As you venture into the world of using artificial intelligence (AI) to create personalized experiences, you may find some unfamiliar terms and phrases. Because AI powers personalization in shoppers’ daily lives, here are a few terms you need to know:
AI is the overall umbrella term for technologies that reason, learn information, and understand language. Basic examples include autocomplete Google searches, Spotify product recommendations, and Facebook ads and stories in your newsfeed based on your browsing history. The rapid increase of connected devices and the “Internet of Things” (IoT) is leading to even more complex uses of AI, such as self-driving cars.
Machine learning is just one way an AI system gets smarter as it analyzes more information. It begins with training data — the more the better — from which the AI system automatically learns underlying relationships between variables. By continually analyzing data, machine learning finds patterns and then uses the data in those patterns to adapt its algorithm to learn how to assign outputs to every input. Previously, computer programmers told a computer exactly what rules to follow and what to do, but machine learning learns from example data to find the most accurate way to connect inputs to outputs.
AI is often confused with predictive intelligence. However, predictive intelligence is actually just machine learning applying the same techniques to find patterns in historical data to make better predictions — and it’s typically used for CRM and personalization. For example, you can use predictive intelligence to directly analyze content and identify customers who are at risk for churn — or customers likely to be in the market for a specific product — and then create a personalized experience based on that information.
Deep learning is a branch of machine learning that uses complex algorithms and raw data to mimic the capabilities of the human brain, especially in the areas of voice, text, and facial recognition. Deep learning is especially useful for extracting signals from raw data from images, video, text, and audio to automate tasks that humans can do quickly and easily. For example, Facebook uses deep learning to suggest friends to tag in a photo based on facial recognition algorithms. And Apple’s Siri and Amazon Echo recognize voice commands thanks to deep learning. You can use deep learning to analyze social media images and find patterns to determine the most effective images and key words for a campaign based on specific, targeted customers.
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