglecting just one element of AI, brands are at risk of reducing efficiency of campaigns in the long run.