salesforce-人工智能如何影响收入和购物（英文）-2018-25页-4mb

报告摘要

Summary of "Personalization in Shopping" Report

Core Content

This report analyzes the impact of AI-driven personalization on shopping behavior and revenue, drawing insights from data across 150 million shoppers, 250 million visits, and $550 million in orders. It emphasizes the importance of personalization in enhancing shopper engagement, conversion rates, and overall revenue, while also offering best practices and real-world examples of brands that have successfully implemented these strategies.

Main Findings

Product recommendations significantly drive revenue, with visits that include recommendation clicks contributing to 24% of orders and 26% of revenue, despite comprising just 7% of all visits.
Shoppers who click recommendations spend 5 times more per visit on average and have a 10% higher average order value.
Personalization increases conversion rates across all device types, with visits including recommendation clicks seeing a 4.5 times higher conversion rate than those without.
Mobile shoppers who click recommendations convert at a 25% rate, higher than desktop (23%) and tablet (7.3%).
Desktop shoppers have the longest visit duration (15 minutes), and they are 24% more likely to create a basket.
Combining personalization with site search enhances conversion: shoppers who use search and click a recommendation convert 3.7 times more often than those who only search, and 4.2 times more on mobile.
Only 6% of shoppers clicked a recommendation, but 37% of order-placing shoppers did so, showing a strong link between recommendation engagement and purchase behavior.
24% of products bought by recommendation-clickers were recommended items, highlighting the effectiveness of AI in driving sales.

Key Insights on Personalization

Personalization is a critical component of the future of commerce and essential for competing in today’s market.
Recommendations are not just a feature but a strategic tool that can significantly increase revenue and shopper engagement.
AI is accessible, even for retailers without large data science teams. Simple implementations like product recommendations on key pages can yield substantial results.
Retailers should not fear relinquishing control to AI. Most systems allow for manual overrides and custom rules, ensuring flexibility and control.

Best Practices for Implementing Personalization

Start with a manageable strategy: Focus on one product type (e.g., alternate products) and gradually expand based on performance.
Leverage AI for tactical execution: Use machine learning to handle micro-level decisions, freeing up human resources for strategic tasks.
Test and optimize: Continuously review personalization performance and refine your approach to improve engagement and conversion.
Use clean data: Ensure your data is organized and accurate to maximize the effectiveness of AI-driven recommendations.
Expand beyond product recommendations: Implement personalization in emails, search results, and homepage/profile/category pages to create a more holistic experience.

Trailblazer Stories

Stonewall Kitchen

A small business that used Commerce Cloud Einstein to deliver 1-to-1 personalization without a data science team.
Einstein Product Recommendations influenced 14% of purchases in 2017, with a 44.3% conversion rate and 39.6% add-to-cart rate.
Personalization helps connect with shoppers on a deeper level, increasing both engagement and sales.

PacSun

A fashion retailer that used Einstein Product Recommendations to create a more personalized experience for its customers.
Implemented a single recommender across all product pages, reducing manual effort and improving bottom-line results.
Plans to experiment with personalized search and predictive sorting to further enhance the shopper journey.

Room & Board

A home furnishings brand that started leveraging customer data in 2009, which led to 2,900% ROI in its first year using Einstein.
Shoppers who engage with recommendations spend 40% more on average, and those who view recommendations before visiting the store spend 60% more.
Continuously uses customer interaction data to refine its personalization efforts and improve marketing campaigns.

Conclusion

Personalization is a powerful tool that can transform the shopper journey, increase conversion rates, and drive revenue. The data shows that recommendation-clicking shoppers are more engaged, spend more, and are more likely to return. Retailers should start small, test effectively, and leverage AI to create tailored experiences that meet the evolving expectations of modern shoppers. By doing so, they can unlock significant business value and stay competitive in the digital marketplace.

Resources to Learn More

Explore AI-powered personalization tools like Commerce Cloud Einstein.
Study how recommendations impact conversion, average order value, and revenue.
Learn from trailblazer stories and best practices to implement effective personalization strategies.