A contextual real-time bidding engine for search engine marketing

Mahesh Goud shares success stories using Ticketmaster's large-scale contextual bandit platform for SEM, which determines the optimal keyword bids under evolving keyword contexts to meet different business requirements, and explores Ticketmaster's streaming pipeline, consisting of Storm, Kafka, HBase, the ELK Stack, and Spring Boot.

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.