Aviva: Bringing Insurance into the Age of AI
Aviva’s Customer Data Science Team is 5x more efficient in developing data projects from beginning (building a model) to end (pushing into production) with Dataiku.
Learn MoreOne 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.
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 |
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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:
For every data product, including the customer acquisition system, the data teams at La Mutuelle Générale follow the same methodology to ensure a thorough and consistent process.
La Mutuelle Générale uses Dataiku from data collection through operationalization as an end-to-end tool that saves time across all parts of the data-to-insights process, allowing the many different teams and profiles of users involved to contribute directly to the final data product:
Heetch uses Dataiku and Kubernetes to treat large quantities of data while maintaining performance and controlling costs, ensuring a positive return on investment (ROI) and smooth execution on hundreds of data projects conducted throughout the organization.
Read moreAviva’s Customer Data Science Team is 5x more efficient in developing data projects from beginning (building a model) to end (pushing into production) with Dataiku.
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