Data Mining by Amazon

This presentation explores how Amazon leverages data mining to enhance its business model, from personalized recommendations to operational efficiencies. It covers key techniques, a case study on Amazon’s recommendation system, and the broader impact of data-driven insights on technology and innovation.

Transcript

1. Data Mining by CHANGING THE WORLD OF RETAIL Presented by Félix CHAROTTE
2. Table of Contents Points to discuss: Business Model Importance of Data Mining Data Mining Techniques at Amazon Case Study: Amazon's Recommendation System Impact of Data Mining on Technology Conclusion
3. Amazon Overview The giant we all know
4. Amazon Overview
5. Amazon Overview From a share at $0.08 in June 1997 to $190 in May 2024 ( $1 980B Market Cap)
6. Amazon Overview Their greatest selling point
7. Amazon Business Model How to generate $574 billion in annual revenue
8. Amazon Business Model Amazon Marketplace - $241B 42% of Amazon's whopping $574 billion in 2023 01 Third-party sellers - $117B fees to promote and advertise on the website 02 Amazon Prime - 150M users 03 The Kindle MarketPlace 04
9. Importance of Data Mining Optimazing the marketplace to become the leader
10. Importance of Data Mining Customer journey optimization Taylor made Predictions Improved Cross Selling
11. Data Mining techniques used by Amazon The giant’s way of getting insights
12. The data mining techniques used by Amazon Clustering Segmentation of customer base for targeted marketing campaigns. Improved customer engagement and loyalty. 01 Association Rule Mining Basket Analysis for product association patterns. Cross-selling opportunities & increased average order value. 02 Text Mining Sentiment analysis of customer reviews. Identify trends, product improvments & customer support. 03 Regression Analysis Demand forecasting and pricing optimization. Efficient inventory management. 04
13. Amazon's Recommendation System Two main-stream Approaches Amazon Recommandation Architecture
14. Amazon's Recommendation System COLLABORATIVE FILTERING Viewed by both users Viewed by her, recommanded to him ! Similar users
15. Amazon's Recommendation System COLLABORATIVE FILTERING Viewed by both users Viewed by her, recommanded to him ! Similar users CONTENT-BASED FILTERING Your recently viewed items and featured recommendations Inspired by your browsing history Related to items you viewed Viewed by user
16. Amazon's Recommendation System Item-To-Item Collaborative Filtering Architecture Sort by Customer Ratings Build list of Recommendation Results Examples
17. Thank you for listening ! Do you have any questions ? Feel free if you to ask ! Jeff