Google Cloud Platform Case Studies Casper Finds Their Dream Data Platform in Looker

Edit This Case Study Record

	Casper Finds Their Dream Data Platform in Looker Google Cloud Platform

Casper Finds Their Dream Data Platform in Looker

Google Cloud Platform

Technology Category	Analytics & Modeling - Real Time Analytics Platform as a Service (PaaS) - Data Management Platforms
Applicable Industries	Retail
Applicable Functions	Business Operation Sales & Marketing
Use Cases	Inventory Management Supply Chain Visibility
Services	Cloud Planning, Design & Implementation Services Data Science Services
Challenge	Casper, a small industry disruptor in the mattress industry, was facing a challenge with scattered and siloed data across files. The underlying technology was not efficient or scalable, and Casper needed a tool to take them to the next level. The business user front end was also impacting company decision making. Analysis was siloed in files and people couldn’t do much more with it other than look at the data. Querying across sources was a huge task and no one place held all the information. It was everywhere. Read More
About Customer	Casper is a company that set out to disrupt the stale mattress industry with one product and a mission in April 2014. Since then, Casper has expanded their product line to include pillows, sheets, a perfect foundation — and even a dog mattress. Casper products are now available in the UK, Germany, Austria and Switzerland, and you can try them out in person at West Elm stores or in flagship showrooms in New York, Los Angeles, and London. Read More
Solution	Casper implemented Looker and AWS to centralize and share data company wide for unforeseen insights and unexpected growth. Redshift became the central warehouse for all of Casper’s data, which helped them to seamlessly join previously siloed datasets. They used Stitch to load their Postgres databases into their data warehouse in real time. Within 30-minute refresh cycles, their data was always available in Redshift, and ultimately in Looker. This allowed them to quickly pick tables to include, then pick fields from those tables. They could turn them on, turn them off, and set the cycle increment they’d like to refresh that data on. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Real Time Analytics

Platform as a Service (PaaS) - Data Management Platforms

Applicable Industries

Retail

Applicable Functions

Business Operation

Sales & Marketing

Use Cases

Inventory Management

Supply Chain Visibility

Services

Cloud Planning, Design & Implementation Services

Data Science Services

Challenge

Casper, a small industry disruptor in the mattress industry, was facing a challenge with scattered and siloed data across files. The underlying technology was not efficient or scalable, and Casper needed a tool to take them to the next level. The business user front end was also impacting company decision making. Analysis was siloed in files and people couldn’t do much more with it other than look at the data. Querying across sources was a huge task and no one place held all the information. It was everywhere.

About Customer

Casper is a company that set out to disrupt the stale mattress industry with one product and a mission in April 2014. Since then, Casper has expanded their product line to include pillows, sheets, a perfect foundation — and even a dog mattress. Casper products are now available in the UK, Germany, Austria and Switzerland, and you can try them out in person at West Elm stores or in flagship showrooms in New York, Los Angeles, and London.

Solution

Casper implemented Looker and AWS to centralize and share data company wide for unforeseen insights and unexpected growth. Redshift became the central warehouse for all of Casper’s data, which helped them to seamlessly join previously siloed datasets. They used Stitch to load their Postgres databases into their data warehouse in real time. Within 30-minute refresh cycles, their data was always available in Redshift, and ultimately in Looker. This allowed them to quickly pick tables to include, then pick fields from those tables. They could turn them on, turn them off, and set the cycle increment they’d like to refresh that data on.

Impact #1	Efficient data modeling with Looker providing an easy-to-manage source of truth.
Impact #2	More people using the data in Looker, with a central location where everyone has access to all the existing analysis that has been produced for the business.
Impact #3	Supply chain team able to ensure fast delivery of all products due to being very data-driven.

Impact #1

Efficient data modeling with Looker providing an easy-to-manage source of truth.

Impact #2

More people using the data in Looker, with a central location where everyone has access to all the existing analysis that has been produced for the business.

Impact #3

Supply chain team able to ensure fast delivery of all products due to being very data-driven.

Benefit #1	Data available in real time with 30-minute refresh cycles.
Benefit #2	Data made available company-wide.

Benefit #1

Data available in real time with 30-minute refresh cycles.

Benefit #2

Data made available company-wide.

Download PDF Version

Overview

Casper Finds Their Dream Data Platform in Looker

Operational Impact

Quantitative Benefit