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AI programmatic advertising
Jul 05, 2024

The role of AI in programmatic advertising

Chris Yu
Author Chris Yu

The omnipresence of AI is steadily growing in the 2020s, including in advertising. Here’s a snapshot of how AI is shaping today’s programmatic strategies. 

In the digital age, the integration of artificial intelligence (AI) into our lives is growing. It’s no different in the world of marketing, as AI slowly shapes digital advertising in front of our eyes. While a large number of users believe AI (or specifically, generative AI) is mainly used for content creation, it has brought about several other applications.

Programmatic advertising is a game-changer in the advertising industry. Combining AI with programmatic advertising is revolutionizing the way brands and advertisers target their audiences and optimize their campaigns.

Today, we take a closer look at the role of AI in programmatic advertising.

What is Artificial Intelligence (AI)?

Before we dive deeper into AI in advertising, let’s break down the concept. 

Google DeepMind defines “AI” as the “science of making machines smart.” That means making machines that can do intellectual tasks that humans can do such as reading and understanding text, understanding language, sensing external environments, etc. In other words, AI technology can simulate human intelligence and problem-solving capabilities.

AI lets machines learn by detecting patterns in data and making predictions from those patterns. It can then learn from those outcomes to make better and better predictions over time. Furthermore, it enables machines to digest large amounts of information and make quicker decisions.  

Digital assistants, GPS guidance, autonomous vehicles, and generative AI tools (like Open AI’s Chat GPT) are just a few examples of AI in our daily lives.

However, AI isn’t just one technology. It’s an umbrella term encompassing a range of smart technologies that can learn and improve independently. Some AI technologies you might hear about are machine learning, deep learning, natural language processing (NLP), neural networks, and speech recognition.

The difference between AI vs. machine learning

While AI refers to technology that enables computers to mimic human intelligence, machine learning is a subset of AI that utilizes data and algorithms to mimic human learning processes. AI digests large amounts of information in a short amount of time; machine learning processes those large amounts of data so it can identify, categorize, and analyze this information to provide valuable insights. AI also makes decisions quickly, but machine learning lets technology use experience and patterns to improve decision-making.

In advertising, many platforms integrate machine learning to detect patterns in real-time data, letting them forecast campaign success and make necessary optimizations. 

For example, marketers can use machine learning to determine where users stand in the buying journey based on past actions. Additionally, it can be utilized to discover new audiences with similar characteristics to existing customers (known as lookalike audiences) and use that information to guide them through the sales funnel.

Where does AI fit into programmatic advertising?

AI-powered advertising has been a booming trend in recent years, including AI-driven programmatic advertising. 

Programmatic advertising is an automated system that facilitates the buying and selling of ad space through real-time auctions. With the use of algorithms, advertisers and publishers can optimize their results. The process has always been driven by decision-based algorithms which facilitate automated media purchasing in real time.

Various programmatic advertising platforms such as demand-side platforms (DSPs) utilize algorithms to adjust ad spending and targeting rapidly, making programmatic ad buying a perfect fit for AI applications.

This efficient approach, which analyzes user data and demographics, maximizes the effectiveness of campaigns by reaching the right individuals at the right moment. It provides scalability, cost-effectiveness, and efficiency for advertisers and publishers alike. 

The increasing presence of AI in advertising is driven by data signals generated through user interactions with their connected devices, including the type of online content consumed. This capability lets advertising platforms deliver more tailored marketing messages to users, no matter when or where they are.

