Casper Finds Their Dream Data Platform in Looker
Company Size
200-1,000
Region
- America
- Europe
Country
- Austria
- Germany
- Switzerland
- United Kingdom
- United States
Product
- Looker
- AWS
- Redshift
- Stitch
Tech Stack
- Data Warehousing
- Data Visualization
- Real-time Data Processing
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Customer Satisfaction
- Productivity Improvements
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
About The 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.
The 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.
The 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.
Operational Impact
Quantitative Benefit
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