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
- 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 and segment elasticity on different dimensions to identify and expose profitable growth opportunities.
- Launch the Learning Phase:
Our AI computes 6 different price points to be tested each day of the week so we can build and segment the elasticity curve.
- We can segment elasticity for multiple dimensions such as day of the week, marketing channels, … and help you identify pockets of profitable growth.
- You use our simulation tool to set-up your business goals and constraints we want to optimise for.
- Run the Optimisation phase:
The Dynamic pricing application computes and pushes price recommendations for the category manager or the pricer to validate.
- Validation can be manual, or automatic based on rules.
- For each price recommendation implemented we measure both contribution to the target and performance vs the control group.
- 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 application to steer their P&L.
- Our price recommendations have an average acceptance rate of 90%.
- Our experience has driven us to an average incremental gross profit around +10,3%, out of error range.