CBInsights-2021年零售人工智能趋势观察（英）-2021.7_27页_1mb

Retail AI Trends To Watch In 2021 Summary

Core Content

The document outlines seven key AI trends that are expected to shape the retail industry in 2021, emphasizing how AI has become essential for adaptation during the pandemic and will continue to drive innovation and efficiency in the post-pandemic era.

Main Trends and Key Information

1. AI for E-commerce Fraud Prevention

• Overview: AI is becoming indispensable in combating e-commerce fraud, especially with the surge in online transactions and new customer authentication standards.
• Challenges: Increased online activity led to a rise in cybercrime and fraud.
• Solutions: AI tools analyze real-time data points like location, device ID, and behavior to identify suspicious transactions and reduce false positives.
• Key Players:
  • Sift raised $50M at a $1B valuation and acquired Chargeback.
  • Forter raised $300M at a $3B valuation.
• Standards: The European Commission's PSD2 directive requires multi-factor authentication, with behavioral biometrics (e.g., from BioCatch) playing a key role.

2. Retailers Invest in Micro-Fulfillment Infrastructure

• Overview: Micro-fulfillment centers (MFCs) are gaining traction as a solution to meet e-commerce demands and improve operational efficiency.
• Benefits:
  • Reduces floor space costs.
  • Speeds up the picking process.
  • Enhances last-mile delivery efficiency.
• Key Players:
  • Walmart is scaling MFCs with vendors like Alert Innovation, Domatic, and Fabric.
  • PepsiCo partnered with Dematic to automate fulfillment.
  • Takeoff Technologies plans to have 40 automated MFCs by year-end.

3. AI for Food Waste Reduction in Supermarkets

• Overview: Supermarkets are leveraging AI to manage inventory and reduce food waste.
• Challenges: Fresh produce has a short shelf life, and consumer preferences affect waste rates.
• Solutions:
  • Wasteless uses reinforcement learning to optimize pricing and reduce waste.
  • Afresh uses historical sales data to predict demand and cut waste to under 10%.
• Sustainability Goals: AI helps retailers meet UN sustainability goals and improve ESG scores.

4. First-Party Data Strategy in a Post-Cookie World

• Overview: With the decline of third-party cookies, retailers are focusing on first-party data to maintain consumer relationships.
• Impact:
  • Google and other browsers are phasing out third-party cookies.
  • First-party data includes app usage, CRM data, and point-of-sale data.
• Solutions:
  • CDPs (Customer Data Platforms) help unify shopper profiles.
  • Notable acquisitions include Bloomreach acquiring Exponea and Twilio acquiring Segment.
• Benefits: Enhanced personalization and better consumer insights.

5. Auto Tagging as a Must-Have AI Tool for Online Retail

• Overview: Auto tagging is essential for improving product discovery and e-commerce performance.
• Technologies:
  • Natural Language Processing (NLP)
  • Computer Vision
• Key Players:
  • Lily Al offers psychographic consumer profiles based on product tags.
  • Syte uses computer vision for detailed product tags.
  • Glisten provides APIs for rapid tagging of thousands of products.
• Benefits:
  • Boosts sales and customer retention.
  • Enables better competitive intelligence.

6. AI for Hyper-Local Inventory Planning

• Overview: AI helps retailers manage inventory at the store level, adapting to local consumer behavior and market conditions.
• Use Cases:
  • Clustering similar stores to create dynamic assortment plans.
  • Simulation of SKU changes based on real-time data and network trends.
• Key Players:
  • Lynx Analytics helped Levi's cluster over 300 stores.
  • Hivery offers tools for category management, reducing planning time from months to minutes.
• Benefits:
  • Reduces markdowns and stockouts.
  • Enhances profitability and customer satisfaction.

7. Checkout-Free Solutions Become More Accessible

• Overview: Retailers are adopting cashierless tech to improve convenience and reduce labor costs.
• Technologies:
  • Computer Vision
  • Sensors
  • Deep Learning
• Key Players:
  • Amazon launched "Just Walk Out" to sell cashierless tech.
  • Imagr and Caper are developing smart carts for pilot programs.
• Benefits:
  • Increases shopping speed and convenience.
  • Enables better consumer data collection.
  • Reduces theft and labor costs.

Conclusion

The retail industry is increasingly relying on AI to address challenges like fraud, inventory management, food waste, and data privacy. As the market evolves, AI tools are becoming more sophisticated and accessible, enabling retailers to adapt to changing consumer behaviors and regulatory landscapes. The shift to first-party data, micro-fulfillment, and auto-tagging are particularly significant in shaping the future of retail.