Over the past decade, the e-commerce sector has taken huge strides in providing more personalized customer experiences. Recommendation sections on retail e-commerce websites now have the same roles as traditional salespeople in brick-and-mortar retail stores in driving customer engagement.
With the advancement in artificial intelligence (AI), recommendation engines are constantly evolving to better understand the user and provide meaningful suggestions.
For retail-focused companies, this capability not only improves the buying experience for the customer but also increases sales conversions and revenue. In the omni-channel eCommerce journey, users create many events and generate massive amounts of data. If this is used wisely and to its full advantage, retailers can improve customer experience, engagement, and revenue.
Recommendation engines take advantage of all the data produced by the user to provide engaging and relevant content. By using catalog and behavioral data, recommendation engines suggest the right product to the right user at the right time and delight users by understanding their needs and preferences.
Netflix and Amazon, both industry leaders, are great examples of the power of recommendations. Amazon has attributed up to 35% of its revenue to its powerful recommendations in past earnings reports, while Netflix believes if it had not implemented a personalized recommendation engine, it would be losing $1 billion or more due to canceled subscriptions.
Differences in Recommendation Engines
Content-based filtering: The recommendation engine tries to find products that are similar in terms of content. When the user is looking at some product, this type of recommendation shows other products that are similar in terms of various attributes like size, color, brand, etc. This type of recommendation system heavily relies on catalog data that encompasses various product details.
Collaborative filtering: The recommendation system tries to find a similar user based on various user data and tries to recommend products based on the behavior or actions of other similar users. Such systems consider things like purchase history, profile data, reviews and ratings, browsing behavior, etc. to find similar users.
Hybrid: Many recommendation engines take a hybrid approach with a combination of content-based and collaborative filtering, using demographics like gender, age, profession, etc. for segmentation.
Types of eCommerce Recommendations
Different types of recommendations are seen on e-commerce sites on the pages throughout the customer’s journey to purchase. Some of the recommendation types are as follows:
View – View
This type of recommendation suggests the products that were viewed/browsed together.
Affinity to Recently Viewed
This type of recommendation shows a list of products based on browsing history. It considers the products in browsing history and information of various dimensions of products in the same category. Recommendations are given based on similarity among various attributes.
Bought – Bought
This type of recommendation gives users a list of products that are complementary and frequently purchased together.
This type of recommendation shows the top trending products on the retail eCommerce store. It is generally used during major sales and the holiday shopping season. The trending items are shown based on customer actions like browsing, add to cart, add to wish list, purchase, etc. It keeps changing very fast based on behavior data and inventory as holiday seasons see heavy buying.
Frequently Bought Together
This type of recommendation shows a list of products that are generally bought with this product in the same order. This recommendation aims at increasing the average order value (AOV) based on upselling and cross-sells.
This type of recommendation shows products that are related to the product getting browsed. This section either uses collaborative filtering, content-based filtering, or any hybrid approach in determining the relatedness of the products.
This type of recommendation shows best-selling products. Depending on where this section appears on the site, companies display best sellers for a specific category, best sellers for specific brands, and best sellers across various categories.
Many sites also show personalized recommendations based on users’ browsing data, purchase history, profile information, and demographics. Personalized recommendations are useful in giving the right content to the right user at the right time. Because personalization focuses on finding the most relevant products for the user, it may significantly improve user engagement and satisfaction.
This type of recommendation shows all the products that were recently viewed by the customer. It helps the user to continue browsing from the previous session. This way the recommendation engine can ensure the user does not miss any product in which they were previously interested.
Learn about the RETISIO Intelligent Recommendation Engine for retailers and brands.