Current applications of AI in programmatic advertising

While human insights remain essential for designing, executing, and optimizing campaigns, AI has emerged as a valuable tool in streamlining processes and handling data-intensive tasks. Here’s a closer look at how AI plays a role in programmatic advertising:

  • Campaign optimization: Behind the scenes, machine learning algorithms optimize data across various dimensions (such as ad format, ad environment, supply vendor, fold placement, geography, etc.). This not only leads to cost-savings for advertisers but also provides transparent reporting, efficiency maximization, and better KPI achievement.
  • Precise segmentation and targeting: AI-enabled programmatic advertising creates high-performance audience segments that align with campaign goals, ensuring ads are delivered to users with a higher likelihood of conversion. Programmatic algorithms continuously assess audience segments for performance (by only segmenting a target audience that increases a campaign’s performance and removes those that don’t) and refine them accordingly. 
  • Ad placements & creative optimization: AI-driven tools can analyze the performance of different ad creatives and automatically optimize them for maximum impact. Additionally, AI-powered marketing ads dynamically adjust digital ad creatives to display relevant content based on web page context, user signals, and intent.
  • Real-time bidding and optimization: AI algorithms can assess bid opportunities instantly, identifying the best price to bid for ad placements by taking into account factors such as audience relevance, past performance, and campaign goals. This guarantees advertisers an optimized return on investment (ROI) by focusing on the most beneficial opportunities.

Pfizer introduces its own generative AI to aid its advertising

Pharmaceutical giant Pfizer introduced its own generative AI platform called “Charlie”  in early 2024. The platform aims to streamline content supply chains and enhance Pfizer’s marketing strategies. This is done through the centralization of content creation, editing, and reviewing, leading to increased efficiency and accuracy. The platform could be a game-changer in the world of modern pharma marketing.

Netflix implements hyper-personalized targeting using AI

Netflix integrates AI algorithms and machine learning through its personalized film and show recommendations. Additionally, the company elevates personalization by dynamically changing the thumbnail image of a film or show on a user’s Home tab based on their viewing history using Aesthetic Visual Analysis (AVA). 

Coca-Cola launches digital campaign featuring its AI platform

In 2023, Coca-Cola partnered with ChatGPT and DALL-E for its “Create Real Magic” campaign,  a contest that encourages digital artists to craft ads using Coca-Cola’s cutting-edge AI technology. By simply inputting text, users can generate customized digital ads, with the algorithms taking care of the rest. The top submissions were showcased on digital billboards worldwide, granting both Coca-Cola and the contributing artists widespread exposure. 

Challenges and considerations for AI

While AI-enabled advertising tools present numerous advantages, it is crucial to address the challenges and considerations that come with them. These include data privacy and ethics, ad fraud, and data safety.

In terms of data privacy and ethics, the use of AI algorithms in programmatic advertising raises concerns about the ethical implications of accessing vast amounts of personal data.

This sparks debates on how data is collected, stored, and utilized including the importance of appropriately obtaining user consent. Advertisers and marketers must adhere to data privacy laws and ethical standards to uphold consumer trust and avoid legal consequences.

Ad fraud is a prevalent issue in the digital advertising realm, and AI does not make programmatic advertising immune to its effects. Fraudsters employ sophisticated bots to replicate human behavior and generate fake ad impressions and clicks. 

This manipulation skews campaign performance metrics and results in wasted advertising budgets. Hence, AI-driven tools must incorporate fraud detection mechanisms to identify and combat fraudulent activities effectively.

Furthermore, ensuring data safety is paramount in programmatic advertising. Given the substantial volume of data involved in these processes, stringent safeguards are essential to protect against breaches and unauthorized access. 

Preserving data security is fundamental to upholding the integrity of AI-powered tools.

Final thoughts

AI has completely transformed programmatic advertising, going beyond just generative AI. It has revolutionized targeting, efficiency, and real-time optimization of ad campaigns, meeting the increasing demand for personalization in digital advertising. However, it is also crucial to consider the ethical implications of data usage, potential ad fraud, and data security measures when incorporating AI into programmatic advertising.

As programmatic advertising technology progresses, AI will play an even more integral role in its future. We can expect further advancements in ad planning and targeting, real-time adjustments, and enhanced quality assurance. Data privacy and measures against ad fraud are likely to evolve alongside these advancements, leveraging the growing capabilities of AI.


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