Recommendation Engines Generate Revenue for E-Commerce Companies
Amazon was one of the first major companies to use recommendation engines to suggest items to visitors. A recommendation engine for e-commerce is a piece of software that can be integrated into existing platforms to help e-commerce websites or applications recommend products based on visitors’ preferences and on-page activity. The aim is that visitors to continue shopping when they encounter various recommendations. In fact, the right product recommended at the right time drastically increases the chances of a sale.
How Can Companies Use Product Recommendation Engines?
Recommendation engines are effective because they enable e-commerce businesses to personalize their offers by showing relevant items at various stages of the shopping journey.
For instance, many e-commerce stores rely on user activity on the website to determine which items to show. By doing so, businesses increase the likelihood of upselling or cross-selling products by using a recommendation engine for e-commerce.
Recommendation Algorithms
There are three main forms of recommendation algorithms used by most e-commerce stores.
Collaborative Algorithm
This method displays recommendations that are based on the actions of similar shoppers. The
The thinking here is that customers who liked a certain product will probably also like other similar products. For instance, a lot of customers who bought a certain type of vintage poster also bought a retro t-shirt. Therefore, when a user buys this poster, they will see recommendations for various t-shirts.
Content-Based Algorithm
Content-based recommendation algorithms rely on customer preferences to recommend items.
One of the ways recommendation engines can make these kinds of suggestions is by analyzing customers’ preferences by asking them to upvote or downvote various types of products and/or content.
Hybrid Recommender Algorithms
Major e-commerce businesses use hybrid recommendation filtering methods because they are the most effective at showing highly-personalized recommendations. It merges both the collaborative and content-based filtering systems to make recommendations.
Recommendations Best Practices
Below is are some of the most common recommendation strategies that have produced great results for leading e-commerce stores.
- Using a ‘Suggested For You’ recommendation is a very effective tactic that uses the customer’s browsing activity to make personalized recommendations. Use customers’ names for an added personalized touch.
- ‘Frequently Bought Together’ suggestions have a proven track record of increasing the average order value.
- ‘Similar Items’ strategy shows products that are similar to the product currency being viewed on the page. It works well because customers can see the entire range of a product line, which makes it easier to choose the one that interests them the most.
- ‘Featured Recommendations’ display attractive offers that customers haven’t even considered.
The final opportunity for e-commerce stores to make suggest products is on the shopping cart page, before the checkout page.
Next Steps
Implementing highly-relevant recommendations allows e-commerce stores to recreate the level of personalization once enjoyed in physical shops (before the digital revolution).
Every effective recommendation software solution is built on top of a business rules engine, which allows businesses to create algorithms that suit their business goals. A rules engine is user-friendly, which empowers non-technical employees to write filtering algorithms in real-time without asking for help from the IT department.
