AI use case · Is your data ready for pricing and rebate optimisation?Pricing AI optimises against your ledger. So the ledger had better be right.
Pricing and rebate tools promise better margin on every quote. They depend on knowing the net price each customer really paid, after discounts, rebates and credits. When the quote lives in the CRM, the rebate in a spreadsheet and the credit in the ERP, nobody knows that number, and neither does the model.
Starts with a 30-minute call. Commits you to nothing.
What it needs
What pricing and rebate optimisation needs from your data.
Each requirement, the systems it lives in, and what we check. The AI is the easy part; this list is the hard part. Each check feeds the Truth Score™, a score out of 100 for how far your data can be trusted.
Truth Score pillars that gate it mostAccuracyConnectivityConsistencyThe six pillars
Where it breaks
Every system looks fine. The gaps between them don’t.
Where pricing and rebate optimisation breaks: not inside one system, but where two of them disagree about the same customer, deal or invoice.
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CRM ↔ Finance
Quoted prices that differ from invoiced prices
What it does to the AIThe model learns from quotes; the business earns from invoices.
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Spreadsheets ↔ Finance
Rebates agreed in spreadsheets and paid as lump-sum credits
What it does to the AINet price per customer is unknown, so the “optimal” price is a guess.
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Operations ↔ Finance
Products re-coded, with history split across old and new codes
What it does to the AIPrice sensitivity is learnt from half the history.
Gaps to test for this use case, not client findings.
Before you sign
Questions to ask the vendor selling it.
Four questions that separate a demo from a deployment. Bring the answers, or the proposal, to the Truth Score Read and we test them against your records.
- Q1
Do you optimise list price, discount, or net price after rebates?
Only the last one is margin. Optimising the first two can raise the headline price and lower what you keep.
- Q2
Where does the tool get cost and margin from?
If cost is a single standard figure set years ago, the margin it optimises is fiction.
- Q3
How do you treat products whose codes changed?
If it can’t link old and new codes, it learns from part of your history and assumes the rest never happened.
- Q4
Can a sales lead see and override a recommendation, and is that logged?
Pricing is a commercial decision. You need to know who overrode what, and whether the override was right.
How a Truth Score Read tests it
Pricing and rebate optimisation, tested against your own records.
Our engine matches the customers, people, deals and invoices across the exports from the systems behind the decision. Every red flag is checked by hand against sample records before sign-off, and the verdict comes in writing. Here is what that means for this use case.
- 01
Rebuild net price
Invoice lines, credit notes and the rebate file joined, so the price each customer actually paid can be calculated, or shown not to be calculable.
- 02
Trace the product
Products from CRM quotes matched to ERP items and invoice lines, including re-coded and duplicate items.
- 03
Check the terms
A sample of contract price agreements compared with what was invoiced.
- 04
Give the verdict
Whether the pricing proposal can run now, after fixes, or not at all, with the gaps listed.
Two systems are enough to start. We send the exact field list after the call.
How the Truth Score Read worksWhat you get back
See what comes back: a real sample memo.
One fictional B2B software company, Example Co: 72,837 records in 7 exports from its CRM, finance and HR systems, run through the same engine and the same checks as a paid Truth Score Read. Each system passes its own checks. Matched across systems, only 13% of deals tie to an invoice.
Questions
Pricing and rebate optimisation, answered plainly.
Anything else, ask on the 30-minute call. It commits you to nothing.
Book the callWhat data does AI pricing optimisation need?
The net price each customer paid on each line, after discounts, rebates and credits; one product code from quote to invoice; and a cost you can trust. Most of that lives in the ERP and billing, with the awkward parts in spreadsheets.
Why do rebates cause so much trouble?
Because they are usually agreed per customer, tracked outside the ledger and paid in lump sums. The invoice shows one price, the customer effectively pays another, and a pricing model only ever sees the first.
Our pricing tool gives odd recommendations. Why?
Usually because it is optimising against a false baseline: quoted rather than invoiced prices, stale costs, or a history fragmented by product re-coding. Each can be found by matching exports across the CRM, ERP and the rebate file.
Can you check one product line or region first?
Yes. Starting with one segment is a sensible way in: one region or product family, tested end to end, before the tool is rolled out further.
Is your data ready for pricing and rebate optimisation? Find out before you sign.
Exports from two or more systems in. A signed verdict on this use case within 5 working days of complete exports. Starts with a 30-minute call. Commits you to nothing.
Starts with a 30-minute call. Commits you to nothing.