The unreasonable effectiveness of Recommendation systems in ecommerce

Recommendation systems have emerged as a cornerstone of modern e-commerce platforms. Navigating the vast ocean of product catalogs can be daunting, and these systems offer a personalized compass to users. Users prefer to be guided by these systems, 35% of Amazon purchases and 75% of Netflix viewings are based on such algorithms, as reported by a McKinsey study.

Several types of recommendation systems can enhance user experience:

Content-based systems offer suggestions based on a user’s past preferences. Suppose you’ve been browsing vintage leather jackets; this system would note your interest and could recommend a retro-style pair of boots, a product consistent with your apparent preference for vintage attire.

Collaborative systems take a broader perspective by taking into account collective user behaviors. For example, if users who bought slim-fit jeans also frequently bought plain white tees, the system would recommend a plain white tee to you the next time you purchase slim-fit jeans.

Community-based systems, on the other hand, draw on your social network’s preferences to provide recommendations, akin to a friend suggesting a book they recently enjoyed. For example, Yelp uses community ratings and reviews to provide personalized restaurant recommendations to its users.

Industry typically follows a hybrid structure which takes into account all of the above in a single recommendation or multiple categories.

Creating a recommendation engine for a D2C brand is an technically intricate process that unfolds in several stages:

  • Data Collection: The first step involves collecting user interactions. This includes capturing user clicks, recording items viewed, added to the cart, or purchased. This is typically done through website analytics or event tracking tools.
  • Data Processing: The collected data must be processed to discern user behavior patterns. This includes segmenting users based on their activity, identifying frequently viewed or purchased items, and spotting trends over time.
  • Creating a Similarity Matrix: This step involves creating a matrix of items, with the similarity between each pair of items calculated based on user interactions. This matrix is critical for making recommendations.
  • User-Item Interaction Analysis: Here, user behavior is mapped to item similarity. For example, if a user shows interest in a particular item, similar items are recommended based on the similarity matrix.
  • Recommendation Generation: Finally, personalized recommendations are created for each user, leveraging the insights gained from the earlier steps.

While this process is technical, solutions like Alme democratize it, offering a plug-and-play interface to deploy customized recommendation systems. Despite their “unreasonable effectiveness,” recommendation systems are crucial for D2C brands seeking personalized customer engagement