You know your tonnage to the kilo. Your margin per account is an estimate.
ZopaAI reads your ERP, quote log and contract book, links them to 230,000+ external sources, and re-scores every account weekly on base price, pass-through, freight and risk.
Steel
Margin per account, as visible as tonnage
Steel sales teams work with a lot of precise data on volumes, grades and orders. The margin per account is often harder to see, because it depends on surcharge formulas, index lags, freight and payment terms that change over the life of a contract. ZopaAI brings together invoice lines, quote logs and contract books with external data on input costs, freight, market prices and customer risk, and rescores every account weekly on realised margin, pass-through and risk. Base price, surcharge and contribution are shown separately, so gaps become visible per contract. Your team goes into each contract round with a clear view of where the margin sits.
ZopaAI stands for reliable, scientifically verified AI for use in companies, in order to avoid the problem of AI hallucinations.
Newspaper, Frankfurt
Eight building blocks, usable on their own, together a steering system
All blocks run on the same data foundation and the same score logic. You can start with one of them without having to rebuild the others later.
Pick your role, the modules used day to day in that role are highlighted.
Account scoring and next best action
One score per account, recomputed weekly, from realised margin, surcharge pass-through, volume stability, payment behaviour and strategic fit. Next to the score sits the movement since last week and the component that caused it, and one action: reopen the contract, hold, shift to inside sales, secure volume, reduce exposure, or do nothing. Each action is shown with its expected effect and the condition that would change it. Before go-live the model is run over your last two contract rounds, so you can compare its recommendations against what your team actually decided.
- Pass-through gap shown per contract, separate from base price
- Weekly movement shown with the component that caused it
- Run over your last two contract rounds before it is used forwards
“Account 4711 moved down 9 points. Driver: pass-through gap on two contracts widened after the last index step. Payment behaviour unchanged. Recommended: prepare for the November renewal, no action before then.”
Illustrative wording. Figures and names are not from a real client project.
Price and quote guidance
ZopaAI gives a price corridor, derived from what comparable grades and dimensions actually realised, adjusted for the current surcharge position, freight and incoterm. Alongside it sits the contribution per hour of the constraining unit, so an order that fills a rolling hour cheaply is visible as such. The corridor is shown with the quotes it was derived from, including the ones that were lost and at what price.
- Upper and lower figure per quote, derived from comparable realised business
- Base price and surcharge position held separately, never as one number
- Contribution per hour of the constraining unit shown next to the price
“Quote 8841, 320 t, spring steel: corridor 40 euros per tonne wide, based on 14 comparable quotes over eight months. Current offer sits at the lower edge. Week 38 is already committed at below-median contribution per hour, so lead time rather than price is the constraint here.”
Illustrative wording. Figures and names are not from a real client project.
Contract round preparation
Frameworks are valued at signature and then not again. ZopaAI recalculates the remaining term at today's input costs and checks the surcharge formula for lag, cap and floor. Set against the account's alternatives, meaning competitor presence in that grade, import pressure and lead times, that gives an opening position with its evidence and a walk-away volume fixed before the meeting.
- Remaining term valued at today's input costs, not at signature
- Surcharge formula checked for lag, cap and floor before it is signed again
- Walk-away volume quantified per account before the negotiation starts
“Account 4711, renewal in week 44: the current formula lags the scrap index by one month, costing around 12 euros per tonne in a rising market. Opening position: shorten the lag to two weeks, hold base price. Walk-away volume 900 t, replaceable from the order book within six weeks.”
Illustrative wording. Figures and names are not from a real client project.
Volume and call-off planning
A rolling forecast per account of what will actually be called off, set against the committed volume. It is built from call-off history, the customer's own order situation and the end market, updated weekly, and shown with the change since the previous week. A gap between commitment and forecast is therefore visible from the second quarter, not at year end.
