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How StackCommerce reduced chargebacks and saved valuable time and money

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Creating new revenue streams for publishers

StackCommerce is the leading native commerce platform for online publishers, communities, and brands. They power deal stores for the world’s top tech and lifestyle publishers by offering curated product recommendations tailored to each client’s audience. A fast-growing business in a thriving market, StackCommerce has more than 1,500 vendors offering products and services to over 200 million monthly users across more than 750 publishers’ websites. As part of their service, StackCommerce handles fraud management for any orders placed on their platform.


Rules don't scale, and fraud slips through

The main type of fraud StackCommerce deals with involves purchases made using stolen credit cards‚ÄĒand the most time-consuming and impactful type of fraud comes from the loss of digital goods that are distributed instantly. When this fraud occurs, it not only hurts cardholders, but also the merchants. StackCommerce needed to stop these transactions as quickly as possible, and they sought a solution that could prevent them in the first place.

Before Sift, StackCommerce was using a legacy, rules-based solution that didn’t include any machine learning. As the company’s order volume grew, they discovered the shortcomings of rules-based systems: they don’t learn and they don’t scale. The team found themselves reviewing hundreds‚ÄĒor even thousands‚ÄĒof orders per day, and fraud review became unmanageable. As a response to the increasing volume, StackCommerce began mass approving orders, which in turn increased disputes. There were times when their support queue was so backed up, they’d have to spend a day or more getting caught up.


Machine learning offers accuracy and efficiency

StackCommerce began looking for a tool they could confidently rely on to prevent fraud, and which also had automation capabilities. After extensive online research‚ÄĒand a recommendation by their payment gateway, Stripe‚ÄĒthey landed on Sift.

Using Sift’s extensive online documentation, they were able to get up and running in less than two weeks. The team saw accurate results immediately, but the results were even more striking after they trained their machine learning model by labeling users.

The StackCommerce team uses Lists to efficiently manage their fraud review process, making instant decisions or flagging orders for additional verification. They also use Sift’s automation tools‚ÄĒFormulas and Actions‚ÄĒto automate fraud decisions, saving even more of the team’s precious time.

The interface and APIs are extremely intuitive. I've now implemented several 3rd party services and Sift's API and integration/web interface is by far one of the best I have seen. The global model worked pretty well for us out of the box, but after 30 days, we saw bad users that were consistently getting through our old system being blocked (scored high) by Sift.

Brandon Robbins, Product Manager


Saving valuable money and time

With Sift, StackCommerce has reduced their chargeback loss rate by 25%, saving more than $2,000 per month on chargeback fees. Not only that, but despite a 30% increase in monthly order volume since implementing Sift, the StackCommerce team hasn’t had to hire additional staff to manage fraud. In fact, they are now down to a single employee spending no more than two hours per day on manual review.

The insights provided by the Sift Console have also helped StackCommerce learn more about what types of deals attract fraudsters. The Formulas and Actions integration has led to even more accurate and powerful fraud prevention, resulting in a 25% decrease in fraudulent chargebacks so far. Compared with the siloed nature of the rules they previously relied on, StackCommerce now leverages Sift’s machine learning technology to accurately visualize the links between related users, using thousands of potential fraud signals to predict and prevent fraud.

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