Dataiku + La Mutuelle Générale
Customer Company Size
Large Corporate
Region
- Europe
Country
- France
Product
- Dataiku
Tech Stack
- Machine Learning
- Data Visualization
- Data Collection
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Productivity Improvements
- Customer Satisfaction
Technology Category
- Analytics & Modeling - Predictive Analytics
- Application Infrastructure & Middleware - Data Exchange & Integration
Applicable Functions
- Sales & Marketing
Use Cases
- Predictive Replenishment
Services
- Data Science Services
About The Customer
La Mutuelle Générale is a French insurance company with more than 70 years of experience in the market. It serves over 1.4 million customers and 8,000 enterprise clients, generating more than €1.1 billion in turnover annually. The company is facing fierce competition in the insurance industry, with the cost of acquiring a new customer significantly increasing in recent years. To address this, the company sought to develop a decision support tool for sales to aid their understanding and prioritization of prospects.
The Challenge
La Mutuelle Générale, a French insurance company with over 70 years of experience in the market, serving over 1.4 million customers and 8,000 enterprise clients, and generating more than €1.1 billion in turnover annually, was facing a challenge in customer acquisition. The competition in the insurance industry is fierce, with organizations all vying to capture the same type of customer. The cost of acquiring a new customer has significantly increased in recent years. To address this, La Mutuelle Générale sought to develop a decision support tool for sales to aid their understanding and prioritization of prospects based on their likelihood to convert and their potential value compared to their cost of acquisition.
The Solution
La Mutelle Générale developed a machine learning-based system using Dataiku that helps sales prioritize their work by assigning an individual probability of conversion to each prospect, whether that prospect is an individual or a group. They first looked at data on existing clients, specifically their cost of acquisition and lifetime value, to establish “look alikes” for each prospect. The end result of this system is a tool available for sales that allows them to more effectively prioritize their prospects by providing two pieces of information to consider: likelihood of conversion and likelihood of recuperation of acquisition costs. The team also created an interactive map containing this data so that any travel to visit prospects could be maximized by visiting other potential prospects nearby.
Operational Impact
Quantitative Benefit
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