Sift > Case Studies > How Zoosk keeps its community safe while improving user experience

How Zoosk keeps its community safe while improving user experience

Customer Company Size

Large Corporate

Region

America
Asia
Europe

Country

United States
Worldwide

Product

Zoosk Online Dating App
Sift Score API

Tech Stack

Machine Learning
Big Data
Real-Time Analytics

Implementation Scale

Enterprise-wide Deployment

Impact Metrics

Customer Satisfaction
Productivity Improvements

Technology Category

Analytics & Modeling - Real Time Analytics
Analytics & Modeling - Machine Learning

Applicable Industries

Telecommunications

Applicable Functions

Sales & Marketing
Business Operation

Use Cases

Fraud Detection

Services

Data Science Services

About The Customer

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.

The Challenge

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.

The Solution

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.

Operational Impact

Zoosk has streamlined their fraud management workflow since implementing Sift.
The fraud team’s review process has become increasingly efficient.
Zoosk is able to offer members an even better experience.
The team can use the Sift Score and Sift’s easy and intuitive tools to block users and investigate suspicious behavior.