Every key a library formula or an initiative template can read, grouped by the domain its key opens with. A key with a Fed by entry arrives from a connected system, or from a query on your own data warehouse where none of those is connected. The rest are recorded by a person, imported, read by a warehouse query, or produced by a formula. See Metrics from your own data warehouse.

What the key's shape means, and where a metric's before-value comes from, is in Metric keys, and where a value comes from.

CRM

Key	What it measures	Type	Unit	Fed by
crm.contacts.lead_to_mql_rate	Percentage of new contacts that became Marketing Qualified Leads.	percentage	%	HubSpot
crm.contacts.mql_count	Contacts that reached MQL (Marketing Qualified Lead) status in the period.	count	leads	HubSpot
crm.contacts.mql_to_sql_rate	Percentage of the period's marketing-qualified contacts that also became sales-qualified.	percentage	%	HubSpot
crm.contacts.new_count	New contacts created in the CRM in the period.	count	contacts	HubSpot
crm.contacts.sql_count	Contacts that reached SQL (Sales Qualified Lead) status in the period.	count	leads	HubSpot
crm.contracts.avg_cycle_days	Average days from contract request to signature.	duration	days	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
crm.contracts.hours_per_contract	Combined legal, sales and operations hours to take one contract from request to signature.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
crm.contracts.processed_count	Contracts drafted, redlined and executed in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
crm.deals.at_risk_share	Percentage of open pipeline deals slipping or forecast to be lost. Watch out for Decide once what makes a deal at risk, such as a slipped close date or a forecast of lost, and keep it the same before and after. It moves with the market as well as the AI, so a formula valuing its change takes only the share of it you credit to the initiative. Good for Valuing pipeline forecasting by the at-risk deals it helps recover. Read the guide	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
crm.deals.avg_demo_to_proposal_days	Average days from demo to proposal sent. Watch out for It's recorded, not synced. Decide when the clock starts and stops, and keep that the same before and after. A faster proposal brings revenue sooner, not more of it: it's valued at your cost of capital. Good for Valuing proposal and quote automation. Read the guide	duration	days	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
crm.deals.avg_sales_cycle_days	Average days from deal creation to close across the deals won in the period. Watch out for It's measured on the deals won in the period, so a batch of old deals closing at once lengthens it. A shorter cycle brings revenue sooner, not more of it: formulas value the days at your cost of capital, and only the share of the change you credit to the initiative. Good for Valuing deals closing sooner after sales coaching or call intelligence. Read the guide	duration	days	HubSpot, Pipedrive, Salesforce
crm.deals.avg_value	Average closed-won deal size in the period.	currency	USD	HubSpot, Pipedrive, Salesforce
crm.deals.lost_count	Number of deals/opportunities closed-lost in the period. Watch out for A pipeline cleanup that closes stale deals as lost in one go raises the count, and drops that month's win rate, without anything happening to sales. Good for With deals won, the decided deals a change in win rate is applied to. Read the guide	count	deals	HubSpot, Pipedrive, Salesforce
crm.deals.pipeline_value	Value of the deals open in the period, taken as a snapshot when the CRM syncs. Watch out for A snapshot of the deals open when the CRM synced, not a total for the month. It's the amounts reps entered, which run optimistic, and it includes deals that will never close. Good for Sizing what slipping deals put at risk, with win rate and sales cycle. Read the guide	currency	USD	HubSpot, Pipedrive, Salesforce
crm.deals.win_rate	Percentage of closed deals that were won. won_count / (won_count + lost_count). Watch out for It moves with the market, pricing and the mix of your pipeline, so a formula valuing its change takes only the share of it you credit to the initiative. Only closed deals count. Deals left open don't lower it, so it can rise while fewer deals close. Good for Valuing sales coaching or deal intelligence by the change in deals won. Turning more qualified leads into an expected number of won deals. Read the guide	percentage	%	HubSpot, Pipedrive, Salesforce
crm.deals.won_count	Number of deals/opportunities closed-won in the period. Watch out for A deal counts in the month it closes, so a quarter-end push makes some months lumpy. Compare like periods. A count says nothing about deal size. Read it beside the value of deals won. Good for The deals a change in conversion, cycle time or proposal speed is multiplied by. Read the guide	count	deals	HubSpot, Pipedrive, Salesforce
crm.deals.won_value	Total value of all closed-won deals in the period, in the deals' currency when they share one and in US dollars at the European Central Bank's rate when they mix. Watch out for It's revenue, not return: a formula books what you keep of it at your contribution margin, never the full amount. A month whose deals mix currencies is converted to US dollars at the European Central Bank's rate, so its total moves with exchange rates. Good for With the count of deals won, your average deal value. Read the guide	currency	USD	HubSpot, Pipedrive, Salesforce
crm.leads.avg_lead_to_won_days	Average days from lead creation to closed-won.	duration	days	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
crm.leads.avg_qualify_minutes	Average staff minutes to qualify one inbound lead.	duration	minutes	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
crm.leads.qualification_rate	Percentage of scored leads that pass the qualification threshold.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
crm.leads.qualified_count	Leads that met the qualification threshold and were routed to a rep.	count	leads	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
crm.leads.scored_count	Inbound leads scored in the period.	count	leads	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
crm.leads.sql_rate	Sales-qualified leads in the period as a percentage of the new leads created in it.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
crm.proposals.hours_per_proposal	Staff hours to assemble and send one proposal.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
crm.proposals.sent_count	Proposals sent to prospects in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
crm.reps.admin_hours_per_week	Sales rep hours per week on administrative work rather than selling.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)

