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

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	How Zoosk keeps its community safe while improving user experience Sift

How Zoosk keeps its community safe while improving user experience

Sift

Technology Category	Analytics & Modeling - Machine Learning Analytics & Modeling - Real Time Analytics
Applicable Industries	Telecommunications
Applicable Functions	Business Operation Sales & Marketing
Use Cases	Fraud Detection
Services	Data Science Services
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. Read More
About 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. Read More
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. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Machine Learning

Analytics & Modeling - Real Time Analytics

Applicable Industries

Telecommunications

Applicable Functions

Business Operation

Sales & Marketing

Use Cases

Fraud Detection

Services

Data Science Services

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.

About 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.

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.

Impact #1	Zoosk has streamlined their fraud management workflow since implementing Sift.
Impact #2	The fraud team’s review process has become increasingly efficient.
Impact #3	Zoosk is able to offer members an even better experience.

Impact #1

Zoosk has streamlined their fraud management workflow since implementing Sift.

Impact #2

The fraud team’s review process has become increasingly efficient.

Impact #3

Zoosk is able to offer members an even better experience.

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Overview

How Zoosk keeps its community safe while improving user experience

Operational Impact