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TigerGraph for eCommerce On Demand Webinar

Findings from a 2017 survey* that focused on eCommerce emphasized the importance of personalized recommendations - shoppers clicked on a recommendation in just 7% of all visits, but these clicks drove an astounding 24% of the orders and 26% of the revenue. Moreover, these purchases with recommendation clicks saw a 10% higher average order value (AOV). Creating personalized product recommendations that drive higher revenue and keep customers coming back requires a deeper understanding of customer behavior.

In order to understand the customer behavior, eCommerce companies need to analyze the search, browsing and order history of individual customers and their network., the sixth largest eCommerce company in the world, uses TigerGraph to better understand their customers' behavior and deliver personalized product recommendations.

Learn how TigerGraph's real-time deep link analytics powered by a highly scalable graph database, combined with machine learning, is allowing eCommerce companies to:

  • Identify what a customer is looking for at a particular business moment
  • Recommend related items that will support customer needs and increase value of the final order
  • Recommend other items that align with the customer preferences, likes and dislikes to increase click-through rate and order value

Speaking: Gaurav Deshpande, VP Marketing, TigerGraph

*Personalization in Shopping,, Nov 2017