How Atom Tickets maintains secure, seamless ticketing
公司规模
1,000+
地区
- America
国家
- Canada
- United States
产品
- Sift Payment Protection
- Sift Insights
技术栈
- Sift Score
- QR code
实施规模
- Enterprise-wide Deployment
影响指标
- Cost Savings
- Productivity Improvements
技术
- 应用基础设施与中间件 - API 集成与管理
适用功能
- 销售与市场营销
用例
- 欺诈识别
服务
- 数据科学服务
关于客户
Atom Tickets reimagines the most convenient way for users to plan a night out at the movies and preorder concessions. The free app and website provide relevant reviews, trailers, and synopses to help moviegoers make the best decision on what to see. Atom users can quickly select the movie, theater, showtime, and seats, and after purchasing tickets they can invite friends and Atom will share the selected showtime and seats with them. At the theater, users skip the box office lines and go right to the ticket attendants, who scan a QR code on the user’s phone or Apple Watch to redeem their order. With millions of users on the platform, Atom Tickets operates in the US and Canada and is enabled on 22,000 screens.
挑战
Atom Tickets was struggling with chargebacks; their chargeback rate was very high, which decreased their revenue and order volume. There isn’t a team focused on preventing fraud – while there are stakeholders across departments, fraud is a one-man army led by Trust & Safety Specialist Aaron Rennell. Managing all of Atom Tickets’ fraud was already a big job but it got even trickier for Aaron when Atom Tickets experienced spikes in activity during blockbuster movie ticket sales. The significant increase in online movie sales for the company also brought in an uptick in fraudulent purchases for blockbuster must-see movies. Given the increase in potentially fraudulent purposes, they needed a solution that would automate and streamline fraud prevention to make it manageable, and help them greatly reduce their chargeback rate.
解决方案
On a recommendation from an industry peer, the head of product at Atom Tickets turned to Sift for a solution. Once they implemented Sift Payment Protection, it took only a few weeks for the model to start yielding powerful results. Within a month, their chargeback rate decreased significantly. Workflows were also set up to automatically block users with a certain Sift Score, which worked well in tandem with rules that Atom Tickets had also set up within Sift. Aaron found Sift Insights (reporting capabilities) integral to his investigations; utilizing the Explore feature, he could view all ticket sales by Sift Score (risk score) and make bulk decisions – a task that previously would have required dropping data into a spreadsheet and reviewing accounts one by one, taking days to complete. This saved Aaron a massive amount of time when quickly dealing with large spikes in ticket sales. And with Roles and Permissions, he was able to see which users across departments were in review queues.
运营影响
数量效益
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