DataRobot Case Studies US Foods Analyzes Transactions from 300,000 Customers with Snowflake and DataRobot

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	US Foods Analyzes Transactions from 300,000 Customers with Snowflake and DataRobot DataRobot

US Foods Analyzes Transactions from 300,000 Customers with Snowflake and DataRobot

DataRobot

Technology Category	Analytics & Modeling - Predictive Analytics Infrastructure as a Service (IaaS) - Cloud Computing
Applicable Industries	Food & Beverage
Applicable Functions	Business Operation Sales & Marketing
Use Cases	Predictive Maintenance Supply Chain Visibility
Services	Cloud Planning, Design & Implementation Services Data Science Services
Challenge	US Foods, one of America's largest food companies, was facing significant challenges with its legacy, on-premises data warehouse. The system required constant maintenance, experienced frequent resource contention, and could not affordably store more than two years’ worth of data. Business analysts took weeks to prepare a single report due to the system’s counterintuitive user interface, inability to load large data sets, and limited BI features. Reporting delays led some business users to seek insights from siloed Microsoft Access databases and Excel spreadsheets. Data science modeling to predict customer loyalty and churn rate was simply impossible. US Foods evaluated several cloud data management solutions, but none offered the right mix of performance and affordability. Read More
About Customer	US Foods is a food service distributor and one of America's largest food companies. The company is based in Rosemont, Illinois and has approximately 26,000 employees. US Foods provides an expansive catalog of food products, culinary equipment, supplies, and technology to approximately 300,000 restaurants and food service operators. The company uses data analytics and data science to monitor performance, predict churn rate, and accelerate growth. Read More
Solution	US Foods adopted Snowflake’s cloud data platform, which scaled to become their single analytics repository for transaction data. The Snowflake Connector for Python and bulk loading from Amazon S3 enabled daily ingestion of large data sets without causing bottlenecks. Snowflake’s native support for SQL and clean, easy-to-navigate interface accelerated report creation. DataRobot integration enabled predictive analytics for churn rate that identified at-risk customers in need of proactive outreach by US Foods’ retention team. This solution provided a significant improvement in data management and predictive analytics capabilities for US Foods. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Predictive Analytics

Infrastructure as a Service (IaaS) - Cloud Computing

Applicable Industries

Food & Beverage

Applicable Functions

Business Operation

Sales & Marketing

Use Cases

Predictive Maintenance

Supply Chain Visibility

Services

Cloud Planning, Design & Implementation Services

Data Science Services

Challenge

US Foods, one of America's largest food companies, was facing significant challenges with its legacy, on-premises data warehouse. The system required constant maintenance, experienced frequent resource contention, and could not affordably store more than two years’ worth of data. Business analysts took weeks to prepare a single report due to the system’s counterintuitive user interface, inability to load large data sets, and limited BI features. Reporting delays led some business users to seek insights from siloed Microsoft Access databases and Excel spreadsheets. Data science modeling to predict customer loyalty and churn rate was simply impossible. US Foods evaluated several cloud data management solutions, but none offered the right mix of performance and affordability.

About Customer

US Foods is a food service distributor and one of America's largest food companies. The company is based in Rosemont, Illinois and has approximately 26,000 employees. US Foods provides an expansive catalog of food products, culinary equipment, supplies, and technology to approximately 300,000 restaurants and food service operators. The company uses data analytics and data science to monitor performance, predict churn rate, and accelerate growth.

Solution

US Foods adopted Snowflake’s cloud data platform, which scaled to become their single analytics repository for transaction data. The Snowflake Connector for Python and bulk loading from Amazon S3 enabled daily ingestion of large data sets without causing bottlenecks. Snowflake’s native support for SQL and clean, easy-to-navigate interface accelerated report creation. DataRobot integration enabled predictive analytics for churn rate that identified at-risk customers in need of proactive outreach by US Foods’ retention team. This solution provided a significant improvement in data management and predictive analytics capabilities for US Foods.

Impact #1	Snowflake’s cloud data platform scaled to become US Foods’ single analytics repository for transaction data.
Impact #2	Snowflake Connector for Python and bulk loading from Amazon S3 enabled daily ingestion of large data sets without causing bottlenecks.
Impact #3	Snowflake’s native support for SQL and clean, easy-to-navigate interface accelerated report creation.

Impact #1

Snowflake’s cloud data platform scaled to become US Foods’ single analytics repository for transaction data.

Impact #2

Snowflake Connector for Python and bulk loading from Amazon S3 enabled daily ingestion of large data sets without causing bottlenecks.

Impact #3

Snowflake’s native support for SQL and clean, easy-to-navigate interface accelerated report creation.

Benefit #1	One report that previously took five hours was executed in three minutes with Snowflake.
Benefit #2	The solution enabled the company to store more than two years' worth of data affordably.
Benefit #3	The solution reduced the time taken to prepare a single report from weeks to minutes.

Benefit #1

One report that previously took five hours was executed in three minutes with Snowflake.

Benefit #2

The solution enabled the company to store more than two years' worth of data affordably.

Benefit #3

The solution reduced the time taken to prepare a single report from weeks to minutes.

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Overview

US Foods Analyzes Transactions from 300,000 Customers with Snowflake and DataRobot

Operational Impact

Quantitative Benefit