What kind of person would each digital advertising channel be at a cookout? We've done the research, and are here to share our findings.
AI vs. Machine Learning -- it’s likely you’ve heard both of these buzzwords relentlessly over the last few years, and often interchangeably.
While these terms are thrown about ad nauseam, and often without context or definition, in reality, they’re distinctly different. Artificial Intelligence is a broad concept with many applications and machine learning is just one part of it. Each has a distinct role and brings unique value to your SEM and digital marketing efforts. We’ll break down their attributes and how you can leverage them to boost campaign visibility, profits, and ROI.
Ever played chess against a computer on your old desktop? You were interacting with AI without even knowing it.
The term artificial intelligence was first coined in 1956 by American computer scientist John McCarthy, who believed it possible to create machines that could mimic the intelligence and many of the reasoning capabilities of a human brain. In actuality, even the earliest computers possessed intelligence capabilities, with the ability to conduct math, perform logic, and store information. Over time, however, computers were developed with increasingly advanced capabilities that allowed them to perform tasks and follow thought processes that more closely resembled human intelligence. Today, AI can perform a wide variety of advanced tasks, such as detecting and understanding languages, recognizing voices, solving complex problems, and event planning, among countless others.
Weak AI and Strong AI
There are two different kinds of available AI: weak AI and strong AI. The most common is weak AI, applications that we utilize on a daily basis in digital assistants such as Siri, Alexa, and chatbots. And while weak AI is designed to mimic the experience of human interaction, it doesn’t actually deliver the same level of intelligence. Instead, it’s programmed to understand certain interactions and tasks, classify them and then respond accordingly.
Alternatively, strong AI processes information more like a real human brain does when learning from and adapting to new experiences. Specifically, strong AI creates clusters and associations that can then inform a response when presented with new data. Over time, it develops strategies and responses independent of its programming.
Artificial intelligence, to say the least, is a broad umbrella term incorporating a wide array of computing and learning capabilities. Machine learning, on the other hand, is a subset of AI with specific unique functions -- which ultimately makes it a more impressive technology.
Specifically, machine learning, a term first coined in 1959 by American pioneer of computer gaming Arthur Samuel, involves processing large amounts of data related to a task, analyzing it, and using it to inform the computer’s performance at that task. Whereas AI technologies entail predefined programming on how to respond to new data, machine learning makes it possible for computers to learn as they perform a task, essentially creating their own new programming as they go to perform it better.
Additionally, machine learning can also be predictive. Algorithms build a mathematical model of sample data which predicts future performance, which informs the decisions to optimize performance at any given task.
Google’s image analysis capabilities provide one such example. While the vast majority of images published on the web provide no alt tag describing what they are, some some of them do. Google’s Vision API analyzes a series of photos that come with a tag, such as “chinchilla.” Inherently, Google doesn’t know what a chinchilla is, as it was never programmed into the system. But by studying the tags and the associated images, machine learning can accurately predict and identify other images that are likely to also be chinchillas.
Machine learning and AI are not mutually exclusive technologies, and each have distinct applications that make them valuable for different areas of your SEM and digital advertising programs. In our breakdown of AI vs. Machine Learning, we weigh the unique benefits of each and show you where each wins out.
AI vs. Machine Learning: Understanding audiences
Both AI and machine learning can help marketers analyze large amounts of consumer data to better understand their interests, needs, and purchase intent, while also automating and optimizing this process. However, machine learning can also identify new segments of your target audience and their unique traits. Affinio is one such enterprise product that does this, with the ability to analyze billions of interest variables and network connections to create unique audience clusters. This in-depth analysis, in turn, reveals new patterns, relationships, and commonalities about audiences that regular data analysis can’t uncover.
Winner: Machine Learning
AI vs. Machine Learning: Optimizing marketing messages
Better understanding your audience also brings more opportunities to create content that’s relevant to their interests. Automating the testing of different content types to see which resonates best with your audience is one of the places where AI shines.
Google Ads already does this with their ad rotation settings. Setting your ad versions to “Optimize” will automatically optimize your ads for individual auctions using signals like keyword, search term, device, and location. Then as Google Ads collects more data about ad performance, machine learning algorithms determine statistically which ads are likely to have stronger performance.
AI vs. Machine Learning: Improving Lead Scoring
With machine learning, it’s possible to create predictive models that leverage input data and probability to accurately determine future trends. While this capability is beneficial to a wide variety of digital marketing initiatives, it’s especially relevant to lead scoring. Traditional lead scoring capabilities rely on individual lead behavior to determine their potential to convert, and machine learning algorithms can draw correlations between those lead characteristics to identify valuable prospects before they express strong purchase intent.
Winner: Machine Learning
AI vs. Machine Learning: Optimizing Marketing Spend
Machine learning’s analysis capabilities can also be used to better allocate marketing spend by considering factors such as consumer data, buying signals, quick bidding options and other historical signals to predict future advertising performance. Powerful software can learn from past market data and current micro-changes in performance to make quick bidding decisions, which can both improve advertising performance and reduce wasted ad spend due to over-bidding.
Winner: Machine Learning
AI vs. Machine Learning: Delivering timely messages
Many would argue that there’s still no AI computer that can process decision factors with the same sophistication as a real human brain. While that may be true, AI can make decisions much faster than an entire team of data scientists.
From there, machine learning can help deliver the right marketing message to the right audience in real time. Using search queries, demographics, location, and other factors, the technology can automatically serve relevant ads to the right audience when they need them, enabling marketers to automatically deliver a more relevant message to their audience.
Winners: AI and Machine Learning
AI vs. Machine Learning: Engage dynamically
Probably the most interesting application of AI to marketing is the potential to engage with audiences dynamically. Smart technologies like Alexa and Siri are prime examples of this type of audience engagement. However, there are many other potential use cases, such as Google’s dynamic ads that can change the PPC advertising message based on audience input.
In addition, chatbots that leverage AI and machine learning have a unique ability to both engage with as well as help their audiences based on the information they provide. Far from being automated Q&A systems, many can perform advanced marketing and sales tasks, using natural learning processing to learn more about audience needs as they interact.
Winners: AI and Machine Learning
Neither artificial intelligence nor machine learning are new concepts -- AI enables computers to make decisions based on input data. Machine learning enables computers using data inputs to grow and learn. Both have been around -- at least in theory -- for decades.
What is new, however, are the ways in which these technologies are used to shape the direction of countless industries, including digital marketing and advertising. With the ability to engage audiences, provide new customer and competitor insights, deliver timely messages and optimize ad spend, these technologies have the power to reshape and essentially revolutionize the SEM industry as we know it, while taking your PPC campaigns to new and yet undiscovered heights.
It's no secret that digital marketers are on the cusp of realizing the power of AI and machine learning. Those who step up first to take advantage of all they have offer will position themselves well for a profitable future that’s leagues ahead of the competition.