AI-Powered Ecommerce: How Machine Learning Is Transforming Online Shopping

Table of Contents About the Author About the Technical Reviewer Acknowledgments Introduction Chapter 1: Economics of Ecommerce Business Overview Ecommerce Business Model Myntra – Pure Marketplace Model Marketplace Models and Profitability Revenue Drivers Cost Streams Marketing and Promotions Cost Technology and Platform Cost Operations Cost Economics of Ecommerce: Profit and Loss Statement Summary References Chapter 2: Ecommerce Platform: Digital Ecosystem of Buying and Selling Overview Browse by Category Ecommerce Platform: Empowering Sellers Digitally Top Funnel: Bring Visitors to Platform Mid Funnel: Engaging Visitors with Products Lower Funnel: A Path Conversion or Real Outcome Summary References Chapter 3: Merchandising for Ecommerce Marketplace Overview Category Management Case Study: Brand Prioritization on Ecommerce Platform Site Merchandising in Ecommerce Digital Marketing in Ecommerce Summary References Chapter 4: Ecommerce Search – Powerhouse of Conversion Overview Search Queries and Machine Learning Search Algorithms – Text Matching Text Matching: BM25 Algorithm Term Frequency (TF) Inverse Document Frequency (IDF) Search Result Ranking Search Architecture in Action: From Query to Results Semantic Search Deep Learning for Search Embeddings Conversational Search – Powered by Gen AI Summary References Chapter 5: Curated Choices Using Art and Science of Recommendations Overview Recommendation Engines for Ecommerce: Engage Buyers with Curated Choices Recommendation Engine – Business Impact The Science of Similarity: Crafting Personalized Choices Recommendation Engine Architecture: Crafting Personalized Choices 1. Data 2. Similarity Measures 3. Algorithms A Brief History on Filtering Algorithms [8] Collaborative filtering (CF) Content-Based Filtering Hybrid Recommendation Algorithms 4. Evaluation Fashion Ecommerce: Recommendations for You Similar Products – Personalized Product Recommendations Neural Collaborative Filtering (NCF) Content-Based Filtering Using Deep Learning Style Up or Complete My Look Using Computer Vision Conclusion References Chapter 6: Ranking: Science of Sorting in Ecommerce Introduction Ranking in Ecommerce Ranking Function Ranking – A Brief History Google PageRank – Revolutionizing Search Search Ranking in Ecommerces Ranking: A Deterministic Model Ranking: A Machine Learning Model Ranking Algorithms: Learning to Rank (LTR) Ranking for Recommendations on Ecommerce Ranking for Similar Product Recommendations Conclusion References Chapter 7: Personalization – AI-Crafted Customer Experience Introduction Location-Based Personalization Home Page Personalization Impact of Personalization in Ecommerce Personalization in Marketing Search Personalization Design to Delivery Personalization: Stitch Fix – A Personalized Stylist Personalized Similar Product Recommendations Summary References Chapter 8: Efficiency a Key Enabler for Delivery Experience and Profitability Introduction The Ecommerce Maze: Navigating the Order Fulfillment Journey Efficiency Equation Returns Orders Customer-Initiated Returns (CIR) Product Content: Creation and Validation AI-Based Size Recommendations: Enabling Customers with Right Decisions Size Recommendations: Machine Learning (ML) Model Size Recommendation – Skip-gram-Based Model [10] Recommending Clothes Sizes with Product Size Embeddings (PSE)[9] Benefits of PSE Reducing Customer Returns – Flagging Platform Abusers Non-deliverable Orders and Return to Origin (RTO) Transactions Cancellations Summary References