“The greatest benefit of using Sift Science is not having to ever worry about fraud again. If you integrate properly and train the system to adapt to your unique circumstances, the word ‘fraud’ disappears from your business altogether.”
Adam ljaz, CEO
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 Science, 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%
As dbrand works to meet the desires of modern consumers, the company 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. On top of the e-commerce offering, dbrand’s customers are treated to thorough and thoughtful care, with every electronic device’s product accompanied by a high-production-value tutorial video. With an agile team, dbrand’s focus is on creating excellent customer experiences.
As the leader in the custom skin market, dbrand’s business is growing rapidly. Currently, 3.4x larger than their closest competitor and reaching an ever-wider audience, dbrand 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 Science, every aspect of our fraud workflow is automated. Fraud gets cut off right at the source.
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 Science, drawn by the product’s machine learning and automation features. Full integration took a week, and was extremely simple with Sift Science’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.
Simply put, Sift Science 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.
Adam’s team saw accurate and actionable results within 3 months of integrating with Sift Science. 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.