How Zoosk keeps its community safe while improving user experience
公司规模
1,000+
地区
- America
- Asia
- Europe
国家
- United States
- Worldwide
产品
- Zoosk Online Dating App
- Sift Score API
技术栈
- Machine Learning
- Big Data
- Real-Time Analytics
实施规模
- Enterprise-wide Deployment
影响指标
- Customer Satisfaction
- Productivity Improvements
技术
- 分析与建模 - 机器学习
- 分析与建模 - 实时分析
适用行业
- 电信
适用功能
- 商业运营
- 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
Zoosk is a leading online dating company that uses Behavioral Matchmaking technology to pair singles with whom they're likely to discover mutual attraction. The technology is constantly learning from the actions of its 35 million members. With the #1 grossing online dating app in the Apple App Store, Zoosk is a market leader in mobile dating. The platform is available in over 80 countries and translated into 25 languages, making it a truly global online dating platform. There is no fee to join Zoosk, but for members who want to use Zoosk’s full communications platform, Zoosk offers users paid subscriptions.
挑战
Zoosk, a leading online dating company, was facing challenges with fraudulent users who were spoiling the experience for legitimate ones. The company was already working to reduce friendly fraud and payments fraud on the site. However, the real-time nature of Zoosk’s service and their ever-expanding user base meant that the company needed a solution that could adapt instantly and scale as their business grew. The existing tools and processes used by the dedicated team tasked with tackling fraud for Zoosk were not sufficient to meet these needs.
解决方案
Zoosk's Payments & Risk Manager, Tal Yeshanov, knew that machine learning was the key to addressing Zoosk’s need for an adaptive and accurate fraud solution. She turned to Sift’s big data and real-time solution, which empowered her fraud analysts with ever-updating intelligence. By integrating Sift’s findings via Sift Score API into Zoosk’s existing management system, the team now has more data and is more efficient. This solution not only addressed existing bad users but also tried to prevent their return, adapting to the fluidity of fraudster behavior.
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