Frequently Bought Together

Boost average order value (AOV) by recommending complementary products that customers often purchase together.

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Frequently Bought Together

What are Frequently Bought Together campaigns?

Frequently Bought Together campaigns target users who have just purchased or added items to cart by recommending complementary items that are commonly bought as sets. This strategy leverages purchase data and customer behavior patterns to suggest relevant add-ons immediately after cart addition or order confirmation, increasing basket size through data-driven product bundling.

Why Frequently Bought Together Matters

Challenges

Many customers buy only the primary product, leaving potential cross-sell revenue untapped. Without reminders, AOV remains flat.

Opportunities

By suggesting items that naturally complement what the customer already chose, you make shopping easier while boosting revenue.

Outcomes

Higher Average Order Value

Increased Customer Satisfaction

Better Product Discovery

Who is it for?

Audience

Users who just purchased or added items to cart, excluding customers who have already seen recent cross-sell recommendations.

Exclusions

Customers who already purchased the recommended complementary items, users currently in other product recommendation campaigns, or recent buyers who made purchases within the last 24 hours.

How it Plays Out

A sample sequence for this use case.

day
Day 0

You might need these too → Most customers of [Product] also got these — complete your set now → [Buy Now]

day
Day 1

Smart pick, [Name]! Add these to complete your set → [Get these now]

day
Day 3

Don't forget the essentials that go with your recent purchase → [Complete Your Order]

day
Day 7

Final reminder: Perfect pairings for your [Product] → [Shop Complements]

Best Practices

Frequently Bought Together Examples & Prompts

Channel Examples

Email
Subject: You might need these too Body: Most customers of [Product] also got these — complete your set now for the full experience. [Buy Now]
WhatsApp
Copy
Smart pick, [Name]! Add these to complete your set and get the most from your purchase [Get these now]

Automate with Zenie Prompts

Analyzing purchase patterns and triggering relevant recommendations requires sophisticated data analysis. With Zenie, you can automatically identify frequently bought together patterns and create personalized recommendation campaigns.

Segment Prompt

Segment users based on specific product combinations frequently bought together ⧉

Copy
Journey Prompt

Trigger post-purchase or cart add message recommending complementary items frequently bought together. ⧉

Copy
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Explore Similar eCommerce Marketing Strategies

Bundle & Save

Incentivize multi-item purchases with discounts.

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Upsell Recommendations

Encourage buyers to upgrade or bundle for higher order value.

See Details

Cross-Sell Campaigns

Suggest complementary products to complete the purchase.

See Details

FAQs

Why are frequently bought together recommendations effective?

They work because recommendations are based on real customer behavior data, making suggestions feel relevant and helpful rather than random. Customers trust recommendations when they see that others with similar purchases found value in the combinations.

How should eCommerce brands identify which products are frequently bought together?

Analyze transaction data to find products that appear together in orders above average frequency. Focus on combinations that have statistical significance and make logical sense from a customer usage perspective.

What timing works best for frequently bought together campaigns?

Send recommendations immediately after cart addition or purchase confirmation when buying intent is highest. Follow up within 24-48 hours while the original purchase decision is still fresh in the customer's mind.

How do you measure frequently bought together campaign success?

Track average order value increases, attachment rates for recommended products, and overall revenue per customer. Compare performance against generic product recommendations to measure the effectiveness of data-driven suggestions.