Our customers range from on-demand services like Instacart to online retailers like JackThreads to small stores using platforms like Shopify.
Each of our customers is unique not only in the way that fraud affects them, but also in the way fraud teams work through manual reviews of suspicious orders and users. Many of our customers prefer to review just their most recent orders while others prefer to focus on orders with high order values or have mismatches between shipping and billing addresses.
We’ve listened, and with the latest release of the Sift Science console, we’re really proud to give customers the ability to customize manual review queues in the way that makes the most sense for their business.
Custom queues that are personalized for your business
You can now filter queues by any attribute that you send Sift, including order value or country. Also, you can create queues using attributes our algorithms calculate, like the distance between billing address and shipping address or the number of failed transactions.
You still have built-in Orders and Users queues, but now you’ll have the ability to customize those queues further. Also, you can now build a queue completely from scratch through Search, and share that queue with other analysts by sharing a URL.
It’s now easier to train Sift Science to spot fraud
We’ve also made labeling users a one-click experience in Queues and the User Details panel to help analysts understand the labeling process better as well as be more efficient. You can still add a reason (like chargeback or spam) after you’ve labeled a user.
We’ll be rolling these changes out to you on August 4, and we won’t be supporting earlier versions of the console moving forward.
Help make Sift Science better!
We love feedback! If you have any thoughts you’d like to share, please let us know what you think by emailing firstname.lastname@example.org.
The Sift Scientists