Dataiku Case Studies Dataiku + La Mutuelle Générale

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	Dataiku + La Mutuelle Générale Dataiku

Dataiku + La Mutuelle Générale

Dataiku

Technology Category	Analytics & Modeling - Predictive Analytics Application Infrastructure & Middleware - Data Exchange & Integration
Applicable Functions	Sales & Marketing
Use Cases	Predictive Replenishment
Services	Data Science Services
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. Read More
About 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. Read More
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. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Predictive Analytics

Application Infrastructure & Middleware - Data Exchange & Integration

Applicable Functions

Sales & Marketing

Use Cases

Predictive Replenishment

Services

Data Science Services

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.

About 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.

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.

Impact #1	The development of a machine learning-based system that helps sales prioritize their work by assigning an individual probability of conversion to each prospect.
Impact #2	The creation of an interactive map containing data on prospects to maximize travel for sales visits.
Impact #3	The integration of the system into the account executives’ CRM and automatic pushing of the information proactively.

Impact #1

The development of a machine learning-based system that helps sales prioritize their work by assigning an individual probability of conversion to each prospect.

Impact #2

The creation of an interactive map containing data on prospects to maximize travel for sales visits.

Impact #3

The integration of the system into the account executives’ CRM and automatic pushing of the information proactively.

Benefit #1	Increased efficiency in customer acquisition by prioritizing prospects based on their likelihood to convert and their potential value compared to their cost of acquisition.
Benefit #2	Maximized travel for sales visits by visiting other potential prospects nearby.
Benefit #3	Improved sales productivity by integrating the system into the account executives’ CRM and automatically pushing the information proactively.

Benefit #1

Increased efficiency in customer acquisition by prioritizing prospects based on their likelihood to convert and their potential value compared to their cost of acquisition.

Benefit #2

Maximized travel for sales visits by visiting other potential prospects nearby.

Benefit #3

Improved sales productivity by integrating the system into the account executives’ CRM and automatically pushing the information proactively.

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Overview

Dataiku + La Mutuelle Générale

Operational Impact

Quantitative Benefit