What Is a Retail Product Recommendation Engine – And Why Do You Need One?
Whether you realize it or not, you have experienced the power of product recommendation engines. This is the 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.
What is a product recommendation engine?
Product recommendation engines analyze the following types of customer data:
- Browser history
- Current purchasing behavior
- Most-viewed products
- 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.
How do product recommendation engines work?
1. Go from placements to relationships
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.
3 types of product recommendation engines
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
Collaborative filtering 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.
Content-based filtering systems
Hybrid recommendation systems
How product recommendation engines can benefit your business
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
Generate higher click-through rates
Increase average order value
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.