Case Study: Teespring achieves real-time eCommerce analytics and improved buyer–seller engagement with MemSQL

How Teespring Enhances its eCommerce Platform with Real-Time Analytics

Teespring, an ecommerce platform that helps entrepreneurs create and sell products (more than 15 million shipped since 2012), needed to surface real-time buyer activity to connect vendors with the right buyers. As its community grew, Teespring required a low-latency analytics dashboard to track clickstream metrics—click-through rates, page views, and completed purchases—across roughly 300 million events per day.

Teespring built a real-time pipeline using MemSQL as the in-memory datastore and serving engine, with Apache Kafka, Spark and the MemSQL Spark Connector, and a parallel AWS persistence pipeline. The solution delivered fast, ANSI‑SQL-powered querying and scalable cloud deployment, enabling sellers to target buyers more effectively, reduce irrelevant browsing, boost customer engagement at scale, and improve operational health with faster issue response.

Teespring

Muru Muthusamy

Analytics Solutions Architect