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Swapfiets Enhances Customer Service with Fivetran Data Insights
Technology Category
- Functional Applications - Warehouse Management Systems (WMS)
- Infrastructure as a Service (IaaS) - Cloud Databases
Applicable Industries
- Cities & Municipalities
- Transportation
Applicable Functions
- Logistics & Transportation
- Warehouse & Inventory Management
Use Cases
- Inventory Management
- Vehicle-to-Infrastructure
Services
- Data Science Services
The Challenge
Swapfiets, the world’s first ‘bicycle-as-a-service’ company, was facing a challenge in understanding behaviors in the emerging market. The company's growth strategy relied on identifying new cities, winning new subscribers cost-effectively, and establishing an efficient local support network. However, the company was struggling with data management. The data engineering team had built custom Python scripts to extract data into their central Redshift instance, which was manageable when pulling from just a couple of data sources. However, as the business started to expand, this approach proved impractical. Swapfiets needed a more streamlined approach to data ingestion to make sense of critical subscription and usage data. It was crucial for Swapfiets to understand its target demographic and how best to provide local support, carefully target its marketing, and avoid over-provisioning stock.
About The Customer
Founded in 2014 in the Netherlands, Swapfiets is the world’s first ‘bicycle-as-a-service’ company. It has quickly grown to become one of the leading micro-mobility providers in Europe with over 270,000 monthly subscribers in Austria, Belgium, Denmark, France, the Netherlands, Spain, and the UK. The service is available in 65 cities across Europe. For a monthly subscription fee, Swapfiets members receive a fully functional bicycle or e-bike solution for their own use. If needed, a repair service is available within 48 hours to repair or directly swap the bike at no additional cost.
The Solution
Swapfiets adopted Fivetran to pull data from multiple data sources into a single centralized data warehouse. This data warehouse established a single source of truth on operations, customers, and bike usage. Standardized, real-time data then allowed Swapfiets’ marketing, service, and operations teams to make informed decisions. Beyond the short-term fix, the engagement ensured Swapfiets could scale its data sources. New sources could be integrated quickly; the data pipeline was more stable; the data science teams had consistent access to the data they need most. Swapfiets was also able to combine user analytics with terrain, weather, and cycling infrastructure to improve the user experience. This was useful in creating the pricing and services to win new subscribers, and to refine the longevity of the Swapfiets’ bikes, the responsiveness of the service teams, and inventory management.
Operational Impact
Quantitative Benefit
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