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One of the potential downsides to meteoric success is that you become a target for all kinds of opportunistic and abusive behavior. Just ask JackThreads, the men’s style destination that was named an Internet Retailer Top 500 company in 2012 when its web sales grew by 359% in a single year.
Launched in 2008 as a flash sale site offering overstock at affordable prices, JackThreads catered to an underserved group: fashion-conscious young men. Over the years, its business model evolved to focus more on high-end, limited-run collections and private label items while creating an amazing customer experience. Unfortunately, increasing accounts of online fraud threatened those goals.
High Traffic Can Mean High Risk
JackThreads had nearly 2 million members and was dealing with thousands of transactions per day when the pain of chargebacks became too much to bear. Like many growing online retailers, its first response was to set up an internal team to review transactions and assess the risk of each one.
To help automate their process, JackThreads’ small but nimble team created a system of rules to identify bad users, using criteria such as purchase amount and geography. But what happens when a trickle of fraud becomes a deluge? It soon became clear that bad orders were slipping through the cracks and good customers were being incorrectly flagged.
Fraud Doesn’t Play By the Rules
So what went wrong? If you’ve ever dipped a toe into tackling or preventing online fraud, you may have considered a “rules-based system” for sorting bad users from good. It seems straightforward at first: simply create an “if x, then y” logic to block fraudsters based on historical trends.
But online purchases happen at a lightning-fast speed, and rules-based systems are slow, require manual updating and are notoriously inflexible. In JackThreads’ case, the fraud team was painstakingly analyzing each of these users and comparing the data to publicly available information on websites and social media to determine if they were indeed fraud.
Machine Learning to the Rescue
In 2013, JackThreads started shopping around for a tool to help plug the holes in its existing solution: too many good customers getting caught in the net, too many bad ones slipping through and too much time spent pouring over a mess of IP addresses, user names and postal codes.
JackThreads senior fraud analyst Blake Morrow came across Sift Science, a large-scale machine learning fraud solution with convenient sign-up and quick integration. After implementing via simple APIs, JackThreads was up and running. Immediately, Blake and his team at JackThreads began to benefit from the Sift Science global network –– a library of 5,000+ (as of July 2015) fraud signals that update in real time.
800% Return on Investment
While the global network offers intelligence that no other machine learning fraud solution can provide, Sift Science was able to tailor their offering for JackThreads’ particular business needs. Once the fraud analysts started labeling data, the machine learning system gathered steam, growing their Sift Science accuracy within a matter of weeks.
Sift Science’s solution nabbed hundreds of users that wouldn’t have been caught with JackThreads’ existing fraud filters. And it was incredibly accurate. Nearly 92% of orders flagged as fraudulent were indeed fraudulent. The industry average? 25%.
Not only does that mean fewer orders to analyze (and more time to focus on what matters), but it also saves major cash and time. The labels API pulls information seamlessly and effortlessly, updating in real time. With Sift Science, JackThreads sidesteps thousands of dollars a month in chargeback fees –– a whopping 8x return on investment.
As JackThreads continues its ascent, one thing is clear: fraud –– once a looming threat to their long-term success –– is now just a footnote.
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