Get your fraud questions answered and connect with product experts in our brand new Sifters community

How HelloFresh reduced promo abuse by 95% with Digital Trust & Safety

See Sift in action
Overview

HelloFresh: The world’s leading meal kit company

HelloFresh is the world’s leading meal kit company, providing more than 600 million meals to 5.3 million households worldwide, in 14 countries and across 3 continents, in 2020. The company provides households with everything they need to prepare quality, delicious, home-cooked meals that require no planning, shopping, or hassle. Customers are supplied with every ingredient needed for thousands of HelloFresh recipes—all planned, sourced, and delivered to customers’ doorsteps. HelloFresh is dedicated to changing the way people eat, helping customers save money, access high-quality meals, reduce food waste, and take the stress out of meal time.

Challenge
  • Users exploiting promotional offers
  • Ineffective and inefficient manual review process
  • Need for a dynamic solution for various regional requirements
Solution
  • Quick and easy implementation
  • Market-specific strategies
  • 12 customized machine learning models tailored to specific business needs
Results
  • More accurately and effectively surface and stop promo fraud
  • Consistently saving time, money, and resources
  • Reallocating resources continuously drives business growth
Anirudh Lanka

Partnering with Sift has been great. Amongst other things, they have helped us reduce our fraud rate by 95%.

Anirudh Lanka, Senior Product Owner - Payments at HelloFresh

Challenge

Users exploit promotional offers, hurting HelloFresh’s bottom line

Promotional offers are an important part of HelloFresh’s business, helping to attract customers in new markets and incentivize them to try the meal kit service. The flip side to offering such generous promos is that they can be exploited.  

The HelloFresh Risk Management team initially started tackling these challenges internally through manual review processes in spreadsheets, but quickly found that they didn’t have the breadth of data they needed to effectively detect which customers were exploiting their system. The team decided it was crucial to seek out a more effective and efficient solution on the market instead of building their own capabilities—and set out to find the best tool out there for their needs.

HelloFresh knew the exact criteria they were looking for in a fraud prevention solution—a flexible model that could adapt to each of their market’s unique needs, responsive and knowledgeable customer support, and an adjustable pricing model. After extensive research, HelloFresh chose to partner with Sift over the competition due to Sift’s unrivaled machine learning customization, the ability to increase or reduce thresholds depending on business needs, and hearing first-hand from other Sift customers how helpful the customer support was for them.

Solution

Rolling out 12 customized machine learning models

The implementation process was quick and simple, taking only a week and requiring minimal code. Sift helped HelloFresh to confirm whether two people with the same address are part of the same household and trying to take advantage of promo offers—and block or unblock accordingly. 

Together, HelloFresh and Sift deployed 12 custom machine learning models to accommodate each market’s needs. With these customizable models, HelloFresh can easily personalize the solution to their markets. 

HelloFresh has developed a global policy that can adapt country by country to fit local market needs, utilizing the global Risk Management team as well as local teams’ expertise. For example, the team can customize each region’s model based on the country’s unique mailing address systems, making it easier to verify households and customers who are trying to trick the system and get free boxes of HelloFresh.

Working with Sift has been a positive experience. Sift has a great tool, and it takes the least amount of time and effort. Customization is important to us, and Sift is really great with that.

Anirudh Lanka, Senior Product Owner - Payments at HelloFresh

Results

Reducing promo abuse saves HelloFresh time, money, and resources

Implementing Sift’s custom machine learning models was a huge success for HelloFresh. They were able to not only achieve 90%+ score precision in the U.S. and reduce promo abuse by 95%, but save the business time, money, and resources. 

By turning to a more automated solution with Sift’s Digital Trust & Safety Suite, the Risk Management team lowered their operational costs by spending less time on tedious manual reviews. All of this saved time, money, and resources meant that the HelloFresh team could reallocate their time and resources to other important projects within the company, improving business efficiency.

Anirudh Lanka

Sift is open to feedback, which is great in a partnership. The team is extremely proactive and our partnership has worked very well for us.

Anirudh Lanka, Senior Product Owner - Payments at HelloFresh

Download this case study

Download now

Secure your business from login to chargeback

Stop fraud, break down data silos, and lower friction with Sift.

  • Achieve up to 285% ROI
  • Increase user acceptance rates up to 99%
  • Drop time spent on manual review up to 80%
Your information will be used to contact you about our service and subscribe you to our direct marketing communications. You can, of course, unsubscribe at any time. Please see our Website Privacy Notice.