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

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

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

Sift

Technology Category	Analytics & Modeling - Machine Learning
Applicable Industries	Food & Beverage
Applicable Functions	Business Operation Sales & Marketing
Use Cases	Fraud Detection
Services	Data Science Services
Challenge	HelloFresh, the world’s leading meal kit company, faced a significant challenge with users exploiting their promotional offers, which was hurting their bottom line. The company initially tried to tackle 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. They were looking for a flexible model that could adapt to each of their market’s unique needs, responsive and knowledgeable customer support, and an adjustable pricing model. Read More
About Customer	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. Read More
Solution	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. 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. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Machine Learning

Applicable Industries

Food & Beverage

Applicable Functions

Business Operation

Sales & Marketing

Use Cases

Fraud Detection

Services

Data Science Services

Challenge

HelloFresh, the world’s leading meal kit company, faced a significant challenge with users exploiting their promotional offers, which was hurting their bottom line. The company initially tried to tackle 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. They were looking for a flexible model that could adapt to each of their market’s unique needs, responsive and knowledgeable customer support, and an adjustable pricing model.

About Customer

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.

Solution

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. 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.

Impact #1	More accurately and effectively surface and stop promo fraud
Impact #2	Consistently saving time, money, and resources
Impact #3	Reallocating resources continuously drives business growth

Impact #1

More accurately and effectively surface and stop promo fraud

Impact #2

Consistently saving time, money, and resources

Impact #3

Reallocating resources continuously drives business growth

Benefit #1	95% reduction in promo abuse
Benefit #2	90%+ score precision in the U.S.

Benefit #1

95% reduction in promo abuse

Benefit #2

90%+ score precision in the U.S.

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Overview

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

Operational Impact

Quantitative Benefit