Ticketmaster has built a real-time online learning pipeline for optimizing keyword bids on Google AdWords both for effective spend and scalability. (AdWords-based marketing is the key revenue driver for Ticketmaster’s SEM team.) The previous process of manual bid adjustment for high-performing keywords was neither effective nor efficient, and Google’s automated bid management software is limited in terms of adopting aggressive bid strategies and enabling customization for meeting different business requirements, such as optimizing marketing for primary or resale seats or other metrics across life span of a live event.
Mahesh Goud offers an overview of Ticketmaster’s technology stack: The streaming pipeline assists in making real-time bid decisions across different keywords using contextual bandit algorithms; the streaming pipeline is built with Storm, with Kafka and HBase as persistence layers; the ELK stack is primarily used for real-time visualizations, insights, and monitoring; a Hadoop minicluster is used to run end-to-end automated integration tests for testing the distributed infrastructure; and continuous integration is done using Jenkins. Mahesh Goud then walks you through the current architecture in which messages are streamed through various modules in the following order:
Mahesh Goud concludes by sharing some lessons learned while building the system, a comparison of results from Ticketmaster’s learning platform and Google’s Double Click Manager (an automated bid manager), and a demo of an interesting real-time visualization.
Mahesh Goud is a data scientist on Ticketmaster’s Data Science team, where he focuses on the automation and optimization of its paid customer acquisition systems and works on quantifying and modeling various data sources used by the Paid Acquisition Optimization Engine to increase the efficacy of marketing spend. He has helped develop and test the platform since its conception. Previously, he was a software engineer at Citigroup, where he was involved in the development of a real-time stock pricing engine. Mahesh holds a master’s degree in computer science specializing in data science from the University of Southern California and a bachelor’s degree with honors in computer science specializing in computer vision from the International Institute of Information Techonology, Hyderabad.
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