The most recent edition of Deloitte’s CMO survey projects 220% growth in AI adoption for optimizing and automating marketing efforts over the next three years. Just 11.8% of survey respondents report they are currently leveraging marketing AI however, this is estimated to triple—reaching 37.7% by 2024.
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Although AI and machine learning adoption in marketing is still nascent, it has never been more important to understand how this technology can benefit marketers and advertisers across industries. This article offers a brief overview of using AI in marketing, including what artificial intelligence is, common applications of AI in marketing, and tips for getting started on your intelligent marketing automation journey.
Artificial Intelligence (AI) is a branch of computer science that deals with building smart machines that can perform tasks that usually require human involvement. The concept of AI has been around for decades. However, recent advancements in machine learning (e.g., deep learning), rising computer power and declining compute costs, as well as the proliferation of difficult to process unstructured data has led to increased interest in artificial intelligence.
Although AI has the potential to disrupt business processes in virtually every industry, the technology is seeing varying adoption rates across sectors. In many industries, including financial services, technology, and retail, innovative marketers are already experimenting with AI. Artificial intelligence makes it possible to automate unstructured data processing, opening up new opportunities for intelligent automations and unlocking insights buried in difficult to process data formats.
As data-driven thinkers, marketers intuitively understand the value of having access to more information—and we’re talking about a lot more information. According to multiple analyst estimates, the majority of data created (80-90%) is unstructured. Because unstructured data doesn’t follow a predefined data model or schema, it is far more difficult to process and analyze than structured information. This means the vast majority of insights and automations that could be powered by unstructured data go unrealized.
It can be exciting, and also daunting, to think about all the ways AI will influence the future of work and life. However, it is often more constructive to look at existing applications of the technology than it is to dwell on unknowns and what could be. Here are some common applications of AI in marketing that offer insight into the tasks and problems machines are capable of solving:
Personalization involves matching the right digital content with user preferences. By either asking directly or monitoring behavior, user preferences are determined through data collection. Traditionally, personalization is a fairly manual process. Data is collected on users and stored in a marketing automation system, then rules are created that determine when users are served different pieces of content.
Modern personalization techniques leverage artificial intelligence to gain insights from massive datasets to predict what users want, when they want it, and their preferred communication method. One of the biggest advantages of using artificial intelligence for personalization is that even though AI systems have to be adjusted and monitored, they are capable of learning and improving autonomously.
Humans have relied on various technologies to deliver messages on our behalf throughout history. Now new tools are using artificial intelligence to write messages for us by understanding our intentions and emotions. Although AI writing still has room for improvement, the technology is already capable of completing formulaic writing tasks independently well as assisting human writers with outlines, section copy, keyword research, and more.
However, it isn’t just written content that AI is getting better at generating. For example, the most recent Matrix video game, which may be more accurately described as a tech demo, was built using Unreal Engine 5. AI systems drive the characters and vehicles, while procedural systems built using Houdini generate the environment. The creators have merged ultra-realistic 3D modeling, ultra-realistic lighting, real time physics, and AI to demonstrate where the future of video games and film are heading.
AI is affecting every part of society, even creative fields that people previously thought were too complex for automation. Moving forward, there will be increased use of AI in designing art, food, and other creations that have traditionally been handled exclusively by humans.
Digital ad bidding platforms are already highly sophisticated systems that rely on complex automations. These systems function independently, with machines buying and serving ads to specific people using massive datasets and complex algorithms. Automated bidding powered by artificial intelligence is one of the most popular and valuable existing applications of AI in advertising, and it will only become more capable as machine learning algorithms grow larger, increasingly complex, and become more interoperable.
Most digital marketers depend on third-party cookies from Google, and identifiers for advertisers (IDFA) from Apple, to track individual users as they browse the web. The data provided by these tools enable customized campaigns, offer deeper insights into consumer behavior, and more. However, privacy regulations across the globe are getting stricter, and companies are changing the ways they gather and share data about users.
Google is eliminating third-party cookies at the end of 2023, and the newest iteration of iOS requires app developers to show a pop-up that asks users to opt in to data tracking. Previously, most data tracking was opt in by default. However, analytics data suggests that when given a choice the vast majority of users opt out of tracking.
Artificial intelligence and machine learning tools make it possible to better understand and predict customer behavior in a manner that preserves user privacy and can be more effective than cookie-based models. Using probabilistic modeling, a statistical technique used to take into account the impact of random events or actions in predicting the potential occurrence of future outcomes, AI enables data modeling for ad targeting that doesn’t rely on cookies at all.
At super.AI, our mission is to make artificial intelligence accessible to everyone and automate repetitive work so that people can be more human. We take this approach in everything we do, and strive to create useful resources that empower everyone to learn about and leverage AI. For additional information on getting started with AI in marketing, check out the following resources:
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