Rafter nimbly outpaces its competitors by using flexible, real-time analytics in Looker
Company Size
200-1,000
Region
- America
Country
- United States
Product
- Looker
- Pivotal Greenplum MPP data warehouse
Tech Stack
- LookML modeling language
- SQL databases
- GitHub
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Cost Savings
- Customer Satisfaction
- Productivity Improvements
- Revenue Growth
Technology Category
- Analytics & Modeling - Big Data Analytics
- Analytics & Modeling - Real Time Analytics
- Application Infrastructure & Middleware - Data Exchange & Integration
Applicable Industries
- Education
- Retail
Applicable Functions
- Business Operation
- Sales & Marketing
Use Cases
- Inventory Management
- Predictive Quality Analytics
- Real-Time Location System (RTLS)
Services
- Data Science Services
- System Integration
About The Customer
Rafter operates a cloud-based platform that helps colleges and universities make educational content more affordable, effective, and accessible. The Rafter platform drives college textbook rentals through Rafter’s own BookRenter.com website, as well as more than 300 white-labeled college online bookstores and popular websites such as half.com, amazon.com, and staples. com. The company also offers an integrated suite of applications for course materials management, which allows professors to discover, adopt, and supply textbooks online; and EasyRent, an in-store textbook rental offering that integrates college bookstore point-of-sale systems with the Rafter platform. The company relies heavily on a modern data warehouse and sophisticated data analytics to stay ahead in a fiercely competitive market.
The Challenge
Rafter, a company that operates a cloud-based platform for colleges and universities to make educational content more affordable, effective, and accessible, was facing challenges in maintaining a competitive position in the market for online book rentals. The company was bottlenecked by a nightly ETL execution that took up to 10 hours and caused slowdowns during the day. Excel and an existing data visualization tool couldn’t handle analytics at scale. In addition, most of the Rafter staff was dependent on data analysts for writing routine queries, causing further delay before their questions could be answered. These limitations hampered critical activities across the business such as finance, inventory management, pricing and campaign management, and operations.
The Solution
Rafter installed Looker as its BI solution to run on top of a Pivotal Greenplum MPP data warehouse. This allowed the company to move from a daily data refresh to near real-time updates used for operational decision-making in a dynamic market. Because the LookML modeling language makes it easy to create reusable queries, everyone in the company has immediate access to the data they need, while analyst resources are freed up for high-value projects such as automating book liquidation and streamlining cumbersome accounting processes. With the LookML modeling language, the data team had the metadata layer they needed to define and maintain consistent metrics, empower end users, and further reduce demand for ad hoc queries. Frontline teams could access near-live datasets with the ability to drill down, create their own metrics on the fly, and pose queries without knowing SQL.
Operational Impact
Quantitative Benefit
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