How E-Commerce Retailers Use AI to Increase Average Order Value and Conversion Rates with Personalized Recommendations

By: GoBeyond Team

August 10, 2026

3 min read

E-commerce AI recommendation engine interface

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

More Case Studies

See All

How Coca-Cola Uses AI to Drive Hyper-Personalized Marketing and Content Creation

Marketing & Advertising

How Johnson & Johnson Reduced Absenteeism by 20% with AI Health Analytics

Healthcare & Medical

How Perform[cb] Improved Lead Quality and Conversion Rates with an AI Chatbot Built on Amazon Bedrock

Technology & SaaS

How Six Flags Enhanced Guest Experience and Operations with Google Cloud Vertex AI-Powered Digital Concierge

Hospitality & Tourism

How James Hardie Leverages Sprout Social’s AI Tools to Enhance Social Media Strategy and Consumer Engagement

Manufacturing

How General Electric (GE) Uses AI to Enhance Employee Performance and Predictive Maintenance

Manufacturing