Finance

Key	What it measures	Type	Unit	Fed by
finance.ap.data_entry_minutes_per_invoice	Staff minutes to key the data from one supplier invoice.	duration	minutes	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
finance.ap.days_payable_outstanding	Average days between invoice receipt and payment.	duration	days	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
finance.ap.invoices_processed	Supplier invoices processed through the extraction pipeline in the period.	count	invoices	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
finance.ap.minutes_per_invoice	Staff minutes to process one supplier invoice end to end.	duration	minutes	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
finance.bills.count	Total vendor bills received in the period.	count	bills	NetSuite, QuickBooks, Xero
finance.bills.error_rate	Percentage of vendor bills entered with an error that needed correcting — amount, coding, vendor or a duplicate.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
finance.bills.minutes_per_error	Staff minutes to catch, investigate and correct one vendor bill error, vendor follow-up included, from a time study or your own timing.	duration	minutes	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
finance.bills.volume	Total value of all vendor bills received in the period.	currency	USD	NetSuite, QuickBooks, Xero
finance.close.cycle_days	Calendar days from period end to final sign-off of the close.	duration	days	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
finance.disputes.avg_resolution_days	Average days to resolve a billing dispute.	duration	days	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
finance.invoices.count	Total outbound invoices created in the period.	count	invoices	NetSuite, QuickBooks, Xero
finance.invoices.error_rate	Percentage of invoices issued with an error requiring correction.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
finance.invoices.minutes_per_dispute	Finance staff minutes to resolve one billing dispute, customer communication and credit memo processing included, from a time study or your own timing.	duration	minutes	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
finance.invoices.paid_count	Number of invoices marked paid in the period.	count	invoices	NetSuite, QuickBooks, Xero
finance.invoices.volume	Total billed on invoices created in the period, tax included.	currency	USD	NetSuite, QuickBooks, Xero
finance.pnl.expenses	Total operating expenses for the period from the accounting system.	currency	USD	NetSuite, QuickBooks, Xero
finance.pnl.net_income	Net income (revenue minus expenses) for the period.	currency	USD	NetSuite, QuickBooks, Xero
finance.pnl.revenue	Total revenue recognized in the period from the accounting system. Watch out for Recognized revenue from your books: it's only complete once the month is closed, so a month still open reads low. It moves with everything the business does. Roiva's formulas use it as the base a rate's change is applied to, not as the AI's result. A formula valuing its change takes only the share of it you credit to the initiative. Good for The base a change in stockouts, markdowns, shrink or overruns is applied to. Read the guide	currency	USD	NetSuite, QuickBooks, Xero
finance.reconciliation.hours	Staff hours on manual reconciliations per close cycle.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
finance.revenue_recognition.adjustment_count	Revenue recognition adjustments or corrections required per period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
finance.revenue_recognition.manual_hours	Staff hours per period building revenue recognition schedules by hand.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
finance.revenue_recognition.minutes_per_adjustment	Accounting staff minutes to identify, correct, document and sign off one revenue recognition adjustment, from a time study or your own timing.	duration	minutes	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
finance.treasury.avg_cash_balance	Average daily cash balance in the period.	currency	USD	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
finance.treasury.manual_hours_per_cycle	Staff hours to build and update the cash flow model per forecast cycle.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)

