Data-driven price optimization


Context and 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.


Key project steps

  1. Identify and collect all data: internal data, customer data, car data, geocoded data, other external data, etc.
  2. Split projects for contracts renewals and for new business acquisition
  3. Build machine learning scores to predict: customer risk, customer elasticities, customer behavior, market competitiveness, lifetime value
  4. Define the business targets and the price optimization framework and solver
  5. 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
  6. Pilot first renewal and acquisition cases with a given entity, then roll-out to 4 entities across 4 geographies


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