Sift > Case Studies > Stopping fake listings from harming customer experiences

Stopping fake listings from harming customer experiences

Customer Company Size

Mid-size Company

Region

Asia
Pacific

Country

Worldwide

Product

Sift's machine learning system

Tech Stack

Machine Learning
REST API

Implementation Scale

Enterprise-wide Deployment

Impact Metrics

Customer Satisfaction
Brand Awareness

Technology Category

Analytics & Modeling - Machine Learning

Applicable Functions

Business Operation

Use Cases

Fraud Detection

Services

Data Science Services

About The Customer

Travelmob, acquired by HomeAway, is a platform created to help global travelers find unique places to stay across Asia Pacific, from Bangkok to Melbourne. Hosts list their rooms and properties, and guests use the Travelmob site or mobile app to book them. The company's customer experience is key to its business model, as it directly impacts the brand experience, repeat business, new users, and continued growth. However, the company was facing challenges with fake listings and credit card fraud, which were negatively impacting the customer experience and resulting in costly chargebacks.

The Challenge

Travelmob, a social marketplace for travellers, was facing a growing trend of fake listings on its site. Bad users were posing as legitimate hosts, posting photos of properties they didn’t own, and trying to con unsuspecting guests into making their payment offsite. This was negatively impacting the customer experience and the company's brand image. Additionally, the company was also dealing with credit card fraud that was resulting in costly chargebacks. Initially, Travelmob began by manually reviewing new listings and booking requests, but this approach was not scalable and fraud was slipping through the cracks. Building dedicated internal tools for fighting fraud would require time and resources that they couldn’t spare, and anything they created internally couldn’t adequately address the complexity of fraud.

The Solution

Travelmob decided to implement Sift's machine-learning fraud solution. After perusing Sift’s easy-to-use REST API, it only took a few hours for the Travelmob team to get the system up and running, and they were fully integrated within a week. Travelmob used Sift Scores to identify high-risk bookings that required manual review. They also used advanced tools and rich insights in the Sift Console, like Network Visualizations, to connect the dots between different users and locations and make smart decisions about who to block. Initially, Travelmob used Sift to catch fake listings, but the experience was so successful that they also applied the machine learning solution to their credit card fraud problem.

Operational Impact

Travelmob started seeing results immediately after integrating with Sift.
Sift’s fraud detection grows increasingly accurate as Travelmob continues to send more data and feedback.
Through the Sift Events API, it was easy for the Travelmob team to record and send new data to Sift.
Sift’s machine learning was incredibly flexible, allowing Travelmob to customize their fraud detection by sending data unique to their business.
The flexibility of Sift has made it a one-stop shop for Travelmob to catch and prevent all types of fraud that threatened their business.