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Data Science for More Effective Customer Acquisition in Insurance
One of the reasons data is so integrally important for insurance is because competition in the industry is fierce — organizations are all looking to capture the same type of customer, and the amount of money needed to acquire a new customer has exploded in recent years.
We sat down with Yann-Erle Le Roux, Data Strategy Manager, and Magalie Didier, Data Scientist and Head of Customer Knowledge, at La Mutuelle Générale to understand how they have made customer acquisition smarter by augmenting the work of humans with data science.
- More than 70 years of experience in the market.
- Serving over 1.4 million customers and 8,000 enterprise clients.
- More than €1.1 billion in turnover annually
La Mutelle Générale developed a machine learning-based system 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. In order to do this, they first looked at data on existing clients; specifically, their cost of acquisition and lifetime value. They use this analysis to establish “look alikes” for each prospect — that is, an existing customer who has similar characteristics and therefore will likely mirror the future actions of the 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:
1. Likelihood of conversion
2. 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.
There were multiple teams and people involved in going from raw data to end product for La Mutuelle Générale’s customer acquisition prediction system. The company has two data-focused teams:
|Data Intelligence Department
Mission: Break down data silos within the company.
Includes teams that manage the data warehouse, data lake, and business analytics teams/tools.
± 20 people
|Data and Analytics Department
Mission: Cross-company expansion of information capital
Includes data science and AI teams plus the customer knowledge team.
± 9 people, including lead data scientist, data scientists, business analysts, project managers
In addition, the customer acquisition project also involved other teams throughout the business to ensure that the final product was aligned with business goals and needs:
- Marketing and sales
- Internal communications
- Customer accounts division