Customers Also Bought Widgets: A Complete Guide

Customers also bought widgets help online stores recommend relevant products based on real purchasing behavior. Along with you may also like and related products widgets, they improve product discovery, encouraging larger orders, and creating a smoother shopping experience.

Although these recommendation widgets share a similar goal, each one works differently and supports a different stage of the customer journey. This guide explains how they work, their key differences, and how Shopify merchants can use them to increase conversions and average order value.

What are Customers Also Bought Widgets?

A customers also bought widget displays products that previous shoppers purchased together with the item currently being viewed. The recommendations come from historical order data instead of manual product selection.

For example, a customer browsing a digital camera might also see recommendations for a memory card, camera bag, extra battery, and tripod because many previous buyers bought these items together.

This approach works because it reflects actual shopping behavior. Instead of guessing what customers might want, the widget presents combinations that have already proven successful.

Why these widgets perform so well

Customers hesitate before adding complementary products because they don’t know what works well together. Purchase-based recommendations remove much of that uncertainty.

There are some factors contribute to their effectiveness.

• They introduce products that naturally complement the current purchase.

• They reduce the effort required to search for accessories.

• They increase buyer confidence through purchasing patterns.

• They encourage larger shopping carts without disrupting the browsing experience.

Rather than feeling like advertisements, these recommendations resemble helpful suggestions from previous customers.

How purchase data improves recommendations

Traditional recommendation systems often rely on product categories. For instance, a shoe product page may display other shoes because they belong to the same collection.

Purchase-based recommendations go much further.

If hundreds of customers buy a yoga mat together with resistance bands, the recommendation engine should recognize these purchasing patterns to automatically suggest those products whenever another visitor views the yoga mat.

Over time, recommendations become more accurate because each completed order provides additional information about buying habits.

Customers Also Bought vs. You May Also Like vs. Related Products

Although these recommendation widgets share a common goal, each one solves a different problem during the shopping journey.

>> Don't miss: 4 Ways To Increase AOV With Product Cross-Sells

A balanced recommendation strategy often combines all three widgets because they influence customers at different stages of the purchasing process.

For instance, a skincare brand may use related products to present different moisturizer formulas, customers also bought to recommend facial cleansers and serums, and you may also like to introduce products based on browsing behavior. Each widget supports a different buying decision while contributing to overall revenue growth.

>> Read more: How Related Products Drive More Conversions

Where Should Recommendation Widgets Appear?

Placement affects performance just as much as recommendation quality.

Different sections of a product page encourage different customer behaviors, so merchants often position recommendation widgets accordingly.

Below the product information

Many stores display customers also bought recommendations below the product description.

Customers typically finish reading product details before deciding whether they need complementary products. At this point, suggestions for accessories and bundles feel helpful rather than intrusive.

Near the add-to-cart button

Some merchants place small recommendation sections near the purchase button.

This placement reminds customers about useful add-ons while purchase intent remains high. A simple recommendation for a protective case or warranty can significantly increase cart value without distracting shoppers from the primary product.

Lower on the product page

Related products and you may also like widgets often perform well near the bottom of the page.

Visitors who continue scrolling frequently seek additional options before making a final decision. Similar products and personalized recommendations encourage them to stay in the store rather than return to search engines.

The ideal layout depends on the store's product catalog, customer behavior, and merchandising strategy. Continuous testing often reveals which placement generates the highest engagement.

Best Practices for High-Converting Recommendation Widgets

Recommendation widgets bring value only when they are relevant to customer needs.

Successful merchants focus on the quality of recommendations rather than their quantity.

Prioritize relevance

Customers respond positively when recommendations genuinely complement the products they already intend to purchase.

Showing hiking boots alongside trekking poles creates a logical shopping journey, whereas recommending unrelated electronics introduces unnecessary distractions.

Avoid recommendation overload

Too many suggestions compete for customer attention.

Displaying four to eight carefully selected products usually creates a cleaner shopping experience than presenting dozens of choices.

Keep recommendations fresh

Customer preferences evolve throughout the year.

Seasonal demand, new product launches, and changing buying trends influence which recommendations generate the highest conversions. Merchants who regularly update their recommendation logic often achieve stronger long-term performance.

Use AI for larger catalogs

Managing recommendations manually is difficult as product catalogs expand.

AI analyzes purchasing behavior, browsing patterns, and product relationships much faster than manual merchandising. AI continuously learns from new orders, allowing recommendations to improve automatically over time.

This capability explains why many growing Shopify merchants incorporate AI product recommendations into their merchandising strategy.

How Fether Simplifies Product Recommendations

As recommendation strategies are more sophisticated, merchants often need multiple tools to manage bundles, cross-sells, and personalized product suggestions. Switching between separate applications can increase complexity and bring inconsistent shopping experiences.

Fether addresses this challenge by combining several recommendation features within a single Shopify solution.

Instead of relying on one recommendation method, Fether supports AI-powered frequently bought together offers, customers also bought sections, related products, product bundles, buy together promotions, and volume discounts.

>> Don't miss: 6 Product Bundles Types Every Store Should Know

Its AI analyzes order history to identify purchasing patterns, allowing merchants to display relevant product combinations without manually configuring every recommendation. Businesses that prefer greater control can also create custom bundles and merchandising rules that align with specific marketing campaigns.

This combination of automation and flexibility allows stores to serve both data-driven recommendations and carefully curated offers, helping merchants increase AOV while maintaining a seamless shopping experience.

Rather than replacing merchandising decisions, intelligent recommendation tools support them by surfacing products that customers are genuinely more likely to purchase together.

Install Fether

Final Thoughts

Customers also bought widgets help shoppers discover complementary products, while related products and you may also like widgets support product comparison and personalized browsing. Using these recommendation strategies together creates a smoother shopping experience and encourages a higher AOV.

For Shopify merchants, solutions like Fether make it easier to manage AI-powered recommendations, product bundles, and cross-sell offers in one place. The right recommendation strategy helps customers find products they value while driving sustainable revenue growth.

FAQ

What are customers also bought widgets?

Customers also bought widgets, displaying products that previous shoppers frequently purchased together with the current product. These recommendations rely on historical order data, making them highly effective for cross-selling complementary products and increasing average order value.

What is the difference between customers also bought and related products?

Customers also bought recommendations that come from real purchasing patterns, while related products Shopify widgets usually recommend items with similar categories, collections, tags, or attributes. The first strategy encourages larger purchases, whereas the second helps customers compare similar options before making a decision.

Do AI product recommendations improve eCommerce conversions?

Yes. AI product recommendations analyze order history, browsing behavior, and product relationships to deliver more relevant suggestions than manual recommendations alone. More accurate recommendations often increase customer engagement, AOV, and overall conversion rates because shoppers receive products that better match their interests and buying intent.