Ticketek operates Australia’s most advanced multi-channel ticket sales distribution network and sells over 19 million tickets to more than 20,000 events each year. Tickets are sold through multiple channels including mobile phone applications, websites, via retailers and direct sales to corporate clients.
Ticketek has long embraced the opportunities presented by Ticket Sales and Customer data, using Analytics throughout their operations to maximize revenue for their event partners and customers. However, with a growing number of events and a high volume of ticket sales through multiple channels, Ticketek realized that it was time to modernize the existing environment to manage their data more efficiently and reduce reporting & query times to deliver faster time to insight.
One of the main challenges was reducing reporting & query times, so executives and event partners could have real time insights on ticket sales (number of tickets sold for each event, demographics of buyers, most popular regions etc). Another challenge was effectively managing the sheer volume of data along with steep spikes in activity based on the number and types of events running.
Ticketek engaged Bryte to re-build a modern, highly scalable and Big Data grade analytics environment that could deliver real time insights & enable Dynamic Pricing. Our team delivered an end-to-end cloud based solution within 6 months using an AWS native approach which featured a combination of Amazon EC2, Kinesis and Redshift technologies.
Automated processes were established using Change Data Capture technology (CDC) and Amazon Kinesis to process, integrate and load data in real time from all key Ticket Sales and CRM systems into Amazon Redshift (Included CDC from SQL Server & MongoDB databases to Redshift). Once Amazon Redshift was established as the single source of truth for all existing/new Ticket sales & customer data, automated ETL workflows and complex data models were built in Redshift using ELT Processing Technology to organize data in real time into appropriate analytical data models. This involved overlaying data on Ticket sales with Event Specific Information (Type of Event, Event capacity etc) and other information. The output was then fed into Executive Dashboards & pricing algorithms to optimize revenues.