|Rahul Bhagat||Amazon.com Inc.|
|Srevatsan Muralidharan||Amazon.com Inc.|
|Alex Lobzhanidze||Amazon.com Inc.|
|Shankar Vishwanath||Amazon.com Inc.|
Repeat purchasing, i.e., a customer purchasing the same product multiple times, is a common phenomenon in retail.In this paper, the authors present the approach the authors developed for modeling repeat purchase recommendations.
Repeat purchasing, i.e., a customer purchasing the same product multiple times, is a common phenomenon in retail. As more customers start purchasing consumable products (e.g., toothpastes, diapers, etc.) online, this phenomenon has also become prevalent in e-commerce. However, in January 2014, when we looked at popular e-commerce websites, we did not find any customer-facing features that recommended products to customers from their purchase history to promote repeat purchasing. Also, we found limited research about repeat purchase recommendations and none that deals with the large scale purchase data that e-commerce websites collect. In this paper, we present the approach we developed for modeling repeat purchase recommendations. This work has demonstrated over 7% increase in the product click through rate on the personalized recommendations page of the Amazon.com website and has resulted in the launch of several customer-facing features on the Amazon.com website, the Amazon mobile app, and other Amazon websites.