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Lifetime Value Optimization through Data Centralization: A BlaBlaCar Case Study
Technology Category
- Analytics & Modeling - Big Data Analytics
- Infrastructure as a Service (IaaS) - Cloud Databases
Applicable Industries
- Cement
- Transportation
Applicable Functions
- Logistics & Transportation
- Sales & Marketing
Use Cases
- Demand Planning & Forecasting
- Last Mile Delivery
Services
- Data Science Services
The Challenge
BlaBlaCar, the world's first online carpooling booking service, faced a significant challenge in accessing and utilizing their data. The company's Business Intelligence (BI) teams were heavily dependent on IT teams for reporting and analytics. The process of data retrieval was time-consuming and repetitive, often taking days to deliver the requested data. The company's data sources were heterogeneous and scattered, making it difficult for the BI teams to access the data on demand. The challenge was to find a solution that could clean, consolidate, and centralize these data sources for easy and immediate access by BI teams globally.
About The Customer
BlaBlaCar is a pioneering company that launched the world's first online carpooling booking service. This innovative approach transformed carpooling into a viable means of transportation. The company has experienced rapid growth, boasting over 10 million members and operations in 13 countries. It continues to expand at a rate of 200% per year. BlaBlaCar operates in the online car sharing platform industry, primarily in the European and Indian markets. The company's primary use case for data science is Lifetime Value Optimization (CRM).
The Solution
BlaBlaCar adopted Data Science Studio to automate the cleansing and centralization of data for global on-demand access. A developer and a BI Data Manager built a stream that automatically retrieves data from various sources, including SQL databases, social network data, user reviews, and partner logs. The data was then aggregated and stored in a Vertica database, optimized for analytical calculations and complex queries in Big Data environments. This solution allowed business, marketing, and BI teams to build and view reports without regenerating complex and expensive queries on SQL databases. The BI teams used Tableau Software, connected to the Vertica database, to create visualizations of the data.
Operational Impact
Quantitative Benefit
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