Quick Overview
General e-commerce retailers use AI to recommend products at checkout based on browsing history, purchase behavior, and preferences. AI-driven personalized suggestions increase average order value (AOV), conversion rates, and repeat purchase intent.
Various global e-commerce retailers
Company Size
Varies widely
Revenue Range
Varies widely
Primary Challenge
Increasing AOV and conversion through personalized upselling and cross-selling
Key Metrics
\- 15–20% increase in average order value\- 15% higher conversion rates\- 80%+ repeat purchase intent
The Problem
Generic product recommendations lacked personalization and relevance, limiting sales potential
The Solution
AI-powered recommendation engines analyze user behavior and preferences to deliver tailored product suggestions during checkout
Results
\- Increased sales and customer loyalty\- Improved shopping experience\- Higher repeat purchase rates
Details
Industry
Retail & E-commerce
Departments
Sales & Lead Generation
Marketing & Content
Use Cases
Upsell & Cross-sell
Tags
Recommendation Engine
GenAI
NLP
Time-Saving
Cost Reduction
Client Satisfaction
SaaS
AI Tools Used
No items found.
Sources
https://dialzara.com/blog/ai-powered-upselling-and-cross-selling-2024-guide/https://www.creatio.com/glossary/ai-for-cross-selling-and-upselling
More Case Studies
See All
How SPAR ICS Achieved 90% Inventory Prediction Accuracy and Reduced Food Waste with AI
Retail & E-commerce
How Redfin Uses Machine Learning to Provide Accurate Property Appraisals
Real Estate
How Bosch Uses Predictive Analytics to Improve Product Quality and Reduce Customer Complaints
Manufacturing
How BACA Systems Doubled Sales Productivity and Achieved 99.9% Shipping Accuracy with Salesforce Einstein 1 Platform
Manufacturing
How ABB Ltd. Increased Production Efficiency and Reduced Energy Costs with AI-Enhanced Robotics
Manufacturing
How The Serafen Group Reduced Staffing Costs by 40% Using ZyraTalk’s AI Chat for Order Handling
Retail & E-commerce