What is a Product Recommendation Engine
A product recommendation engine scans large amounts of information such as browsing behavior and shopping history in order to make personalized suggestions for each customer.
Essentially, recommendation engines are sophisticated filtering systems that anticipate and show the items a customer might like to purchase. Behind each effective recommendation engine is a business rules engine that makes countless decisions in an instant.
Data Used to Make Suggestions
Brands use various information to suggest items to customers including the following:
- Browsing Behavior
- Customer Ratings
- Recently Viewed Items
- Shopping History
- Session Duration
Using this information, a recommendation engine can offer relevant products that might pique the interest of customers.
Recommendation Methods
Recommendation engines are classified according to the type of information they use to make suggestions. Most fall into these groups:
- Collaborative Filtering
- Content Filtering
- Hybrid Filtering
Let's now take a look at how these filtering systems function.
Collaborative Recommendations
The collaborative recommendation typically makes suggestions based on the purchase and browsing information for a few items by many visitors. The most common example of collaborative filtering is the suggestion, “people who bought this item also bought that item”.
Content-Based Recommendations
Content-based algorithms display items that resemble ones that the customer has liked or purchased in the past. These types of recommendation engines generate the ‘since you bought this product, you’ll also like this product’ suggestions on many ecommerce websites.
Hybrid Recommendations
As the name suggests, the hybrid method uses features from both the collaborative and content-based filtering systems. It combines information from similar customers with the past preferences of each site visitor.
For example, an online retailer might use data on customers who bought a gaming desktop computer with an individual customer who purchased a gaming desktop computer.
The filtering system can be set up to display high-definition monitors because other customers who purchased desktop computers for gaming also bought high-definition monitors and the individual shopper also browsed for monitors in the past.
Benefits Recommendation Engines
A product recommendation engine boosts brand awareness, customer retention, and drives revenue.
Average Order Value
Filtering systems are a great way to cross-sell, upsell, and sell slow-moving products.
Product recommendation engines that display to your site visitors a large amount volume of useful items that complement their purchases are likely to be of use to them.
Bundle Up Items
Brands can use recommendation engines to put together several related items into one bundle to upsell or cross-sell products across different description groups.
Online retailers can use customer behavior and shopping history are very useful data points that enable a recommendation tool to suggest items that could be bundled up together.
Increasing Customer Retention Rates
Making product recommendations is not as simple as it may seem. Businesses need to make sure that their recommendations are relevant in order to avoid showing items that don't are out of stock or don't ship to a customer’s location. Properly configured product recommendation engines take all these parameters into account to provide a seamless customer journey.
The Customer Experience Improves with Personalization
Product recommendations engines give visitors personalized content based on data that includes their, interests, needs, and shopping behavior.
When consumers find what they’re searching for along with other relevant products with ease, the customer experience is improved.
