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AI USE CASE
Configure-price-quote (CPQ) optimisation
Recommend pricing, discounts and product configurations that maximise win rate and margin.
What it is
An ML model trained on historical quotes, win/loss outcomes and customer attributes recommends the optimal price and configuration for every deal. Sales reps still set the final number, but get guardrails that protect margin. Typical impact: +2–5 points of gross margin without losing deals.
Data you need
24+ months of quotes with win/loss outcomes and pricing context.
Required systems
- crm
- erp
Why it works
- Make overrides easy but tracked
- Refresh the model quarterly with new wins/losses
How this goes wrong
- Reps override the model on every deal
- Model trained on a discount-heavy past keeps recommending discounts
When NOT to do this
Skip if your pricing is genuinely simple, the ROI hides in complexity.
Example vendors
This page describes an idea. It gives no budget or timeline: those depend on your company. The free diagnostic tells you which ideas fit your situation.