SnappCar case

Ranking the Cars

 

Introduction

SnappCar is well on its way to become the leading peer-to-peer carsharing community in Europe. To better serve their community, they always want to serve the car that suits a specific customer best. A data driven game.


The Business
– SnappCar is well on its way to become the leading peer-to-peer carsharing community in Europe. Within the SnappCar community, thousands of car owners share their personal cars with other people. The sharing community including people like neighbors and friends, all looking for a more social, durable and cheaper alternative. SnappCar wants everyone to think more about how to efficiently own a car, because the more cars we share, the less we need. SnappCar aims for 5 million fewer cars in Europe by 2022.

The Solution – To accomplish fast growth and expand in other countries, a mature data infrastructure plays a crucial role. The data science team of SnappCar started building a data infrastructure, but has the ambition to take this to a next level. To get an objective review of the maturity level of their data infrastructure, they asked Enjins to do a Machine Learning Audit. In this audit, we challenged SnappCar’s existing data science infrastructure and delivered a detailed report including advice on how to make this more scalable and future-proof. Once the data science infrastructure reaches a higher level, Enjins will help the data science team. Namely, the goal is to create a personalized car ranking algorithm together.

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