- Ecommerce company offering custom electronics wraps
- Toronto-based, with customers worldwide
- Chargeback rate of 2.18% from stolen credit cards
- 4 customer service reps had to become fraud management experts
- Automation with Sift, resulting in zero full-time fraud managers
- Easy integration and API documentation
- Saved $250,000+ and recovered ~ 2% in gross revenue
- Chargeback rate dropped from 2.18% to 0.12%
Customizing products for today's consumers
Dbrand offers shoppers the opportunity to personalize their countless gadgets with unique, customizable, and precision-fitted vinyl wraps. With all of their products developed and manufactured in Toronto, dbrand’s distinctive and industry-changing virtual skin building interface puts the creative power in the hands of consumers. They turned to Sift to prevent chargebacks and automate fraud management.
More growth led to more chargebacks
As the leader in the custom skin market, dbrand’s business is growing rapidly, and the company saw fraudsters creeping onto the site as sales increased. The overwhelming majority of the fraud that dbrand experienced was bad users purchasing goods using stolen credit cards. The resulting chargebacks were costly, not only due to the high-quality product that was lost, the sale that was refunded, or the bank-levied chargeback fees, but also the hours of manual review and headaches that the fraud caused. Even as their chargeback rate reached a high of 2.18% in a single month and 4 customer service employees became dedicated fraud management experts, fraudsters continued to slip past their defenses. To mitigate the impact of fraud on their bottom line and brand, dbrand sought a smarter and more scalable solution.
“With Sift, every aspect of our fraud workflow is automated. Fraud gets cut off right at the source.”
Custom machine learning catching unique fraud
After researching fraud management solutions, dbrand CEO Adam Ijaz was disappointed to find that many required ongoing manual review and hand-holding. In search of a vendor that could reduce their workload by growing efficiency, Adam discovered Sift, drawn by the product’s machine learning and automation features. Full integration took a week, and was extremely simple with Sift’s easy API and extensive documentation. With just one month of training, dbrand’s custom machine learning algorithms were catching fraud unique to the business, identifying returning and new fraudsters alike.
“Sift saves you the hassle of chargebacks, combats stolen credit card purchases with ease, and - once the machine-learning system has been trained - does it all automatically.”
A system so accurate it's automated
Adam’s team saw accurate and actionable results within 3 months of integrating with Sift. By using Sift Scores and the features that support automating fraud review within dbrand’s existing order management system, the team saved 200 hours a month in fraud investigation. Now, dbrand dedicates just 1 hour every month to fraud management, reviewing the system parameters and ensuring that results remain accurate. The fraud management team has since returned to their customer service roles, and zero people deal with fraud full-time; their system is so accurate that it’s in large part fully automated. By catching fraudsters early and identifying suspicious users before any product is lost, dbrand recovered about 2% in gross revenue and has saved well over a quarter million dollars in chargebacks and their associated costs.
“The API was fantastic and very well documented. The web interface is always seeing improvements and has a clean, responsive design.”