公司规模
Large Corporate
地区
- America
- Europe
国家
- United States
- Germany
- United Kingdom
产品
- Sift Console
技术栈
- Sift
实施规模
- Enterprise-wide Deployment
影响指标
- Cost Savings
- Customer Satisfaction
适用行业
- 零售
适用功能
- 销售与市场营销
用例
- 欺诈识别
服务
- 网络安全服务
关于客户
Harry’s is a care brand making thoughtful products for all men, with operations in North America and the UK. They design all of their products in their New York office and manufacture them in their factory in Germany, then sell direct to customers via Harrys.com and Target and Walmart stores. “No middlemen, no upcharges. That’s the Harry’s way.” Harry’s offers both Shave Plan subscription and a la carte services, allowing customers the freedom to order blades depending on how often they shave. No waste, no excess costs. Just high quality blades when their customers need them.
挑战
A few months after launching their business, Harry’s started considering the need for a proactive, scalable solution to put into place before fraud became a larger problem. Being a trustworthy site is critical to Harry’s business model and core beliefs, so getting the jump on fraud before it became a deep-rooted issue was essential. Harry’s did see some fraud, mainly in the forms of promo abuse, payment abuse, account abuse, and friendly fraud. Resellers would make fake accounts to buy large quantities of blades and sell them them at a profit online, while other fraudsters would use Harry’s for stolen credit card testing. Some returning customers would try to game the system by canceling their subscription after having received product. Harry’s needed a solution that wouldn’t just stop all of these types of fraud, but would teach their fraud team about the tactics of bad users.
解决方案
The COO and Head of Engineering researched a number of different options, but the ease of integration and accuracy of Sift spoke to Harry’s needs. Once Harry’s reached out to Sift, integration was fast and the solution was quickly up and running, learning and adapting in the background. Soon, Harry’s was using the intuitive Sift Console to pull Sift Scores, Network Visualizations, and social media data to investigate suspicious users. They were able to build an internal workflow around Sift Scores, the ranges of which helped Harry’s determine when to block or review.
运营影响
数量效益
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