Were you ever doubtful whether product recommendations worked? Judging by Amazon's success, one can only say it does. And believe it or not, 35% of Amazon's revenue stems from personalized product recommendations.
Besides, there are other interesting facts that support the invaluable benefits of a product recommendation engine. Let's take a look.
- As opposed to the 19% of the first-time visitors who didn't click on a recommendation, 37% of first-time visitors who clicked on recommendations returned.
- Purchases influenced by recommendations witnessed a 10% higher AOV.
- An online shopper who follows a recommendation is 4.5 times more likely to add items to the cart and complete the purchase.
Convinced that a product recommendation engine is just what you need to increase your sales? Well, read on, we have informative and actionable insights for you on all things product recommendations.
Table of Contents
We are talking about product recommendation as a strategy. But what do you need to actually embed it to your eCommerce store? A product recommendation engine.
Typically, these recommendation engines filter and sort your eCommerce store's product offers based on a combination of rules. They use product data such as reviews, number of views, sales, and others to recommend relevant products to the ideal shopper without having them search for the products.
Additionally, the recommendation engines analyze the collected user-specific data—such as the customer's most viewed products/categories, purchase history, etc. to match the most relevant offer with the optimal shopper.
Having a product recommendation engine can help you in special offer personalization, email marketing campaign optimization, up-selling and cross-selling strategies, and so much more.
Product recommendation engines are operated by underlying algorithms. And there are three basic ways of configuring these algorithms:
- Content-based filtering system
- Collaborative-filtering system
- Hybrid filtering system
All three methods use artificial intelligence (AI) and machine learning algorithms to provide personalized recommendations. Though the mathematical principles behind these filtering systems are complex, their application to your store isn't at all going to be overwhelming.
Content-based filtering system
This type of product recommendation engine analyzes the customer data based on the browsing history and the likes and dislikes of users. The basic idea behind this algorithm is that if person X likes item Y, then they might also enjoy item Z. So if the user wishlists casual capri pants, the filtering system would start recommending similar products.
Collaborative filtering system
The collaborative filtering system fetches data from users who have purchased similar products and then combines that information to make recommendation decisions. The benefit of using this kind of recommendation engine is that it is capable of making complex recommendations even without having to understand what the product is.
It works like this: If customers A and B, like a set of products, then the collaborate filtering system would assume that they have similar interests. In turn, it would recommend products that customer A bought to customer B, and vice-versa.
Hybrid filtering system
As the name suggests, hybrid recommendation systems combine both content-based and collaborative filtering systems to focus the output based on shopper-specific attributes. They render greater personalization and cater to higher conversion rates.
In this section, we’ll take a look at the different strategies offered by personalized product recommendations engine—including how they work and when you should use them.
Most popular items
One of the most common and relevant product recommendation strategies is to display the most popular items of your eCommerce store. They can be branded as top items, best-sellers, and others. Now, these items are scored to the top of the board through the weighted summation of interactions such as purchases, additions to the cart, and product views.
The product recommendation engine would favor the recent interactions in this regard over historical ones to update the score every time a data feed is synchronized.
User affinity items
Effective product recommendation engines strive to excel in customer experience and customer satisfaction by showing relevant items at just the right time. And affinity-based recommendation systems make a compelling and personalized recommendation to those they matter the most.
When shoppers browse your site, they'll interact with your products and are exposed to a number of product attributes, as well. The recommendation engine would fetch these data to marry user affinities and preferences with intelligent product recommendations.
The power of product recommendations goes beyond the aforementioned strategies and helps retailers show shoppers similar items—based on a group of items customers are currently viewing.
This complicated algorithm factors in the item's popularity and is designed to ascertain metrics (categories and keywords inputted to the data feed) and assign a similarity score for every item. Those with the highest similarity scores are then displayed in sync with the customer intent.
Bought Together Items
In this category, the product recommendations are items that customers buy frequently together with those items currently being viewed. The algorithm scores products based on their purchase frequency in the same transaction while demoting items that are bought with many other items.
So, it recommends products that are deeply linked or related to each other rather than connecting out-of-context items to popular products. Generally, these product suggestions are based on historical data (about the past six months) and are scored every twelve hours. This strategy is optimal to up-sell and cross-sell product bundles.
Viewed Together Items
This particular recommendation strategy is dependent on what product a customer is viewing at the moment. The product score is then assigned based on the frequency of views in a single session.
Whenever the particular item is viewed with many other products, the recommendation system considers the connection to be weak—this lowers the chance of the product being served as a recommendation. Displaying to your shoppers those viewed together items is suitable for all your product categories.
We looked at the various product recommendation strategies. But they are of use only when they reach your customers. Here are the seven places where you can display accurate recommendations to amplify user experiences and generate more sales.
Product Page: You could recommend products that have the same tags or categories, similar yet pricier products, and other popular related products.
Checkout Page: You could cross-sell products based on the shopper's purchase behavior and their browsing history.
Home Page: Feature personalized recommendations for returning visitors and best sellers for new visitors.
Product Category Pages: Implement recommendations when customers click on a product link or hover over products lest you disturb their shopping experience.
Order Confirmation/Receipts: Nudge your customers to come back for another purchase by showcasing more products they might like on the receipts.
Ingage is an AI-driven CRM that will help you collect and analyze customer data to deliver personalized recommendations. It excels the traditional product recommendation widgets and plugins by allowing you to offer completely unique and personalized offer to each of your customers.
Schedule your demo today and witness exponential cross-sells and up-sells.