What is a Product Recommendation Engine?

Whether you realize it or not, you have experienced the power of product recommendation engines. This is the ecommerce technology at work when you are shopping online and the site shows you other items “you might also like.” Or when you go on social media and see ads from the brand showcasing similar or complementary products. Or when you get an email or text from the brand suggesting relevant items and encouraging you to buy them, perhaps with a personalized promotion code. These are examples of product recommendation engines in action.

Product recommendation engines analyze data about shoppers to learn exactly what types of products and offerings interest them. Based on search behavior and product preferences, they serve up contextually relevant offers and product options that appeal to individual shoppers — and help drive sales.

Let’s look at how product recommendation engines work and why they’re a helpful tool for both shoppers and retailers.

A product recommendation engine is a technology that uses machine learning and artificial intelligence (AI) to generate product suggestions and predictive offers, such as special deals and discounts, tailored to each customer. An effective product recommendation engine analyzes data and uses the results to create accurate, individualized customer profiles. These profiles help the engine generate the exact kind of content or products a specific customer might be interested in. That’s why you may get frequent follow-up emails from your favorite brands based on recent purchases and site searches.

Product recommendation engines analyze the following types of customer data:

• Browser history
• Current purchasing behavior
• Feedback
• Most-viewed products
• Preferences
• Previous purchases
• Recently viewed items
• Search history
• Shopping carts
• Wish lists

Based on that data, the technology can surface relevant products the customer might like. It can intelligently anticipate customer intent and include the recommended products in marketing materials, on an app, in site searches, and on ads featured on other web pages.

Product recommendation engines typically rely on sophisticated algorithms . These algorithms take into account massive amounts of customer data, including purchase history, preferences, and search behavior.

The algorithm enables set processes to automatically generate appropriate recommendations based on the customer data. The system then delivers the best suggestions for each individual. When new information about the customer becomes available, the system incorporates that criteria and offers updated recommendations.

Product recommendation engines differ based on the specific kind of information they collect and how they use it to determine the products they suggest to a customer. There are three common approaches:

• Collaborative filtering systems
• Content-based filtering systems
• Hybrid recommendation systems

A collaborative filtering system analyzes data from multiple customers to predict what products will be of interest to a particular individual. It harnesses the wisdom of the crowd to offer highly effective product recommendations.

For example, a customer looking at a coffee machine on a lifestyle website may see recommended items purchased by other customers who viewed the same product. They may also see items customers purchased along with the coffee machine, like a milk frother.

Collaborative filtering is a good option for large brands that have access to large amounts of customer data.

A content-based filtering system analyzes each individual customer’s preferences and purchasing behavior. The system creates a unique preference profile and offers recommendations based on the customer’s personal tastes. This type of filtering system is usually behind the “Since you bought this, you’ll also like this …” recommendations.

A hybrid recommendation system offers a combination of filtering capabilities, most commonly collaborative and content-based. This means it uses data from groups of similar users as well as from the past preferences of an individual user.

Hybrid systems usually run these analyses separately and then combine them to offer tailored product recommendations. For example, an ecommerce store might cross-reference data from customers who have bought ring lights with an individual customer who bought a ring light. The filtering system might find that other customers also bought lapel mics and that the individual user searched for microphones in the past. The ecommerce store might then showcase a lapel mic as a recommended product.

The right product recommendation technology gives businesses the power to use client behavior data to optimize their own customer service efforts while also increasing the potential ROI of their marketing efforts.

A product recommendation engine can raise awareness of the brand or new products and increase revenue and customer satisfaction in a number of ways. Consider these benefits of using tailored product recommendations:

• Generate higher click-through rates
• Increase average order value
• Boost conversion rates
• Lock in more revenue
• Perfect your customer experiences

It’s easy to see the immediate impact of an intelligent product recommendation engine. Studies have found that the click-through rate of personalized recommendations is twice the click-through rate of non-personalized recommendations.

Shoppers who engage with AI-powered product recommendations have a 26% higher average order value (AOV). Intelligent product recommendations allow for natural, logical opportunities to upsell and cross-sell. Customers demonstrate interest through their behavior and history, and the product recommendation tool automatically offers suggestions. Small transactions become larger ones, and customers who might not have been on the path to make a purchase suddenly find themselves with a full cart.

One example is “complete the set.” As a shopper views one product, the recommendation engine surfaces complementary products, such as pants and shoes to match a blazer. Seeing the item in the context of the product set can increase the inclination to buy.

When shoppers click on product recommendations, the chance they’ll complete the sale nearly quadruples . That likelihood continues to increase the more they engage with the suggested products.

Predictive product sort and personalized product recommendations boost conversions because they ensure customers find the products they need and want the most. With the help of AI, brands can automatically tailor search and category pages based on every action a shopper makes. This includes micro-moments on mobile devices.

Brands can also use product recommendation engines for strategic upsells and to promote slow-moving inventory. With higher conversion rates and more money spent per transaction, revenue increases. A study by Barilliance suggests that up to 31% of ecommerce site revenue is generated from personalized product recommendations.

Product recommendations provide customers with highly relevant content suited to their unique needs, interests, and buying habits. Customers feel the benefits from that tailor-made experience. And the more they encounter personalized recommendations, the more they expect to get them every time and every place they shop online. Using product recommendation engines is a competitive advantage against brands that have not yet embraced personalization and AI.

Now that you know what product recommendation engines are, how they work, and the benefits they provide, it’s time to start thinking through how you can take your retail experience to the next level. For example, you can use predictive offers, such as a coupon or notification about a special sale, to anticipate spending habits and create hyper-targeted marketing campaigns.