How E-Commerce Websites Use Recommendation Systems To Generate Repeat Purchases

Improvements to recommendation systems is a low-hanging fruit that would not only ensure that customers have a high repeat rate but also improve customer experience

- Tamanjit Bindra

Recommendation systems are one of the primary ways in which e-com-merce websites tend to generate repeat purchases, that is, getting a purchase from an already registered customer. Repeat purchase is one of the key metrics that e-commerce websites measure, since a repeat customer means less money spent on marketing to get him/her to make a purchase.

Recommendation systems are not a new technology. These have been around for as long as there has been online shopping. The most talked about recommended system is that of Amazon. The technology and its working are visible through sections like Recommended For You (shown in the image on next page), Frequently Bought Together or Based On Your Previous Purchases Following Is Recommended For You, among others.

Although the system has been around for a while, it seems to have not evolved with time. By evolution I mean changes in recommendation systems have been largely pushed back. With this article I wish to discuss what can be done on current recommendation systems within the framework of already existing algorithms.

How recommendation systems work

Recommendation systems have different algorithms. A simple recommendation system looks for users who have made the same purchases as you and rated these items similarly. The system then removes already-purchased items from the list of other items these similar customers purchased and rated highly. Rest of the items are then recommended to you. Two popular versions of these algorithms are collaborative filtering and cluster models.

Collaborative filtering. A collaborative filtering algorithm represents a customer as an n-dimensional vector (n being the number of item types that can be considered distinct). Vector components are positive for positively-rated items and negative for negatively-rated ones.