With more people getting into online shopping, it’s only natural for more and more retailers to begin their expansion into online retailing. The digital age has pushed the Internet to become more than just a hub of information and entertainment. There’s also a lot of money to be made off of the Internet market, and by a lot, we do mean a lot.
Once you’ve got your online storefront up and running, it’s time to look into a tool that can help increase your conversion and boost your sales: product recommendation engines.
What are Product Recommendation Engines?
Product recommendation engines are a subclass of information filtering systems that try to predict the preferences of a user when it comes to a certain item, product, or service. It does so by using a platform that is either built from characteristics of the item or from characteristics of the user base and social environment. In other words, these engines make recommendations and provide alternatives to users based on their browsing history, user profile, or on the current item that they’re looking at.
How are Product Recommendation Engines Implemented?
There are three approaches for implementing product recommendation engines: collaborative filtering, content-based filtering, and hybrid recommender systems.
Collaborative filtering collects information, such as user behavior, activities, and interests, from a large user base. This data is then used to provide recommendations to a user based on his or her similarity to the wider pool of users. In a way, you could look at user-based collaborative filtering systems as similar to asking a friend or acquaintance for a recommendation.
Content-based filtering, which is another way to implement the system, makes use of algorithms instead. The engine tries to predict the preferences of the user by using his or her previous browsing history, as well as take into consideration what the user is currently looking at. This method applies concepts from information retrieval and information filtering research.
Lastly, hybrid recommender systems make use of a combination of methods from both collaborative filtering and content-based filtering. This approach has been found to be the most effective as it can provide more accurate recommendations to users.
What are Product Recommendation Engines Used For?
A lot of eCommerce sites have product recommendation engines installed. When a customer views a certain product, a list of related items will be generated and displayed on the page. One of the largest retailers on the Internet that makes use of these engines is Amazon. The popular auction site eBay also implements this platform.
Aside from retailers, entertainment and music streaming services such as Pandora Radio and Netflix also make use of product recommendation engines to give a list of related titles for users to stream, watch, or download.
The use of product recommendation engines is not limited to online commerce as well. It’s also used by social networking sites like Facebook and MySpace, review sites like Rotten Tomatoes, online databases, and blogs, among others.
Benefits of Product Recommendation Engines
Product recommendation engines provide more exposure for your goods and services to your customers. A user will normally log onto a site, search for a specific item, and view the product page before checking out. Many retailers then display related items and products on that page that the customer might also be interested in. There is a high probability that the customer will check out the recommended goods because it is related to what he or she is looking at or may be looking for.