solutions/margin & pricing

The right price, customer by customer.

Find the price or discount that maximises margin for each customer, inside the rules you set. No blanket discounting, no leaving money on the table.

profit_curve.solvesolved
recommended price
$0
margin lift
+0.0%
discount cap
[✓] best price within 10% discount cap
Why a score isn't enoughA / the problem >>>

Blanket discounts are easy and expensive. They give margin away to customers who would have paid full price and under-discount the ones on the fence. Most teams have no defensible way to set price per segment.

From data to decisionB / how it fits >>>
// you feed it

Transaction history, customer attributes, and your pricing rules: floors, caps, and the maximum discount you'll allow.

// it decides

It models the response curve per customer and finds the margin-maximising price within your guardrails.

// you get

A recommended price or discount per customer, with the expected margin lift and the reason behind it.

How it worksC / the engine >>>
01

model the curve

It learns how each segment responds to price, explained by the features that drive sensitivity.

02

respect the rules

Recommendations stay inside your floors, caps, and maximum discount, never a price you can't honour.

03

rank by lift

Customers are ranked by margin opportunity so you act on the biggest wins first.

What it runs onD / inputs >>>
[✓]Transaction & order history
[✓]Customer / segment attributes
[✓]Price floors, caps & discount rules
[✓]Margin targets
What you walk away withE / outcomes >>>
+8.1%typical margin lift within a 10% discount cap
per-customerpricing, not a blunt blanket rate
explainableevery price carries its drivers
Common questionsF / faq >>>
Other leversG / more >>>

Stop guessing. Start deciding.

engine: online