Coyote: From Churn Analysis to Predictive Safety

Technology Category

Analytics & Modeling - Machine Learning
Analytics & Modeling - Predictive Analytics

Applicable Industries

Automotive
Telecommunications

Applicable Functions

Logistics & Transportation
Sales & Marketing

Use Cases

Predictive Maintenance
Traffic Monitoring
Vehicle Telematics

Services

Data Science Services
System Integration

The Challenge

Coyote, a European leader in real-time road information, uses IoT-based devices and mobile applications to warn drivers of traffic hazards and conditions. The company collects extensive data on the different uses of its community, such as mileage, time spent on the road, or the number of alerts issued by the community members. Initially, Coyote started with predictive analytics for improving their customer retention. However, they wanted to leverage the value of their vast data sources and implement a data-driven strategy at the heart of their core products and services. They aimed to improve road safety using IoT devices.

About The Customer

Coyote is the European leader in real-time road information. The company uses IoT-based devices and mobile applications that enable their users to warn other drivers of traffic hazards and conditions that are detected while driving. Through its connected devices, Coyote collects extensive data on the different uses of its community, such as mileage, time spent on the road, or the number of alerts issued by the community members. The company aims to improve road safety using IoT devices.

The Solution

Coyote developed a project using Dataiku to leverage vast amounts of IoT-derived data to identify steep, potentially dangerous turns on car roads and develop a dynamic recommended speed limit model to prevent road accidents. The machine learning model facilitated the detection of speed limit anomalies and enabled Coyote to estimate the global quality and reliability of the displayed speed limit. The project involved identifying all S-curves in France and calculating their angle, developing a dynamic recommended speed limit model based on this data, building and releasing a database of dangerous road curves, and monitoring and service optimization. Dataiku provided the end-to-end solution for connecting to data, building the dynamic speed limit model, feature engineering, deployment of database, data visualization and visual AutoML, and model monitoring and optimization.

Operational Impact

Coyote was able to develop a dynamic recommended speed limit model to prevent road accidents.
The machine learning model facilitated the detection of speed limit anomalies and enabled Coyote to estimate the global quality and reliability of the displayed speed limit.
Coyote was able to build and release a database of dangerous road curves.

Quantitative Benefit

Coyote's marketing campaigns saw an 11% increase in efficiency thanks to increased knowledge of the actual uses of the service.
Coyote was able to successfully deploy and continuously optimize their predictive maintenance products and services for delivering quality driver assistance and improving road safety.