Why "Frequently Purchased Together" Drives AOV Beyond the Main Product

"Frequently Bought Together" (FBT) modules represent one of the most effective cross-sell strategies available to online retailers. These recommendations leverage actual purchasing behavior to suggest items that complement what's already in a customer's cart. Unlike generic product suggestions, FBT displays specific combinations that real shoppers have validated through completed transactions, resulting in higher conversion rates and increased average order values.

In this blog, we will explore why FBT recommendations outperform other cross-sell approaches, identify which businesses benefit most from this strategy, explain how to determine optimal product pairings, and provide technical guidance for implementation across different eCommerce platforms.

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Why Frequently Bought Together Works

FBT recommendations succeed because they tap into fundamental consumer psychology and behavioral patterns. Here's why this approach consistently delivers results:

• Social proof drives trust: When customers see items frequently purchased together, they perceive these combinations as validated by other shoppers, reducing purchase anxiety.
• Convenience eliminates friction: Shoppers don't need to search separately for complementary items, streamlining their buying journey.
• Value perception increases: Bundled suggestions create the impression of savings and completeness, encouraging larger purchases.
• Purchase momentum capitalizes on intent: After committing to buy one item, customers are psychologically primed to consider related products.
• Relevance reduces decision fatigue: Unlike browsing endless options, FBT presents curated choices based on actual purchasing patterns.
• Cross-category discovery happens naturally: Customers discover complementary items they might not have considered independently.

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Difference Between Recommendation Types

Understanding how FBT differs from other recommendation strategies helps merchants deploy the right approach for specific goals:

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Who Benefits From Frequently Bought Together?

Stores selling products with natural complementary relationships see the strongest results from FBT implementations. The strategy works best when customers typically need multiple related items to achieve their goals.

• Electronics retailers experience substantial gains when pairing devices with accessories. A customer purchasing a camera naturally needs memory cards, lenses, and protective cases. Phone buyers need cases, screen protectors, and chargers. These combinations feel obvious to shoppers, making the add-on decision nearly automatic.
• Beauty and cosmetics stores benefit from product routines. Skincare regimens require multiple products used together - cleansers pair with serums, moisturizers complement treatments. Makeup sets bundle foundation with brushes and setting sprays. Customers already expect to purchase multiple items, making FBT suggestions feel helpful rather than pushy.
• Fashion retailers succeed with complete outfit suggestions. A dress pairs naturally with shoes, jewelry, and bags. The "complete the look" approach helps customers visualize full outfits while increasing transaction values.
• Home and garden merchants capitalize on project-based purchasing. Buying a sofa naturally leads to pillow and throw purchases. Garden tool purchases pair with gloves, plant food, and protective gear. Customers appreciate seeing everything they need in one place.

» Here's everything you need to know about eCommerce product bundling

Key characteristics of businesses that benefit most:

• Products with high-margin accessories or add-ons
• Items frequently purchased in multiples
• Categories where customers expect complementary purchases
• Stores with diverse product catalogs enabling cross-category recommendations
• Brands targeting increased merchandising control

» Learn more about the benefits of upsell and cross-sell personalization

How To Determine Product Pairings

Successful FBT recommendations start with understanding which products naturally belong together, combining data-driven insights with strategic business objectives to create combinations that drive actual conversions.

Start With Purchase Data

Analyze what customers actually buy together to establish your baseline recommendations:

• Use market-basket analysis to find patterns across thousands of completed orders and identify which items consistently appear in the same transactions.
• Track purchase frequency data to understand which product combinations represent genuine shopping patterns versus coincidental pairings.
• Identify statistically significant relationships between items to focus on pairings that will consistently drive additional purchases.

Handle New Products Strategically

When you lack sufficient purchase history for newer catalog items, use logical grouping principles based on product attributes and category relationships:

• Match items by category and function, such as pairing phone cases with phones or printer ink with specific printer models.
• Follow vendor compatibility guidelines for technical products that require specific accessories or components to work properly.
• Apply successful pairing patterns from similar existing products to new items until sufficient transaction data becomes available.

» Make sure you know the difference between a product category and a collection

Balance Algorithms With Human Control

Let technology process large-scale data while maintaining merchandising oversight to ensure recommendations align with brand strategy and business goals:

• Allow algorithms to process thousands of transactions efficiently and identify winning combinations that human analysis might miss.
• Enable merchandisers to override algorithmic suggestions when brand positioning, profit margins, or seasonal promotions require different pairings.
• Create capabilities for promoting high-margin items or featuring products that support broader business objectives beyond pure purchase frequency.
• Maintain exclusion controls to prevent pairings that don't make business sense even when transaction data suggests they might work.

» Unsure when it's best to cross-sell? Find out where in the conversion funnel you should cross-sell

Test And Refine Continuously

Track performance metrics that reveal what actually drives incremental sales and adjust your strategy based on real conversion data:

• Monitor click-through rates on FBT modules across different product categories to understand where recommendations resonate most strongly.
• Measure how often customers actually add recommended items to their cart versus simply viewing the suggestions.
• Compare conversion rates and average order values across different pairing strategies to identify which approaches deliver the strongest results.
• Use A/B testing to validate changes before rolling them out across your entire catalog.

» Make sure you understand the importance of personalization in online shopping

Real-World Examples of “Frequently Bought Together” in eCommerce

1. Amazon

Amazon's FBT feature is prominently displayed on product pages, often in a section labeled "Frequently Bought Together." For instance, when viewing a printer, Amazon suggests complementary items like refill kit and printing paper. These suggestions are based on customer purchase patterns, enhancing the shopping experience and increasing average order value.



2. Sephora

Sephora employs a personalized FBT strategy on their product pages. When a customer views a product, Sephora suggests complementary items like makeup brushes or related cosmetics. These recommendations are tailored to the individual, considering their browsing history and preferences, thereby enhancing user experience and boosting sales.



3. Steve Madden

Steve Madden’s “Lookbook” section pairs curated outfit ideas with FBT suggestions for matching accessories and complementary items. This approach nudges shoppers to add multiple products to their cart, enhancing engagement and driving higher AOV.



» Ready to cross-sell? Here are our top cross-selling tips

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When to Withhold FBT Suggestions

While FBT can boost sales, there are situations where showing these suggestions may backfire. Displaying irrelevant or distracting recommendations can frustrate shoppers and even reduce conversion. Consider withholding FBT in these scenarios:

• Low relevance products: If an item doesn’t have natural add-ons or complementary products, forcing FBT can seem random and diminish trust.
• Urgent or emergency purchases: Customers buying essentials or time-sensitive items usually want a fast checkout; extra recommendations can slow them down.
• Gift items or curated bundles: Pre-packaged gifts or special bundles are designed as complete offerings—additional suggestions may confuse shoppers or undermine perceived value.
• Insufficient data: When purchase history or customer behavior doesn’t clearly indicate complementary items, showing FBT risks presenting irrelevant options that feel spammy.

» Learn how to implement AI in your eCommerce store

How Fast Simon Can Boost Your Cross-Sell Strategies

When you run your eCommerce business, driving higher average order value and customer loyalty is key. Fast Simon makes it easy to implement smart cross-sell and upsell strategies, including “Frequently Bought Together” recommendations. By leveraging AI-driven personalization, you can show the right products to the right customers at the right time, increasing conversions without being intrusive. Whether your store sells clothing, electronics, or lifestyle products, these recommendation features help guide shoppers toward complementary items naturally.

The result is more orders per customer, improved retention, and a smoother shopping experience. With Fast Simon, your business can turn simple product suggestions into meaningful revenue growth while keeping the customer journey seamless.

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