Audit intermediate Works with: Any AI

Discount Policy Audit

Table output Has example

Check discounts for leakage and precedent.

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[VERIFY] is an editorial flag, not a blank to fill. Wherever the prompt shows [VERIFY], check that item against a reliable source before you use it.

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Audit my discount practice. List of recent quotes with list price, discount given, reason and outcome: [QUOTE_LIST]. Margin floor: [MARGIN_FLOOR]. Who may approve discounts: [APPROVAL_RULES].

Find the average and spread of discounts, deals that fell below the margin floor, discounts given without a reason, discounts unrelated to volume or term, and customers who now expect a discount. Compare win rates with discount levels carefully, noting small samples.

Return a table with Quote, Discount, Margin and Concern (red, amber, green), a score from 1 to 10 for discipline, and a simple approval ladder and discount reasons list.

Also suggest how to record the reason for each discount at quote time, a monthly review of exceptions with the approver, and a short script for declining a discount request politely while offering a non-price alternative such as terms or scope.

Label every assumption, keep forecasts as ranges rather than predictions, and tag legal, tax and reporting requirements [VERIFY] for [JURISDICTION] before I rely on them.

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Replace each bracketed placeholder with your own details, then paste into Any AI or another assistant.

See an example result (fictional data)
Quote review (invented 20 quotes)
Average discount 14%; range 0 to 32%. Margin floor 25%.
Below the margin floor: 5 quotes (25%). Largest: 32% given to keep a repeat customer; margin 11%.
No reason recorded: 8 quotes.
Discounts unrelated to volume or term: 9 quotes.
Win rates: quotes with discount over 20% won 4 of 7; quotes with 0 to 10% won 5 of 9. Small samples; no strong conclusion.
Approval ladder: up to 10% rep; 10 to 20% manager with a reason; over 20% director.
Reason list: volume, term, competitor, strategic, goodwill. Check margin before sending.

Illustrative only, made-up data. Your results will differ and must be reviewed.

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