- Rolling call-off forecast per account, against the committed volume
- Weekly change shown, so a drift is visible while it can still be acted on
- Aggregated by plant and grade, so the production side sees the same figure
“Account 4711: 12,000 t committed, 9,400 t forecast at year end. The gap opened over the last two quarters, driven by the customer's own order intake. Recommended: address at the November review, before the renewal discussion assumes last year's volume.”
Illustrative wording. Figures and names are not from a real client project.
Early risk detection
Payment behaviour, filings, capacity announcements and a customer's own call-off pattern change weeks before receivables do, so a credit limit is usually cut when the tonnage is already in the order book. ZopaAI monitors accounts and the groups behind them for these signals, aggregates exposure to group level rather than per legal entity, and shows where the book is concentrated: one group, one end market, one destination, one quota regime.
- Deterioration signals from payment behaviour, filings and capacity announcements
- Exposure aggregated to group level, not per legal entity
- Concentration shown by end market, destination and quota regime
“Group 3120, four legal entities: payment delay up 11 days over two quarters, one plant closure announced at its largest customer. Open exposure 4,200 t across the next two quarters. Recommended: cap open volume, shorten terms at the next order.”
Illustrative wording. Figures and names are not from a real client project.
Contact and visit steering
A contact frequency and a channel per account, derived from expected effect per contact hour with travel time included, instead of an A/B/C class set once a year from tonnage. Each processor, distributor or industrial customer is assigned to a personal visit, to inside sales or to digital outreach. Coverage is also shown per territory, which surfaces groups covered by two representatives and accounts with no assigned owner.
- Contact frequency per account instead of rigid A/B/C classes
- Channel assignment: visit, inside sales or digital outreach
- Duplicate coverage inside groups and unassigned accounts made visible
“Region West: 22 of 145 processors carry 70 % of the expected effect. Recommended: 8 monthly, 14 quarterly, the rest through inside sales. Three accounts visited monthly show no measurable response after four cycles.”
Illustrative wording. Figures and names are not from a real client project.
Competitive analysis
Which competitor supplies which grade into which region, from trade flows, capacity announcements and your own quote outcomes.
- Competitor presence by grade, region and destination market
- Import pressure tracked through customs and trade flow data
“Bearing steel, northern Italy: 4 of 11 losses were on lead time, not price. Average gap 9 days. Price concessions in this segment did not change the win rate.”
Illustrative wording. Figures and names are not from a real client project.
New customer discovery
Candidates matched on grade, dimension and application profile against what you already produce profitably: processors, forges, foundries and distributors. Group structures are resolved, so subsidiaries of accounts you already supply appear as well. Existing qualifications are part of the match, with an estimate of the approval effort where one is missing.
- Matched on grade and dimension profile, not on industry code
- Group structures resolved, so subsidiaries of existing customers surface
- Existing qualifications carried into the match, approval effort estimated where missing
“17 candidates with a matching grade profile. 6 are subsidiaries of groups you already supply. 9 existing accounts run an application already approved for your special grade at a sister plant.”
Illustrative wording. Figures and names are not from a real client project.
What changed at a steel producer in 12 months.
Scope of the case study
- Client
- Steel producer, long and flat products (anonymised)
- Territory
- Europearound 900 active accounts26 sales staff
- Implemented
- Margin and pass-through analysis, price guidance, early risk detection, contract preparation
- Period
- 12 months, two contract rounds, measured against control regions
Full case study as a PDF
Two pages on initial situation, data foundation, methodology, results. Get access to the link.
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Presented anonymised. Figures from the client's own project controlling.
We used to negotiate on tonnage. Now we negotiate on where the margin actually is, and we know which accounts to keep and which ones the market will decide for us.
Reference from an ongoing project in steel
Questions we hear first
Short answers before your first call.
Scrap, alloy and energy surcharges are modelled per product line exactly as they are contractually agreed, including index reference, lag, cap and floor. Input costs and freight are refreshed at least weekly, and contract exposure is recomputed on every quote and every order-book update. Base price, surcharge and realised contribution are shown separately, so a pass-through gap becomes visible per contract instead of disappearing into an invoice total.
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