Case Study: dbrand reduces chargebacks 95% and saves $250K with Sift

How dbrand automated chargeback prevention

dbrand, a Toronto-based maker of precision-fitted, customizable vinyl wraps and a leader in the virtual skin-building market, was seeing rapid growth—and with it a rising wave of fraud from stolen credit cards. Chargebacks peaked at 2.18%, costing the company sales, fees and time, and forcing four customer-service reps to act as de facto fraud managers.

dbrand integrated Sift’s machine-learning fraud prevention in about a week and trained the system in a month, automating review workflows and catching business-specific fraud patterns. The result: chargebacks fell to 0.12% (about a 95% reduction), the company recovered roughly 2% in gross revenue and saved over $250,000, the team cut 200 hours/month of investigations and now spends about one hour a month on fraud oversight.

dbrand

Adam Ijaz

CEO