Product Recommendations: Types, Examples, & How to Implement

Product recommendations are non-intrusive nudges shoppers that help customers find products related to their current purchases. 

They help increase order value and are also capable of providing memorable shopping experiences that persuade customers to return for repeat orders. 

If you want to offer such product recommendations in your store, this article will guide you from their types and examples to implementing them in your store. 

Types of product recommendations

You can provide different types of product recommendations to your audience in e-commerce. 

Personalized recommendations

Personalized recommendations use customer behavior, preference, and purchasing history to offer suggestions. These recommendation engines usually use data science and machine learning to produce what customers might be interested in. When you deliver the same, it increases the likelihood of a purchase. 

With personalized recommendations, you can: 

• Provide good customer experience. It improves customers' shopping journeys by showing products that match their tastes. 
• Increases conversions. People are more likely to purchase products that are relevant to them. 
• Increases customer loyalty. Customers return for repeat purchases when you send relevant product recommendations based on their buying patterns. For example, SmartrMail sends recommendations through emails based on customer browsing and purchasing behavior. 

Cross-Selling

Cross-selling suggests additional complementary products for those customers who are currently shopping for or have already purchased from you. It increases the total order value by showing customers products they are more likely to buy. 

It gives e-commerce stores a chance to increase average order value while improving the shopping experience for buyers. Relo, a software that helps increase repeat revenue, makes it easier to cross-sell or upsell in a shopper’s journey. The tool automatically identifies complementary products and sends recommendations to make customers’ shopping experience more personalized. 

Upselling

Upselling encourages customers to purchase a higher-end version of the product they’re considering. Additional features or services might encourage buyers to spend more on an upgraded product than a basic one. 

For example, if a customer is looking at a standard smartphone, an upsell might involve suggesting the premium version with more storage and better camera features. Think about purchasing an iPhone 15 Pro Max 1TB version rather than the iPhone 15 Pro Max 256 GB. Both have similar features, but the former offers more storage, allowing Apple to charge premium pricing. 

Frequently bought together

These recommendations suggest products that people often purchase in a bundle. They use users' collective buying behavior to offer suggestions. Data analysis plays a major role in identifying and recommending common product combinations to customers.

Frequently bought-together notifications allow e-commerce stores to increase the average order value in their business, helping them drive more revenue. 

New arrivals and best sellers

Showcasing new arrivals and best sellers is a reliable way to capture buyers' interest. The latest products attract shoppers who are on point with the latest trends. When you add limited-time availability with new products, it creates a sense of urgency and exclusivity while purchasing. 

You can use these signals to deliver social proof to other website shoppers as these products start selling out. This would further increase your sales as new buyers would be reassured by popularity and positive feedback from other buyers. 

Benefits of product recommendations in e-commerce

Product recommendations benefit e-commerce stores in several ways. 

Streamline product discovery

Product recommendations make it easier to find products that customers look for. When you put the right product in front of the right customer at the right time, it creates a more seamless and enjoyable shopping experience. It allows shoppers to discover products faster, increasing their satisfaction in their buying experience. 

Offer personalized experience

Personalization increases engagement. It’s not “nice to have” anymore. Shoppers expect personalized suggestions from you to make their shopping journey easier. Suppose buyers are interested in purchasing a camera. Showing recommendations for DSLR cameras, similar to the ones they added to their wishlist a year ago will shorten their journey to find what they’re truly seeking. 

This level of personalization with recommendations makes the shopping experience memorable and encourages customers to return time and again to experience the same. 

Improve conversion rate

Targeted recommendations improve shoppers' confidence in your business. When you recommend relevant products, shoppers feel that you value their needs, and they show greater loyalty toward the brand. 

Such shoppers don’t need much convincing to transform them into customers. They have a comparatively greater trust that motivates them to purchase. It clearly reflects on your conversion rate. 

Increase average order value (AOV)

Product recommendations help in upselling and cross-selling, increasing the amount a shopper spends on one transaction. They also increase the average order value, as shoppers will likely purchase complementary products that support their purchase. 

Improves ROI on customer acquisition cost (CAC)

Product recommendations that encourage shoppers to add more products to their cart or bring them back to the store improve the ROI of the cost spent to acquire them. Upselling or cross-selling increases the average order value (AOV), and repeat purchases contribute to revenue expansion. It also increases the customer's lifetime value, improving the ROI you get on customer acquisition costs.

Top 5 product recommendation examples

Below are five product recommendation examples that we found amusing and thoughtful. 

