Use Cases

Pricing strategies approach

We don’t believe in the one-size-fits-all approach to Dynamic Pricing. Our Dynamic Pricing solutions will provide you with a library of algorithms that optimise for different use cases. You can choose from: multi-product pricing, psychological pricing, cross selling, etc…

Multiple Trading Targets

Our client, a major retailer in the French market doing 20% of its revenue on-line, is looking for a way to help category managers to better optimise profitability in their different product categories. PricingHUB provides price optimization software for different categories pursuing different business goals (Revenue, Volume or Profitability).

Challenges :

  • Build a holistic data model : aggregating different datasets from various functional business areas.
  • Monitor price recommendation acceptance rate: Provide Category managers with enough information so they can understand and validate a specific price recommendation.
  • Explore the data to identify and expose profitable growth opportunities.

Methodology :

After building the data lake we deploy our solution in 2 phases.

  1. Launch a learning phase
    • We run the algorithm to build the first points of the elasticity curve.
    • We set-up the pricing tool & implement the business goals for each product category.
    • We train the category managers for the tool.
  2. Optimisation phase
    • The pricing tool pushes price recommendations and optimises for the target.

Achievements :

  • Category managers are equipped with data driven decision pricing tools based on elasticity understanding rather than only focusing on benchmark data.
  • Category managers have a clear understanding of the elasticity of their category and have access to a powerful Dynamic Pricing tool to steer their P&L.
  • Our experience has driven us to an average incremental gross profit around +10,3%, out of error range.
  • We aim to achieve the best balance among the multiple KPIs targeted.
  • We implement the process & tools looking after more than 90% of price recommendation adoption.

Use Cases