Pricing Twin
What price creates the most value — not just the most revenue?
How should we price when customers, competitors and demand do not react uniformly?
Pricing decisions are often made with incomplete information about customer response.
Historical prices show what happened.
They do not automatically tell us what would have happened under a different price.
Simple margin calculations therefore miss the most important part of the decision: behavioural response.
The highest price is not necessarily the
most profitable price.
It turns pricing into a response and scenario model.
A Pricing Twin combines historical prices, volumes, customer or product characteristics, cost information and relevant market drivers.
The output is not simply a recommended price.
It is a pricing response surface showing the trade-offs behind different pricing choices.
Price–demand response modelling
Estimate how demand changes as price changes, while controlling for relevant product, customer or market characteristics.
Price sensitivity segmentation
Identify products, customer groups or markets with materially different price responsiveness.
Revenue and margin optimisation
Translate demand response into expected revenue and contribution margin across alternative price points.
Scenario simulation
Test alternative price, cost and competitor scenarios before implementing a change.
Which products or customers can absorb a price increase?
Where are we discounting more than necessary?
What price maximises revenue?
What price maximises contribution margin?
How much volume is at risk under a proposed price increase?
Which segments are most price-sensitive?
