Machine learning in e-commerce represents a key trend in retail digital transformation and involves the adoption of self-learning computer algorithms that can autonomously improve their performance through experience. Specifically, machine learning algorithms are trained by processing large data sets, identifying recurring patterns, relationships, and anomalies among all that data, and building mathematical models that represent the relationships, can be given.
These models are refined as algorithms process more and more data and provide us with valuable insights into certain e-commerce phenomena and the interrelationship of all the variables involved. Something that has proven to be extremely useful in analyzing current events, predicting future trends, and making data-driven decisions. Here are some of the benefits we can expect from its deployment:
Personalization: Evolving the shopping experience and connecting consumers to their favorite goods through recommender systems, targeted advertising, and smart search engines.
Security: Ensuring secure transactions, protection of user data and hence regulatory compliance through machine learning powered fraud and anomaly detection tools.
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