From Churn Analysis to Predictive Safety: Coyote’s 5-Year Journey with Dataiku

Dataiku Company, Dataiku Product, Scaling AI Nancy Koleva

In order to successfully implement a data-driven strategy and embark on the journey to Enterprise AI, companies often need to start small with simpler, concrete impact projects, in order to convince stakeholders in the value of their data initiatives before moving on to more advanced applications. This is precisely what Coyote, the European leader in real-time road information and one of Dataiku’s oldest customers, was able to do.

Incidentally, this week marks the 5-year anniversary since Coyote became a Dataiku customer. Coyote uses IoT-based devices and mobile applications that enable their users to warn other drivers of traffic hazards and conditions (e.g., traffic obstruction, accidents, , etc.) that are detected while driving. Initially having started out with predictive analytics for improving their customer retention, today Dataiku has become an integral part of Coyote’s predictive safety operations.

Ensuring subscriber retention and loyalty with predictive behavioral analytics

Before venturing into predictive safety for their core services, they built and implemented a predictive behavioral analysis application to customer knowledge and service improvement. The application automatically compiles and processes heterogeneous and completely anonymized data (contractual data, customer-declared data, real-time device data...). This data is then processed by a machine learning algorithm to model user experience. This model and its results were subsequently adjusted in order to optimize marketing campaigns and resulted in an 11% increase in efficiency of their outbound campaigns.

By improving retention rates, Coyote wished to enhance the following virtuous circle: the more users they acquire and retain, the better the service quality of their applications, and vice versa. What’s more, having proven the value of leveraging predictive analytical capabilities through a centralized platform for their marketing operations, Coyote’s data team now had the opportunity to evangelize the use of data science and predictive machine learning for the company’s core services - using IoT devices to improve road safety.

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Leveraging machine learning and IoT devices to improve road safety

More recently, Coyote has developed a project using Dataiku to identify steep, potentially dangerous turns on car roads and, based on that, to develop a dynamic recommended speed limit model to prevent road accidents. The project is comprised of four major elements:

  1. Identifying all S-curves in France and calculating their angle, as well as the distance between them.
  2. Developing a dynamic recommended speed limit model based on this data;
  3. Building and releasing a database of dangerous road curves;
  4. Monitoring and service optimization.

Dataiku’s centralized and elastic environment, collaboration features and focus on operationalization have allowed Coyote to significantly reduce the time needed for feature engineering, successfully deploy and continuously optimize their predictive maintenance products and services for delivering quality driver assistance and improving road safety for everyone.

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