HubSpot

Key	What it measures	Type	Unit	Fed by
hubspot.contacts.lead_to_mql_rate	HubSpot's lead-to-MQL rate for the month. Written to crm.contacts.lead_to_mql_rate instead when this connection is the primary source.	percentage	%	HubSpot
hubspot.contacts.mql_count	HubSpot's marketing qualified leads for the month. Written to crm.contacts.mql_count instead when this connection is the primary source.	count	leads	HubSpot
hubspot.contacts.mql_to_sql_rate	HubSpot's MQL-to-SQL rate for the month. Written to crm.contacts.mql_to_sql_rate instead when this connection is the primary source.	percentage	%	HubSpot
hubspot.contacts.new_count	HubSpot's new contacts for the month. Written to crm.contacts.new_count instead when this connection is the primary source.	count	contacts	HubSpot
hubspot.contacts.sql_count	HubSpot's sales qualified leads for the month. Written to crm.contacts.sql_count instead when this connection is the primary source.	count	leads	HubSpot
hubspot.deals.avg_sales_cycle_days	HubSpot's average sales cycle for the month. Written to crm.deals.avg_sales_cycle_days instead when this connection is the primary source.	duration	days	HubSpot
hubspot.deals.avg_value	HubSpot's average won deal size for the month. Written to crm.deals.avg_value instead when this connection is the primary source.	currency	USD	HubSpot
hubspot.deals.lost_count	HubSpot's lost deals for the month. Written to crm.deals.lost_count instead when this connection is the primary source.	count	deals	HubSpot
hubspot.deals.open_count	Deals open in HubSpot at sync time, a snapshot rather than a period total, written once per month and rewritten on every sync.	count	—	HubSpot
hubspot.deals.open_value	Value of the deals open in HubSpot at sync time, a snapshot rather than a period total, written once per month and rewritten on every sync.	currency	USD	HubSpot
hubspot.deals.win_rate	HubSpot's deal win rate for the month. Written to crm.deals.win_rate instead when this connection is the primary source.	percentage	%	HubSpot
hubspot.deals.won_count	HubSpot's won deals for the month. Written to crm.deals.won_count instead when this connection is the primary source.	count	deals	HubSpot
hubspot.deals.won_value	HubSpot's won deal value for the month. Written to crm.deals.won_value instead when this connection is the primary source.	currency	USD	HubSpot
hubspot.marketing.campaigns_launched	HubSpot's campaigns launched for the month. Written to marketing.campaigns.launched_count instead when this connection is the primary source.	count	campaigns	HubSpot
hubspot.marketing.email_open_rate	HubSpot's marketing email open rate for the month. Written to marketing.emails.open_rate instead when this connection is the primary source.	percentage	%	HubSpot
hubspot.marketing.organic_sessions	HubSpot's organic search sessions for the month. Written to marketing.web.organic_sessions instead when this connection is the primary source.	count	sessions	HubSpot
hubspot.marketing.posts_published	HubSpot's content published for the month. Written to marketing.content.published_count instead when this connection is the primary source.	count	posts	HubSpot
hubspot.tickets.avg_resolution_days	HubSpot's average ticket resolution time for the month. Written to support.tickets.avg_resolution_days instead when this connection is the primary source.	duration	days	HubSpot
hubspot.tickets.closed_count	HubSpot's support tickets closed for the month. Written to support.tickets.closed_count instead when this connection is the primary source.	count	tickets	HubSpot
hubspot.tickets.new_count	HubSpot's support tickets opened for the month. Written to support.tickets.volume instead when this connection is the primary source.	count	tickets	HubSpot
hubspot.tickets.open_count	Tickets open in HubSpot at sync time, a snapshot rather than a period total, written once per month and rewritten on every sync.	count	—	HubSpot
hubspot.tickets.resolution_rate	HubSpot's ticket resolution rate for the month. Written to support.tickets.resolution_rate instead when this connection is the primary source.	percentage	%	HubSpot

Professional services

Key	What it measures	Type	Unit	Fed by
professional_services.billing.realization_rate	Billed fees as a percentage of standard-rate value of hours worked.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
professional_services.billing.unbilled_hours_per_practitioner_weekly	Billable hours worked but not captured per practitioner per week.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
professional_services.knowledge.active_users	Practitioners actively using the knowledge system in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
professional_services.knowledge.search_minutes	Average minutes per search for prior work and precedent.	duration	minutes	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
professional_services.knowledge.searches_per_practitioner	Times per period a practitioner searches for prior work, methods or precedent.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
professional_services.practitioners.available_hours	Working hours available per practitioner in the period.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
professional_services.practitioners.billable_count	Billable practitioners in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
professional_services.practitioners.billable_hours_worked	Hours worked by billable practitioners in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
professional_services.projects.at_risk_detection_lead_weeks	Average weeks before delivery failure that an at-risk project is identified.	duration	weeks	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
professional_services.projects.at_risk_identified_count	At-risk projects identified in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
professional_services.projects.avg_overrun_depth	Of projects closed in the period that exceeded the approved budget, their cost over budget as a percentage of their contract value.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
professional_services.projects.budget_overrun_rate	Percentage of projects that exceeded the approved budget.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
professional_services.projects.pm_count_using_automation	Project managers using the automated reporting system.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
professional_services.projects.recovery_hours_per_project	Senior PM hours spent recovering a late-detected at-risk project.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
professional_services.proposals.prep_hours	Staff hours to assemble one proposal.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
professional_services.proposals.submitted_count	Proposals or statement-of-work responses submitted in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
professional_services.reporting.client_reports_delivered	Client status reports delivered in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
professional_services.reporting.hours_per_client_report	Staff hours to compile, format and deliver one client report.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
professional_services.reporting.reports_per_pm	Client reports each project manager prepares in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
professional_services.utilization.bench_share	Percentage of available practitioner hours spent on the bench.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
professional_services.utilization.billable_rate	Billable hours as a percentage of available practitioner hours.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)

