Rebuy

Post-Purchase Personalization with Rebuy + Order Editing

Rebuy is the leading AI-powered personalization engine for Shopify, driving revenue through intelligent product recommendations. When combined with Order Editing, Rebuy's personalization extends into the post-purchase window—turning order modifications into revenue opportunities.

How the Integration Works

Intelligent Recommendations in the Edit Experience

When customers access Order Editing to modify their order:

• Rebuy AI analyzes their purchase history and current order
• Personalized recommendations appear in the edit interface
• Smart suggestions drive additions to the order
• Revenue increases without additional acquisition cost

Data Powers Better Predictions

The integration creates a feedback loop:

1. Customer modifies order via Order Editing
2. Rebuy learns from the modification behavior
3. Future recommendations improve
4. Conversion rates increase over time

Key Benefits

1. Higher Post-Purchase Conversion

Rebuy's AI is already optimized for your catalog. In the edit experience:

• Show products likely to convert for each customer
• Display items that complement the current order
• Highlight new arrivals the customer would love
• Recommend based on browsing and purchase history

Result: 34% higher add-to-order rates vs. generic recommendations.

2. Increased Average Order Value

Every edit session is an opportunity:

• Customers are already engaged
• They're in buying mode
• Friction is minimal (already authenticated)
• Payment information is on file

Result: $18.50 average additional revenue per edited order.

3. Better Customer Experience

Personalized suggestions feel helpful, not pushy:

• "Based on your order, you might like..."
• "Complete the look with..."
• "Customers who bought [item] also added..."
• "Don't forget..."

Real Results from Rebuy + Order Editing Merchants

Apparel Brand

A fashion retailer using Rebuy across their store:

• $340,000 in additional post-purchase revenue annually
• 23% of order editors added recommended products
• 41% higher AOV on orders with post-purchase additions
• AI accuracy improved by 18% from edit behavior data

Beauty Company

A skincare brand with complex product relationships:

• "Complete your routine" recommendations converted at 28%
• Subscription upsells from one-time purchases increased 45%
• Cross-category recommendations drove discovery
• Customer LTV increased by $47 per customer

Food & Beverage

A specialty food brand:

• Flavor recommendations based on previous purchases
• Bundle completion suggestions drove volume
• Seasonal products surfaced to interested customers
• Reorder suggestions timed with consumption patterns

Integration Features

Recommendation Types

Recommendation	Use Case
Complementary	Products that go with the current order
Frequently Bought Together	Proven product combinations
Similar Items	Alternatives the customer might prefer
Trending	Popular products the customer hasn't tried
Personalized	AI-selected based on individual behavior
Recently Viewed	Products the customer was considering

Smart Display Logic

Rebuy knows when to show recommendations:

• Low-value orders: Suggest items to reach free shipping
• Repeat customers: Show new products they haven't tried
• First-time buyers: Recommend best-sellers
• Cart contents: Complement what's already there

A/B Testing Integration

Test recommendation strategies:

• Different algorithms
• Placement variations
• Messaging options
• Offer types

Customer Journey Example

Original Purchase

Customer buys running shoes.

Edit Session Begins

Customer opens Order Editing to change shoe size.

Rebuy Recommendations Appear

"Complete your run"

┌─────────────────────────────────────────────────┐
│ [Running Socks - $15]  [Insoles - $25]          │
│ Customers who bought these shoes also added...  │
│                                                  │
│ [Shoe Cleaner - $12]  [Spare Laces - $8]       │
│ Frequently bought together                      │
└─────────────────────────────────────────────────┘

Customer Adds Items

Running socks added to order.

Result

• Original order: $150 (shoes only)
• Post-edit order: $165 (shoes + socks)
• Incremental revenue: $15
• Customer satisfaction: Higher (better prepared for their run)

Implementation

Setup Process

1. Connect Rebuy in Order Editing dashboard
2. Configure recommendation widgets for edit experience
3. Set up A/B tests to optimize performance
4. Launch with monitoring
5. Iterate based on performance data

Optimization Strategies

• Start with proven performers: Use your best-converting Rebuy widgets
• Test placement: Above vs. below order summary
• Experiment with messaging: Direct vs. suggestive copy
• Analyze by segment: What works for different customer types

Technical Details

Data Shared with Rebuy

• Current order contents
• Customer purchase history
• Edit behavior patterns
• Session context

Recommendation Customization

• Define which products can be recommended
• Set price thresholds
• Exclude specific items or categories
• Configure display rules

Why Growth-Focused Brands Choose This Integration

"Rebuy was already driving significant revenue for us in-cart and on product pages. Adding it to Order Editing was a no-brainer—now we're capturing revenue from customers who are already engaged and ready to buy more."

— Director of eCommerce, Apparel Brand

Rebuy + Order Editing extends your personalization strategy into post-purchase, capturing revenue that would otherwise be left on the table.