How ChowNow started focusing on growth instead of fraud
Company Size
1,000+
Region
- America
Country
- Canada
- United States
Product
- ChowNow online ordering platform
- Sift
- Sift Workflows
- Sift Insights
Tech Stack
- Online ordering system
- Fraud detection system
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Cost Savings
- Revenue Growth
Technology Category
- Application Infrastructure & Middleware - Data Exchange & Integration
Applicable Industries
- Food & Beverage
Applicable Functions
- Business Operation
- Sales & Marketing
Use Cases
- Fraud Detection
- Supply Chain Visibility
Services
- System Integration
About The Customer
ChowNow is a platform designed to help restaurants meet growing customer demands for faster, easier ways to order their meals. They create online ordering channels on both web and mobile, so their growing base of 9,000 restaurant customers can compete and win in an increasingly digital world. With ChowNow’s platform, restaurants streamline pickup & delivery and accept more orders than ever before. The company's goal is to make online ordering easy, so restaurants can focus on what they do best: cooking delicious food and connecting with their customers.
The Challenge
ChowNow, an online ordering platform for restaurants, was facing a growing problem of fraud. As the business expanded, so did the losses from chargebacks, which had grown to 1% of revenue. Fraudsters were actively sharing tips on how to defraud ChowNow, and certain locations like Miami, Atlanta, New York, and Chicago emerged as significant sources of fraud. This situation was discouraging growth in large markets. The company was spending an increasing amount of time fighting chargebacks, and was considering hiring a full-time team to tackle the problem. As a growing business, ChowNow didn’t want to restrict sales or spend too much on fraud operations, but couldn’t continue operating with growing revenue loss.
The Solution
ChowNow turned to Sift, a fraud detection solution, and tasked two engineers with the integration. It took one month to get set up, and they saw results as soon as it was implemented. Sift was able to pick up on repeat offenders and catch fraudsters who previously had gotten through. Shortly after integration, they set up Workflows, Sift’s automation feature, which enabled them to take action on orders in real-time, a critical part of mitigating risk for their on-demand business. They also used Workflows to build logic incorporating the riskiness of various regions, so they could easily adjust acceptance rates without affecting order volume from good locations. ChowNow also uses Sift Insights, a reporting capability, to monitor order growth as well as block rates and chargeback rates.
Operational Impact
Quantitative Benefit
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