The best in Saas Dynamic Pricing tool for retailers

  • Our Saas Solution is a scalable Revenue Management tool that allows you to optimise the pricing of your product catalogue to achieve different business goals.
  • You can visualize real time your performance and measure value creation.
  • Working with us also means that you have access to a set of pricing experts that will help you write a success story!
user friendly Saas interface

A step by step approach guided by a user friendly interface.


Clear performance visualization dashboards based on an unbiased methodology.

Result oriented

Optimization of different business targets per category taking into account the relative constraints.

Data driven

Our solution allows you to leverage product elasticity at the most granular level.


Our machine learning algorithms help you manage a very large product catalogue under various Point of Sales.


Our methodology allows you to reduce noise coming from external factors such as seasonality or promotions.

Understanding Price Elasticity

Our approach is based on building an elasticity curve of your products and  understanding how much price variance is driving sales, volume, and profit.

During the Learning Phase our machine learning algorithms compute (in)elasticity pockets at the most granular level. We cluster your inventory as well as customer demand price sensitivity!

During the Optimization Phase we push pricing recommendations gathered from the previous phase and analyse their outcomes in order to identify the best pricing configuration that will optimise for the set business targets.

Why Dynamic Pricing works with PricingHUB

Price Elasticity Exploration

We build the elasticity curve rather than trying to predict it.

  • Our models are based on understanding price elasticity at a specific moment in time within a given competitive context. To achieve this we develop reinforced learning models that constantly run pricing tests and measure performance vs business goals.
  • Most competitors use predictive models that leverage past data to create a pricing structure for any given product. These pricing strategies run the risk of being biased toward external events (promotions, seasonality, marketing campaigns etc.) and are subject to a lot of data noise.
  • To rigorously measure value creation, we use a sturdy data-science framework to build the best possible proxy for an A/B test without any related drawbacks. 
  • No predictive black box : we give you advance notice of the key information needed to understand a price recommendation we have made before it even goes live.
  • You give us access to the different data sources – we do the heavy lifting and build the data model.
  • Once the data lake is up and running we start the price optimization machine learning phase during which:
    • we will push you the first price recommendations
    • our customer success team trains you and helps you to set-up the pricing optimization software so you can start to optimise your pricing