Data Driven Price Optimization
Context & Objectives

An international insurer needs to transform its pricing practices in order to make them more data-driven.

Deploy algorithmic pricing practices to dynamically set product offer, pricing, and promotions.


In average across the group, improvement of both the top-line by +10% and of the bottom line by +2pp

Our approach

Step 1 – Identify and collect

  • Identify and collect all data: internal data, customer data, car data, geocoded data, other external data, etc.
  • Split projects for contracts renewals and for new business acquisition

Step 2 – Build

  • Build machine learning scores to predict: customer risk, customer elasticities, customer behavior, market competitiveness, lifetime value
  • Define the business targets and the price optimization framework and solver
  • In parallel, design the IT architecture to host the solution (dynamic optimization of prices in production). Arbitrate the make-or-buy at each step of the pricing process

Step 3 – Pilot

  • Pilot first renewal and acquisition cases with a given entity, then roll-out to 4 entities across 4 geographies

Our experts
Aimé Lachapelle
Managing Partner
Related Capabilities
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