AI for insights and analytics
A customer running forty machines buys 600 dollars of parts per machine a year where fleets like it buy 2,400, and twelve of its machines came off warranty with no contract. AI analytics reads the installed base, parts sales and warranty files, and answers the aftermarket VP: where is parts revenue missing, and which units are unprotected.
These are the questions manufacturers ask. Each one maps to a measure our tools compute from your files. The AI model chooses the measure and explains the result; the arithmetic is done by our code, and every total is checked.
| The question | The measure behind it | What comes back |
|---|---|---|
| Which customers buy far fewer parts than their fleet should? | Aftermarket attach | Parts and service revenue per machine against the norm for fleet type and age, with the gap valued. |
| Which units came off warranty with no service contract? | Service contract renewal by fleet age | Units off warranty by customer and age, with contract status and the revenue at stake. |
| How complete is our installed base? | Installed base completeness | Units in the base against units shipped and units seen in parts orders, with the unmapped ones. |
| Which customers order parts we do not list for their machines? | Catalogue fit | Parts ordered against the catalogue for the customer's units, with the mismatches. |
| How is quote conversion, by count and value? | Quote conversion, count and value | Conversion by customer and product line, with the large lost quotes listed. |
| Are direct and distributor sales telling the same story? | Direct and distributor sales, combined | Revenue by end customer across both channels, with the customers only one channel sees. |
Every figure below was computed by a tool from the rows and checked before it was shown. The lines sum; the percentages match; each line opens to the rows that make it.
Where is aftermarket revenue missing against the fleet?
The gap between parts revenue and the fleet norm is $2.6m a year across 3 regions, at parts margins. Central region holds $1.3m, 50% of it.
| Central region | $1.3m | 50% of the total |
| Western region | $800k | 31% of the total |
| Southern region | $500k | 19% of the total |
| The 3 lines sum to | $2.6m | 0 unexplained |
Which central customers?
3 customers hold $980k of the $1.3m gap in the Central region, 75% of it. None has bought a machine in three years, so none is on an account manager's list.
| Ridgeway Aggregates | $480k | 40 machines; $600 per machine against $2,400 norm |
| Pennant Construction | $310k | 12 units off warranty, no contract |
| Marlow Quarries | $190k | orders parts from a distributor only |
| These 3 are | $980k | 75% of Central region |
Who owns these customers now?
Aftermarket sales, from this quarter. Each gets a named owner, a parts and contract conversation, and a place on the monthly attach review. The base gets cleaned so the next question finds more.
| Aftermarket sales | Assign owners and visit Ridgeway, Pennant and Marlow | This quarter |
| Service operations | Quote contracts on the 12 off-warranty units | This month |
| VP aftermarket | Review attach against norm by region monthly | Monthly |
Each measure has one formula, one source and one meaning. They are computed per account manager and channel and in total, and every one carries an identity that must hold before it is shown.
| Measure | Formula | From | What it tells you |
|---|---|---|---|
| Aftermarket attach | Parts and service revenue ÷ installed units, per customer, against the norm for unit type and age | Ledger; installed base register | Customers sourcing parts elsewhere |
| Installed base completeness | Units with confirmed owner, site and status ÷ units shipped and not retired | Installed base register | Whether aftermarket figures can be trusted |
| Service contract renewal by fleet age | Contracts renewed ÷ contracts due, by unit age band; units coming off warranty with no contract | Service contract file; register | Where renewals are lost and who should call |
| Quote conversion, count and value | Quotes matched to orders ÷ quotes decided | Quote log; order file | Customers who price-check; families that lose |
| Catalogue fit | Product families bought ÷ families relevant to the customer's application | Ledger; customer application data | Families a customer could buy and does not |
| Direct and distributor sales, combined | Direct sales + distributor sell-through, per end customer | Ledger; distributor point-of-sale reports | The end customer view without double counting |
| Warranty claims by customer and product | Claims and cost ÷ units under warranty, per customer and product | Warranty system; register | The installed base that costs the most |
| Customer concentration | Top ten end customers' share; largest share; by margin | Ledger and sell-through | Dependence on a few OEM or end customers |
| Price realisation | Invoiced price ÷ list or agreement price, by customer and family | Ledger; price file | Discounts that were not decided |
| Order intake against backlog | Orders booked ÷ revenue shipped; backlog in weeks, by family | Order book; ledger | Whether the factory is filling or emptying |
Each measure is worked through, with the export it comes from and what to drop, in Sales KPIs for industrial manufacturers.
From your question and the measures declared for industrial manufacturers, the model picks the one that answers it, and the period and comparison the question implies.
Deterministic code reads the rows, computes the measure, and checks the identities below. The same question on the same data gives the same answer, every time.
The AI model writes the sentence around the result, naming the account manager or account behind it. It states no figure that is not in the result, and every figure links to its rows.
| Installed base | Units shipped = active + retired + status unknown |
| Channels | End customer sales = direct + distributor sell-through; sell-in is excluded from this view |
| Service contracts | Due = renewed + lapsed + pending |
| Order book | Opening backlog + intake − shipments − cancellations = closing backlog |
The exports manufacturers already produce. Column names are mapped once and the mapping is reused. A file is the way in; scheduled delivery and connections to your systems come with the plan, and every source is listed here.
An answer is a list with an owner and a cadence, or it is a chart nobody works.
| Measures | Owner | Cadence |
|---|---|---|
| Units off warranty with no contract; quote conversion | Aftermarket sales; account managers | Monthly |
| Aftermarket attach; catalogue fit | Vice president of aftermarket; sales director | Quarterly |
| Installed base completeness; channel reconciliation | Sales operations with service | Quarterly |
| Warranty by customer; price realisation; intake and backlog | Commercial director; finance | Monthly to quarterly |
From your own installed base and parts sales: the median parts revenue per machine for fleets of the same machine type and age band. A forty-machine fleet at eight years old is compared with fleets like it, and the gap is valued at the customer's own fleet size. The peer set is shown.
The tool says how incomplete: units shipped against units in the base, and customers who order parts for machines the base does not list. Those are the first customers to clean up, because the gap on them is understated. Completeness is reported as a measure, not assumed.
Yes, when both files carry an end-customer field, or when a distributor's point-of-sale file can be mapped. Revenue by end customer is shown across channels, and customers seen by only one channel are listed. Where the end customer is unknown, the row says so.
The installed base by customer and unit, parts and service sales, warranty expiry by unit, service contracts, and the quote log. The parts catalogue by machine type adds catalogue fit. Every file is an ERP or service-system export.
Aftermarket attach · Catalogue fit · Quote conversion · Estate · Concentration · Price realisation
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Bring a few thousand rows. The data map opens next, every column mapped once, and the first question is answered in minutes. Free, in your browser, no account.