Sift
Case Studies
How dbrand automated chargeback prevention
Overview
How dbrand automated chargeback preventionSift |
Analytics & Modeling - Machine Learning | |
Retail | |
Sales & Marketing | |
Fraud Detection | |
Data Science Services | |
Operational Impact
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. | |
Quantitative Benefit
Saved $250,000+ and recovered ~ 2% in gross revenue | |
Chargeback rate dropped from 2.18% to 0.12% | |