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Revolutionizing Car Rental Industry: Europcar Mobility Group's Data-Driven Approach
Technology Category
- Analytics & Modeling - Machine Learning
- Sensors - Autonomous Driving Sensors
Applicable Industries
- Automotive
- Transportation
Applicable Functions
- Logistics & Transportation
- Procurement
Use Cases
- Autonomous Transport Systems
- Transportation Simulation
Services
- Data Science Services
The Challenge
Europcar Mobility Group, a global mobility service provider operating in 130 countries, was facing challenges in accurately predicting fluctuations in demand for car rentals at airports based on market changes. The International Air Transport Association predicted an increase of 2.35 billion annual passengers by 2037, particularly in the Asia-Pacific region, which would significantly impact Europcar's operations. To address this, Europcar aimed to build an application using data from various sources, including fleet traffic, passenger volume, reservation and billing data, and data on new airline routes. However, the data was scattered across different locations, in different formats, and was massive in volume, posing a significant challenge.
The Customer
Europcar Mobility Group
About The Customer
Europcar Mobility Group, in business since 1949, has built a strong car rental network and is now a global mobility service provider. The group operates in 130 countries and offers traditional car and truck rentals through several brands, including Europcar and Goldcar. They also provide new mobility services through brands like Ubeeqo, the leading car-sharing service. Other activities include scooter sharing, peer-to-peer car sharing, and driver services. Europcar has been able to transform their approach to executing data-driven projects at the company, using machine learning and advanced data strategies for better forecasting, pricing, and field/capacity management.
The Solution
Europcar assembled a cross-functional team of analysts, IT professionals, and the head of forecasting to build a solution that would enable better decision-making regarding the purchase and movement of vehicles between airport hubs. They leveraged Dataiku to build a predictive web application and dashboards that forecast activity by country, suggest optimized fleet distribution, and define revenue and capacity management strategies. The data and the web application, along with the dashboards, were shared with everyone in the company, from top management to analysts, promoting data democratization. Europcar also plans to automate data ingestion, improve data quality, and collaborate with more departments to identify additional data sources.
Operational Impact
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