Show what customers like this one usually add, before the salesperson finishes the quote.
AI Deal Optimisation looks at past orders and quotes to suggest an add-on, a bundle or a larger pack while a deal is open. It also notes what lost deals had in common. The salesperson chooses what to offer. Nothing is added to a customer’s quote without that choice.
Many orders leave money on the table for a dull reason. Nobody remembered that a buyer of a pump usually needs a starter panel, or that a customer ordering paint nearly always forgets brushes and thinner. The follow-up order arrives a week later, from a competitor.
This capability reads your own order history and notes what tends to go together. It shows that beside the open quote, with the reason. Whether it suits this buyer is for the salesperson to say. The software offers nothing to the customer directly.
The problems this is meant to ease.
What AI deal optimisation does.
What this looks like in a working week.
The people who open this every day.
From first conversation to daily use.
Discovery on the deal process
We map your stages, quote steps, approval rules and the loss reasons staff enter. We also ask which offers your team would never want suggested.
Data readiness for orders and products
Past orders need proper line items, and the product list needs consistent names. Duplicates and vague loss notes are cleaned before any pairing is counted.
Trial run on actual order history
One product line and one team are loaded with their history. Suggested add-ons and next steps are compared with what the salespeople would have chosen themselves.
Staff judge the suggestions
Salespeople mark each suggestion as useful or poor. The sales head retires weak ones and approves the first bundles.
Going live on open quotes
Suggestions, briefs and the approvals tracker open on live deals for the trial team, then for the rest of sales.
Reviewing what was used
Accepted, refused and ignored suggestions are read alongside deal outcomes. Bundles, rules and loss categories are revised from that review.
How you would know it is working.
Record where you stand before it goes live, so the change is measured against your own figures.
- Of the deals that have closed, the portion that were won gives the win rate, measured against the CRM figures from before go-live.
- Average deal value is the mean order value on won deals, using the previous quarter’s orders as the baseline.
- Sales cycle duration is the number of days from first enquiry to order, measured from stage dates already stored in the CRM.
- Suggestions accepted, refused and ignored are counted per salesperson, with no earlier baseline since they begin at go-live.
- Approval requests are grouped by exception type and counted, with the discount approvals logged before go-live serving as the earlier figure.
Where a person stays in charge.
Suggestions are for staff only
Nothing reaches a customer until a salesperson puts it in a quote. The buyer never receives an automated offer.
Bundles need the sales head
A bundle cannot be quoted until the sales head approves its contents and price. Unpopular ones are retired.
Only your own records
Competitor notes are limited to what your staff entered, such as a price a customer mentioned. Nothing is collected from other websites or private sources.
Past orders cannot see a change in taste
Suggestions come from history and may miss a shift in what buyers want. A salesperson’s judgement on the account outranks the list.
It is one part of a system, not an island.
A module earns its place by what it passes to the next one. These are the connections we set up most often.
- Quote and proposal management
- Order management
- Product management
- Price lists and schemes
- Opportunity management
Products that include it.
How we would put it in.
Questions owners ask about AI deal optimisation.
What does it need before it works?
Orders recorded with proper line items and a product list that names things consistently. If the same part is typed three different ways, the pairings are lost and we tidy the list first.
Will customers see automated offers?
No. Suggestions appear only to your staff. Nothing reaches a customer until a salesperson puts it into a quote, which keeps the conversation human and keeps pushy offers out.
Does it track what competitors are charging?
Only what your own team records, such as a price a customer mentioned. We do not collect competitor prices from websites or other private sources.
Is a business with a short product list a fit?
Usually not. If you sell six items, the salesperson already knows what goes with what. The value starts with a catalogue long enough that nobody remembers every combination.
Can it push a bad bundle?
A bundle is only available once the sales head approves it, and unpopular ones are retired. The suggestions come from past orders, and past orders cannot see a change in what buyers now want.
Who approves discounts and special terms?
The people you name in your own rule. The tracker records the request, the approver and the decision, but it never grants an exception on its own.
How are the next-step suggestions chosen?
They come from the stage, the silence on the deal and what moved similar deals in your history. They can be wrong, so the salesperson treats them as a prompt.
What does it connect to, and what is the effort?
Quotes, orders, products and price lists. Email and call notes are added only if you connect those accounts. Most effort goes into cleaning product names and loss reasons.
Businesses that ask for this.
Modules that work with it.
Tell us how you handle AI deal optimisation today.
A spreadsheet, a register or another system: say which, and we will tell you plainly what is worth changing.
- No obligation
- A reply within one business day
- Your details stay private
Start with your requirements
Seven short steps that tell us what you need. An engineer reads them before getting in touch.
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