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.
Vendors to consider
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