Stopping fake listings from harming customer experiences
Company Size
200-1,000
Region
- Asia
- Pacific
Country
- Worldwide
Product
- Sift's machine learning system
Tech Stack
- Machine Learning
- REST API
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Brand Awareness
- Customer Satisfaction
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
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