How iwantmyname cut fraud losses and freed up time and resources

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Overview

Simple, transparent domain registration

iwantmyname is a global domain name registrar that helps people search, register, and manage a great domain. They offer a free DNS service, robust customer support, and an easy one-click import that automatically loads required DNS records for all of a customer’s favorite hosted web services. In addition to offering an excellent service, iwantmyname sees themselves as the ethical registrar of choice. They work actively in the community, support startups, and give back a portion of every registration fee for charitable donations towards environmental sustainability projects and disaster relief. Based in New Zealand, iwantmyname boasts around 100,000 global users.

Challenge

Fraud was costly and time-consuming

Excellent customer experience is a key differentiator for iwantmyname, which seeks to make the process of purchasing a domain name simple, clear, and transparent. However, fraud was putting a strain on the business’ mission. Not only were they losing money to fraud and chargebacks, but their highly time-consuming fraud prevention practices were negatively affecting the user experience and causing them to miss out on some legitimate sales.

Before they implemented Sift, detecting fraud was a 100% manual effort. In the early days of the company, two of the co-founders checked every single transaction for suspicious signals, then—once they began identifying sources of the highest levels of fraud—they had little choice but to block all users from those countries from making purchases.

Those country-based blacklists hurt iwantmyname’s business in two major ways. First, the company missed out on revenue from legitimate customers who resided in those countries. Second, existing customers who were traveling in those blacklisted countries faced extra hassle with their accounts, which reflected negatively on iwantmyname’s brand. Even worse, the team as a whole spent as much as 30% of their time on managing the burden of fraud—time that the company felt would be much better spent on delighting good customers and positively growing the business.


Solution

Immediate results from Sift

iwantmyname calculated that they were losing 2% of their revenue to fraud—an amount they deemed unsustainable for a business of their size and given the available net margins in a very competitive industry. They began looking for an automated solution that would cut their fraud losses, reduce false positives, and free up employees’ time to focus on more productive tasks. Upon the recommendation to look into machine learning technology, Sift’s promise of easy integration and excellent reputation convinced iwantmyname to sign up.

From there, iwantmyname saw impressive results incredibly quickly. Two developers completed the integration in a single day, and Sift’s machine learning technology began catching fraud immediately, dramatically reducing both fraud attacks and nuisance transactions from credit card testers.

After a couple days of training, the team successfully incorporated the Sift Console into their daily operations. Within days, iwantmyname saw value in the accurate and actionable Sift Scores. Within weeks, they experienced a 75% reduction in fraud on their site by using the Sift Console to make their decisions—and every other order is approved without any friction whatsoever.

Sift was a way to get all of our implicit knowledge about fraud behaviors automated. We saw the value from the outset, and were excited to see the benefits.

Paul Spence, COO and co-founder


Results

Less fraud, more time for valuable projects

Using Sift has enabled iwantmyname to save money, accept orders they couldn’t have previously, and devote employees’ time to more valuable endeavors—including growing their already active community-giving efforts. Not only are the days of losing 2% of revenue to fraud long gone, but iwantmyname has been able to lift their country blacklists and begin accepting orders based on Sift’s sophisticated machine learning-based risk scores instead. This means much higher accuracy, a better customer experience, and more revenue.

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  • Achieve up to 285% ROI
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  • Drop time spent on manual review up to 80%
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