Keeping fraudulent ticket buyers off the platform
Company Size
1,000+
Region
- America
- Asia
- Europe
Country
- United States
Product
- Etix ticketing platform
- Sift's fraud prevention solution
Tech Stack
- Machine Learning
- Predictive Analytics
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Cost Savings
- Productivity Improvements
Technology Category
- Analytics & Modeling - Machine Learning
- Analytics & Modeling - Predictive Analytics
Applicable Functions
- Sales & Marketing
Use Cases
- Fraud Detection
Services
- Data Science Services
About The Customer
Etix is the largest independent ticketing company in North America, with headquarters in the U.S. and offices in Europe and Asia. The company sees millions of unique users visit their website and mobile app every month, selling 50 million tickets per year via their ticketing platform. Etix aims to ensure a flexible, secure, and premium pre-event experience for their partners and customers. Their suite of products extends beyond online ticket sales to include marketing solutions, ads, and analytics, providing venues and promoters with a full arsenal of tools to make every event premium. Founded in 2000, Etix has grown significantly and continues to innovate in the ticketing industry.
The Challenge
Etix, the largest independent ticketing company in North America, was facing a growing problem of fraudulent transactions as their online and mobile business scaled. These fraudulent transactions resulted in chargebacks, costing the company money and the invaluable time of fraud analysts who had to respond to fraud attempts. The challenge of discovering fraud through manual review was daunting and unsustainable. Chargebacks often were not reported until after events, making it even more difficult to track and prevent fraud. Etix needed a solution that could respond in real time to potential fraud and prevent fraudulent orders before they were processed.
The Solution
Etix decided to implement Sift’s fraud prevention solution after exploring its intuitive interface and easy-to-understand pricing plans. The solution was fully implemented and running in three weeks by a single engineer. Sift's machine learning solution allowed Etix to keep up with their order volume, while the global model’s predictive analytics provided insights to prevent fraudulent orders before they were processed. Leveraging the data of all of Sift’s users empowered the Etix team to block bad users and orders, significantly reducing the volume of orders in their review queues. The Etix team can now automate on Sift Scores, making for a more efficient review process.
Operational Impact
Quantitative Benefit
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