Industry profiles

Industry benchmarks, without the made‑up numbers.

You won’t find sector average scores here. A benchmark needs a large, comparable set of measured companies, and until one exists we won’t print a number. What we can say honestly: how each industry’s systems are usually put together, where their records tend not to agree, and what we would test first.

Get your Truth Score See the seven profiles

Sector profile · what’s inside7 sectors

Sector median scoreNot published
Pillar averages by industryNot published
“Top performers do X”Not published
The systems usually in playIncluded
Where records tend not to agreeIncluded
AI plays, and what each needsIncluded

No measured peer set yet, so no benchmark. Your own Truth Score™ is measured from your records.

Seven profiles

Where the data usually breaks, sector by sector.

Every system can look clean on its own; the gaps sit between them. Each profile lists the pairs of systems we compare first, and the data each common AI play depends on.

SaaS and technology

The product collects data by design, so records arrive structured. The risk is definitions: what counts as an active customer, a renewal or a churned account often differs between the CRM, billing and the product, and every pivot leaves another layer in the warehouse.

CRM
Subscription billing
Product analytics
Support desk
Data warehouse

Where we look first

CRM ↔ BillingAccounts active in the CRM with no live subscription
Billing ↔ ProductPaying customers with no usage, or usage with no paying account
CRM ↔ SupportTickets that can’t be tied to a customer or contract

AI plays usually proposed

Customer churn predictionOne customer ID across CRM, billing and product
Lead scoringClean contacts and a reliable win or loss history
Revenue forecastingClosed-won deals that match invoices

The first questionDo the CRM, billing and product agree on how many customers you have today?

SaaS in depth

Financial services

Regulation enforces discipline inside each system, so records tend to be maintained. The gaps sit between the core platform, the CRM and the ledger, which were usually bought at different times and joined later.

Core platform or policy system
CRM
General ledger
Payments
Compliance and onboarding records

Where we look first

Core platform ↔ CRMCustomers on the platform with no relationship record, or the reverse
Payments ↔ LedgerTransactions that don’t trace to a ledger entry
Onboarding ↔ CRMCustomer details that disagree between systems

AI plays usually proposed

Fraud detectionComplete, consistent transaction history
Customer service automationContacts tied to accounts and products
Document intelligenceAssessed with your team, not from exports alone

The first questionCan every transaction in the payments data be traced to the ledger and to a known customer?

Financial services in depth

Healthcare

Many sites, many systems, often joined by acquisition. Each location can run cleanly while the group view depends on matching patients, payers, clinicians and sites across systems that were never designed to share IDs. We ask for operational and financial exports, not clinical records.

Practice or patient management system
Billing and claims
Scheduling
HR and rostering
Finance

Where we look first

Scheduling ↔ BillingAppointments delivered with no matching claim or invoice
HR ↔ RosteringClinicians rostered who are not in HR, or have left
Sites ↔ FinanceSite figures that don’t add up to the group ledger

AI plays usually proposed

Demand forecastingAppointment history without gaps or duplicates
Process automationA process recorded the same way at every site
13-week cash flowClaims, payments and terms that reconcile

The first questionDoes every appointment delivered have a claim or invoice you can find?

Healthcare in depth

Professional services

The business runs on people and hours, and the records of both are scattered: hours in a timesheet tool, rates in a spreadsheet, clients in the CRM, invoices in finance. Knowledge about how a number was produced often lives with the person who produced it.

CRM
Practice or project management
Timesheets
Billing and finance
HR

Where we look first

Projects ↔ FinanceBillable hours with no matching invoice
HR ↔ TimesheetsTime booked by people who have left, or never joined
CRM ↔ BillingClients invoiced under names the CRM doesn’t recognise

AI plays usually proposed

Revenue forecastingPipeline, hours and invoices that agree
Process automationEngagements recorded the same way every time
Customer churn predictionOne client ID across CRM, projects and billing

The first questionCan you show, for last quarter, every billable hour and the invoice it ended up on?

Professional services in depth

Retail and e-commerce

Transactions are captured precisely at the till and the checkout. Customers and products are not: the same shopper appears in the web store, the loyalty scheme and the email tool under different identities, and product codes drift between channels and the ERP.

