The activity described-Amazon collecting massive amounts of data about how, when, why, where, and what people buy and then analyzing that data to better serve its customers-refers to big data analytics and data-driven personalization. Amazon uses big data analytics to gather extensive customer information, including purchase history, browsing behavior, search queries, shipping addresses, and product reviews. This data is analyzed using sophisticated algorithms and machine learning models to create a detailed, 360-degree view of each customer
. Through this analysis, Amazon can personalize the shopping experience by offering tailored product recommendations, customized homepages, personalized promotions, and targeted email campaigns
. This process is often called predictive analytics or collaborative filtering in the context of e-commerce, where Amazon predicts what customers are likely to want based on their own behavior and the behavior of similar customers
. The goal is to enhance customer satisfaction, increase loyalty, and boost sales by making the shopping experience more relevant and efficient for each individual
. In summary, this activity is best described as Amazon's use of big data analytics and personalization through data science to optimize customer experience and business outcomes.
