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Use case

Multiple trading targets

Your category managers want to optimize the profitability of their different product categories. To do this, they need to:

   Deploy multiple pricing strategies across different distribution channels

   Respect the business constraints

   Optimize prices for different business objectives (Revenue, Volume or Profitability)


  Build a holistic data model: aggregate different data sets from different functional areas of the business.

  Explore data and segment price elasticity along different dimensions to identify and expose profitable growth opportunities.

  Drive change and monitor the acceptance rate of price recommendations

  Provide category managers with sufficient information to understand and validate a specific price recommendation.

  Measure the isolated impact and contribution of different pricing strategies to the achievement of objective

  Deploy the tool to dozens of manager categories, sometimes spread over several countries


   The set-up phase

8 weeks to automate data flows, train users and configure the Dynamic Pricing application in SaaS according to the client’s business constraints.

   The learning phase

The product catalog is segmented into portfolios that are optimized either using the rule engine approach or using the consumer price.

The rule engine allows:

  Automatically adjust prices according to data such as competition, stock level, elasticity index, etc.

  To leverage the category manager’s business knowledge

The Target Based Pricing engine allows:

  To refocus the price on the final consumer and to adjust the price quickly according to the variations of the consumer context (period of confinement for example)

  Optimize prices automatically to meet a business objective and specific constraints

  Use AI to constantly update prices based on changes in price sensitivity and the achievement of defined objectives

In both cases a very short learning phase of a few weeks, allowing a very fast implementation.

   The continuous optimization phase

  Each price recommendation is made available on the PricingHUB interface for validation by the category manager. Validation can be manual or automatic based on rules.

  Once validated, the prices can be automatically uploaded to our client’s back- office thanks to the connectivity of a web service for automatic updates on the web and stores equipped with electronic labels.

  For each price recommendation validated and implemented, we measure the performance and contribution to the objective.


  Category managers are equipped with decision support tools based on understanding elasticity rather than focusing solely on reference data

  Category Managers have a clear understanding of their category elasticity and have access to a powerful Dynamic Pricing application to drive their P&L.

  Our price recommendations have an average acceptance rate of 90%.

  Our price recommendations have an average acceptance rate of 90%.

  Our experience has led us to an average incremental gross margin of about +10.3%, excluding the margin of error

Evaluate the potential of price elasticity on your business

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