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.
After building the data lake we deploy our solution in 2 phases.
- Launch a 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 set-up the pricing tool & implement the business goals for each product category.
- We train the category managers to use the tool and leverage the exhaustive reporting dashboards.
- 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.
- Once validated prices can be uploaded automatically to our customer ́s back office thanks to a web service connectivity
- For each price recommendation validated and implemented we measure performance and contribution to the target
- 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.