The Go-To Glossary for Marketers Needing to Brush Up on AI

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Every corner of marketing and technology is being shaped by AI.
Every corner of marketing and technology is being shaped by AI. Credit: Bakal/iStock
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It's not enough for marketers to collect petabytes of data; it takes a sharp mind to make sense of it all. Actually it takes a nonhuman one.

That's why artificial intelligence has invaded the marketing world, with Facebook, Google, Salesforce, IBM, Amazon and others building machine learning into their platforms.

Now marketers must understand the lingo if they're going to survive the machines. To help, here's a guide to the terminology around A.I.

Machine learning

When a machine teaches itself with minimal programming needed. Google showed off the powers of machine learning when it created a computer that learned the rules to the ancient game Go. Machine learning can be helpful in direct marketing and email marketing, in particular, by ingesting sense of vast sets of consumer data and using it to determine things such as the best times to send emails. Proponents say it can also identify the clients or customers that would be most receptive to given messages. And machine learning is used in ad targeting to help deliver messages to those audiences.

Image recognition

AI looks for patterns in images. Machines can obviously analyze many more images than humans, and with machine learning they can identify what's in the images and reveal patterns that people would never detect. Brands can use image recognition technology, for example, to find every photo online in which their logos appear. That could help brands locate their most loyal customers and tease out other actionable marketing insights. "Computer vision" is a term associated with image recognition, and refers to computer programs that analyze and categorize digital images.


A fancy word for a group of people—or anything—sharing a common characteristic. AI programs can identify clusters within mountains of data, uncovering patterns that humans alone couldn't perceive or connections that people alone wouldn't draw. Clusters can lead to developing audiences or segments for marketing purposes, creating a group of people with common traits and targeting them with ads.

Unstructured data

A term for disorganized pools of data that appear random and unconnected. Machine learning can make sense of the data, and it can identify clusters within it and other patterns that could be useful when making marketing decisions. Marketers gather all kinds of data, even if it doesn't seem relevant at the time, because they never know when something interesting is going to pop up.

Natural Language Processing

The technology that enables machines to interpret what people are saying in words or in text. Sophisticated AI can decipher speech, not just understanding the words but the context. Advanced natural language processing, or NLP, could detect sarcasm and other subtle human tones. Natural language processing is essential to the automated customer service of the future.


The programs running inside messaging apps and on websites that help consumers perform simple tasks. Chatbots were popularized by apps like Facebook Messenger and Kik, and they operate like apps within the messaging services. Brands and publishers build chatbots to do things like deliver news stories and facilitate e-commerce transactions. The smarter chatbots become, the better they understand language, the more useful they can be. Chatbots could become the personal assistants of the future, booking flights, making reservations and handling schedules.

Deep Learning

A more advanced branch of machine learning, where a computer teaches itself with only minimal amounts of programming. With deep learning, marketers can make the most use of data and apply it to make predictions about consumer behavior.

Neural Networks

Artificial intelligence programs modeled after the human brain. They incorporate deep learning and natural language processing to perform functions like recognizing handwriting and faces in photos.

Dynamic Pricing

One of the common tasks that deep learning-based programs can perform, setting prices based on consumer data. Dynamic pricing means that each consumer is presented with a price based on their own particular circumstances, depending on the time of day, their financial situation and other factors. Dynamic pricing can come into play in rates for plane tickets, for instance. AI can help craft personalized prices that are most likely to ensure a sale at the most efficient rate.

Recommendations/Content Curation

AI is useful in figuring out what goods to recommend to shoppers based on data, the way Amazon volunteers products that a visitor to its site might want. When a consumer visits a sneaker website, for instance, AI can help determine which the person might like based on their past browsing habits and other factors. A website could be customized for each visitor, much as Facebook's algorithm personalizes the feed users see, tapping AI to order its content in the way most likely to appeal.

Weak/Narrow AI

AI that's limited to specific tasks. Basically all of the AI found in marketing is weak. Most AI overall—the code behind everything from virtual assistants to self-driving cars—is actually considered weak. The next evolution is artificial general intelligence, bringing us closer to the long-anticipated, futuristic robots that could outperform any human at any task.