Customer recommendations are a very small portion

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e., an algorithm making recommendations based on similarities between users) to more scalable solutions driven by a deep learning framework called DSSTNE. DSSTNE is pronounced “destiny,” by the way—as in “buy this jewel-encrusted toilet brush… because it’s your destiny.” They’re not subtle. Today, of Amazon’s total investment in AI. Netflix doesn’t sell products, but they similarly credit the combination of contextually-aware recommendations and personalization (both powered by machine learning models) with saving them $ChatGPT billion a year.

How does this work? By reducing one-month chu Cape Verde Email List rn by several percentage points. In the crucial 60 to 90 seconds that a customer will spend browsing before quitting the app in frustration, Netflix serves up content most likely to appeal to people with similar tastes. And it works: 80% of what people watch comes from a recommendation. By presenting customers with content they’re more likely to watch first, Netflix reduces churn. Even thumbnails are selected by a neural network based on predicted clickthrough.



Say you’re not Amazon or Netflix, though. Say you’re part of a small team or a startup tight on resources. What can you do? The good news is that today’s marketers can similarly take advantage of AI-powered recommendation engines on a more affordable scale. Not only is some of this tech available to your developers, but more than one tool exists right now that lets you deliver product and content recommendations based on audiences, their intents, and their interests.