Manish Upadhyay

8:21 PM, 16th Mar 2018

What Wikipedia Can't Tell You About, The Go-To Glossary for Marketers Needing to Go over on AI!

It's insufficient for advertisers to gather petabytes of information; it takes a sharp personality to comprehend it all. Really it takes a non-human one.

That’s why artificial intelligence has invaded the marketing world, with Facebook, Google, Salesforce, IBM, Amazon, and others incorporating machine learning with their stages.

Presently advertisers must comprehend the language on the off chance that will survive the machines. To help, here’s a manual to the terminology around A.I.

Machine Learning

When a machine teaches itself with minimal programming needed with the past data. Google showed off the powers of machine learning when it created a computer that learned the rules of the ancient game, Go. Machine learning can be helpful in direct marketing, marketing automation's and email marketing, in particular, by ingesting vast sets of consumer data and using it to determine things such as the best suitable times to send emails or Push notification or SMS with personalization such mood, demographics, interests. And machine learning is used in ad targeting to help deliver targeted personalized messages to these audiences.

Computer vision

“Computer vision” is a term associated with image recognition, and refers to computer programs that analyze and categorize digital 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. More powerful Image recognition techniques already exist that could potentially go even further in defeating methods of Image recognizations.

Clustering or unsupervised learning

A fancy word for a group of people – or anything – sharing a common characteristic. AI programs can identify clusters within tons of data, uncovering patterns that humans alone couldn’t draw. Clusters or Unsupervised learning can lead to developing audiences or segments for marketing purposes, creating segments of audiences with common traits and targeting them with distinctive content or ads.

Unstructured Data

A term (unstructured information) refers to information that either does not have a pre-defined data model or is not organized in a pre-defined manner. It's is typically disorganized pools of text-heavy data, but may contain data such as dates, numbers, and facts that appear random and unconnected. Machine learning can make sense of the data, and it can identify patterns within it and other patterns that could be useful when making strategic 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 in their mind.

Natural Language Processing

At the heart of conversational interfaces are radical advances in natural language processing (NLP)—the ability of a computer to recognize both the words and the intention of a conversation—and in artificial intelligence (AI)—the ability to decipher speech, not just understanding the words but the context.

When integrated, the two technologies make it possible for a computer to understand casual, and advanced natural language processing, or NLP, could detect sarcasm and other subtle human tones and respond with contextually relevant information. Natural language processing is essential to the automated customer service of the future.


The programs powered by rules and sometimes artificial intelligence, that you interact with via a chat interface inside messaging apps and on websites that help consumers perform simple tasks. The service/task could be any number of things, ranging from functional to fun, and it could live in any major chat product (Facebook Messenger, Slack, Telegram, Flo Chat, Text Messages, etc.).

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 that help you to schedule and reminders for certain task such as meetings, bookings, booking flight tickets, ordering pizza, ticket booking for movie etc.

Deep Learning

What’s deep in Deep Learning is the architecture, not the learnings. It’s about scale. it is 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 behaviour. Help you to improve links, social media referrals, on-page elements.

Neural Networks (connectionist systems)

Neural networks approach the problem in a different way. The idea is to take a large number of data, known as training examples and then develop a system which can learn from those training examples. 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. The goal of dynamic pricing is to allow a company that sells goods or services over the Internet to adjust prices on the fly in response to market demands.

Recommendations/Content Curation

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.

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