1. The North Face

The North Face, an outdoor performance and sports gear company, offer product recommendations based on shoppers' behavior on the website. 

It populates recommendations in a “Just For You” section based on the items you viewed previously. These recommendations help visitors consider different alternatives to what they’re looking for.

2. Sephora

Sephora, a luxury cosmetics and skincare brand, goes one step deeper into offering ratings and ingredients of products that may interest you. It expedites product discovery and makes it easier to compare options that are either substitutes or products that complement what you’re looking for. 

3. Chewy

Chewy, a popular pet products store, offers recommendations based on your search history on the store. You'll find related products on the same page when you search for a product on their online shop. This gives customers more options, and they’re happy to consider as, in this case, people are shopping for their adorable pets.

4. USAHair

USAHair sells hair extension products. Their website asks shoppers about the type of hair extension they’re looking for based on the hair-thinning patterns they observe. The product pages suggest similar products with more details on the kind of coverage a shopper would want. 

5. Amazon

Amazon, the e-commerce giant, does a fantastic job of recommending products that shoppers purchase frequently. It adds a CTA to add the whole bundle to the cart, making the shopping journey easier for the buyer if they want to buy the bundle. 

Moreover, it suggests similar items based on the current session intent, keeping the recommendations relevant to shoppers' interests. 

How to implement product recommendations in your online store

You need the right technology to show relevant product recommendations to e-commerce store visitors.

Use the right technology

Adopt a product recommendation engine to predict customers’ actions rather than reacting to them. You need a tool that can combine your products, customers’ preferences, and the intent of their current session to deliver recommendations that hit all the sweet spots. 

Some e-commerce platforms offer recommendation technology. In addition, some products specialize in showing recommendations, offering several features and ways to deliver them. 

Below are technologies that complement product recommendation engines.   

• Fomo. Delivers personalized push notifications with social proof, adding more credibility for shoppers to purchase recommended products. 
• Smartrmail. Integrate with popular e-commerce platforms to deliver different types of product recommendations over email. 
• Relo. Recognize and use upsell and cross-sell opportunities with relevant recommendations to maximize e-commerce revenue. 

Collect and analyze data

Make sure you have the systems set to collect and analyze data related to completed orders and deliveries. 

Analyze this data to gain insights about your customers and their journey. This analysis will help you improve the quality and personalization of your recommendations.  These insights will also help you segment and target customers based on their demographics. 

For example, you’ll show US recommendations for the Black Friday or Cyber Monday sale. In contrast, Diwali sales observed in India will have different recommendations (with products like gift hampers or clothes). 

You don’t need to be a global store to observe changing demographics. They can easily change from state to state. 

Use AI and ML to show substitute and complementary products

Ensure that the recommendation engines you select have AI and ML functionalities to show substitute and complementary products to customers. The AI models these systems use work using vectors. These vectors are points in an n-dimensional space where products are stored. 

AI models semantically search for similarities between two vectors to offer relevant suggestions when suggesting substitutes or similar items. 

To wrap up, ensure your recommendation engine integrates seamlessly with your e-commerce platform. This will help you manage everything from one place instead of hopping between applications, trying to offer your customers the best possible experience. 

Best practices to follow while providing product recommendations

When you’re all set to offer product recommendations that make sense for your customers, follow these practices to observe an optimal output. 

• Make sure the recommendations you offer are relevant. If they're not contextual, product recommendations can throw people off. It’s necessary to map the current session’s intent with the product to deliver the right recommendations to shoppers. 
• Design recommendations to improve shoppers’ experience. Ensure that the recommendation you supply makes it easier for shoppers to discover and navigate the product. 
• Be transparent when you collect shoppers' data. Always add a disclaimer when you collect shopper data. Since you’re doing it to improve their shopping experience, it’s best to be transparent about it, considering shoppers’ concerns related to data privacy. 
• Monitor performance consistently. It’s advisable to A/B test product recommendations to optimize them for relevance and shoppers’ intent. This will allow you to improve the quality and personalization of your supply recommendations to your audiences. 

Bringing it all together

Take inspiration from the above examples and follow the implementation steps to offer product recommendations on your e-commerce store. 

It’s easier said than done, but with the right technology, you can offer product suggestions that increase average order value and conversions. Recommendation engines would be of little use as a standalone system. It’s advisable to pair them with social-proof marketing software that can create FOMO around your recommended products. It will encourage buyers to make a purchase faster. 

Learn more about how FOMO marketing can help you increase conversion on your e-commerce store.