E-commerce platform
Point of sale
Marketplaces
Email and loyalty
Inventory and ERP

Where we look first

E-commerce ↔ LoyaltyOne shopper held as several customers
Channels ↔ ERPProduct codes and prices that differ by channel
Orders ↔ InventoryStock movements that don’t match orders

AI plays usually proposed

PersonalisationOne view of each customer across channels
Demand forecastingOrder history without gaps or duplicates
Pricing and rebate optimisationPrices that match the ledger

The first questionHow many customers do you have, and does every system give the same answer?

Retail in depth

Manufacturing

Operational data on the line is usually measured carefully. Commercial data is not: customer and distributor records split between the ERP and the CRM, price lists and rebates in spreadsheets, and plant systems that don’t share IDs with the enterprise.

ERP
CRM or distributor portal
Production and quality systems
Pricing spreadsheets
Finance

Where we look first

CRM ↔ ERPCustomers and distributors held under different names or IDs
Spreadsheets ↔ ERPPrice lists and rebates the ERP doesn’t hold
Orders ↔ InvoicesShipped orders that can’t be traced to an invoice

AI plays usually proposed

Demand forecastingOrder history without gaps or duplicates
Pricing and rebate optimisationPrices and rebates held in one system
Process automationOrders recorded the same way at every plant

The first questionWhere do your price lists and rebate terms actually live?

Manufacturing in depth

Logistics and distribution

High volumes, many parties and many handoffs. Each job passes through the operations system, carrier portals and finance, and every handoff is a chance for a shipment, a charge or a customer to be recorded twice, or not at all.

Transport or warehouse management
ERP and finance
CRM
Carrier and customer portals
Spreadsheets

Where we look first

Operations ↔ FinanceJobs delivered with no matching invoice or charge
CRM ↔ ERPCustomer accounts that disagree on terms or rates
Portals ↔ OperationsCarrier records that can’t be tied to a job

AI plays usually proposed

Demand forecastingJob history without gaps or duplicates
13-week cash flowInvoices, payments and terms that reconcile
Customer service automationQueries tied to jobs and customers

The first questionDoes every delivered job have a charge you can find in finance?

Logistics in depth

Profiles describe typical system set-ups and where we look first. They are not findings about any company or sector.

Why there is no league table

A number from too few companies is a guess with a decimal point.

Small samples mislead
A median needs a crowd.
A sector average from a handful of companies moves with every new one. Quoting it to a board would give it a weight it hasn’t earned.
Systems, not sectors
The estate decides more than the industry.
Two distributors on different ERPs, joined by acquisition or not, can sit far apart. The profiles show the set-ups; your exports show yours.
Your decision
Only your number matters for it.
A proposal on your desk depends on your records, not the sector’s. A Truth Score Read measures them, within 5 working days of complete exports.

Questions

About these profiles.

Why there are no sector averages, where the profiles come from, and how to get a number for your own company.

Book the call

Why don’t you publish average Truth Scores by industry?

A benchmark needs a large set of companies, measured the same way, that are genuinely comparable. We don’t have that yet, and a median from a handful of companies would be a guess presented as a fact. We would rather publish nothing than a number you might repeat to your board.

Are these profiles based on your client work?

No. They describe how the systems in each industry are typically put together and where records commonly fail to agree, which is where we look first. They are a starting point for the first call, not findings about any company.

Does industry decide how ready a company is?

Less than the sector label suggests. Two companies in the same sector can be far apart, because what matters is the systems they run, how they were joined, and who looks after the data. Your own number is the only one that matters for your decision.

How do I get a real number for my company?

A Truth Score Read takes exports from two or more of your systems, matches the customers, people, deals and invoices between them, and returns a signed memo with your Truth Score (a score out of 100 for how far your data can be trusted), a Leverability read on the AI in question (which AI ideas you can use now, after fixes, or not at all) and the red flags between systems, within 5 working days of complete exports. See a real sample memo.

Skip the sector average. Get your own number.

Exports from two or more systems, matched between them, every red flag checked by hand. Your Truth Score and the gaps between your systems in a signed memo, within 5 working days of complete exports.

Get your Truth Score See a real sample memo

Starts with a 30-minute call. Commits you to nothing.