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This document discusses how machine learning can be used in ecommerce applications such as email marketing, cross-selling, personalized recommendations, and abandoned cart emails. It explains that the process involves collecting customer data on purchases, views, likes, and wishlists, training a model with this data, and then making predictions. An example is given of a wine company that saw a 45% increase in session length, 22% increase in conversion rate, and 37% increase in average order size after implementing personalized recommendations and similar product suggestions based on machine learning.





