Construction

Key	What it measures	Type	Unit	Fed by
construction.bids.awarded_value	Contract value of projects awarded in the period.	currency	USD	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
construction.bids.submitted_count	Bids submitted in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
construction.documents.search_hours_per_week	Staff hours per week locating current drawing versions and document history.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
construction.estimating.hours_per_bid	Estimator hours per bid.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
construction.estimating.variance_rate	Average estimate-to-actual variance as a percentage of the estimate.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
construction.job_costing.review_cycle_days	Average days between formal job cost reviews on active projects.	duration	days	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
construction.job_costing.review_cycles	Formal job cost review cycles in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
construction.job_costing.review_hours_per_project	PM and controller hours per project per job cost review cycle.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
construction.labor.hours_worked	Field labor hours worked in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
construction.payroll.minutes_per_timecard_correction	Payroll admin minutes to find and correct one timecard error, from a time study or your own timing.	duration	minutes	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
construction.payroll.processing_hours_per_week	Staff hours per week collecting, reconciling and processing timecards.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
construction.payroll.timecard_error_rate	Percentage of timecards requiring correction.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
construction.payroll.timecards_processed	Timecards processed in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
construction.projects.active_count	Active projects in the portfolio during the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
construction.projects.avg_overrun_depth	Of projects closed in the period that exceeded the approved budget, their cost over budget as a percentage of their contract value.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
construction.projects.cost_overrun_rate	Percentage of projects that exceed the approved budget.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
construction.rfis.response_days	Average calendar days from RFI submission to formal response.	duration	days	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
construction.rfis.submitted_count	Requests for information submitted in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
construction.safety.observation_hours_per_week	Staff hours per week on safety observation reporting and job hazard analysis distribution.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
construction.safety.recordable_incident_rate	OSHA recordable incident rate (incidents × 200,000 ÷ hours worked).	ratio	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)

Financial services

Key	What it measures	Type	Unit	Fed by
financial_services.advisors.accounts_per_advisor	Client accounts managed per advisor.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
financial_services.advisors.aum_per_advisor	Average assets under management per advisor.	currency	USD	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
financial_services.advisors.count	Advisors using the client intelligence platform.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
financial_services.advisors.organic_growth_rate	Net new assets in the period, inflows less outflows with market movement left out, annualized, as a percentage of assets under management at the period's start, from your custodian or portfolio system.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
financial_services.advisors.proactive_outreach_rate	Percentage of clients contacted proactively per quarter.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
financial_services.fraud.false_positive_rate	Percentage of flagged transactions that were legitimate.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
financial_services.fraud.flagged_count	Transactions flagged by the fraud system in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
financial_services.fraud.loss_rate_bps	Fraud losses as basis points of transaction volume.	count	bps	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
financial_services.fraud.minutes_per_investigation	Analyst minutes to investigate and resolve one flagged transaction, customer contact included, from a time study or your case system's time tracking.	duration	minutes	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
financial_services.kyc.review_hours_per_account	Staff hours to review and approve one account.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
financial_services.onboarding.accounts_count	New accounts onboarded through KYC/AML in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
financial_services.onboarding.days_to_active	Average days from application to active account.	duration	days	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
financial_services.regulatory_reporting.error_rate	Percentage of regulatory report line items requiring correction before submission.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
financial_services.regulatory_reporting.line_items_per_cycle	Line items across the regulatory reports filed in one reporting cycle.	average	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
financial_services.regulatory_reporting.prep_hours_per_report	Staff hours to prepare and validate one regulatory report.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
financial_services.transactions.volume	Total transaction volume processed in the period.	currency	USD	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
financial_services.underwriting.decision_hours	Average hours from application submission to decision.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
financial_services.underwriting.manual_review_rate	Percentage of applications requiring manual underwriter review.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
financial_services.underwriting.overturned_decline_rate	Of past applications the AI would decide without manual review, the percentage it approves that the old process declined, from a back-test against known decisions.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
financial_services.underwriting.review_hours_per_application	Average underwriter and analyst hours of hands-on review per application decided, an automatically decided one counting as none — not the elapsed time to a decision.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)

Healthcare

Key	What it measures	Type	Unit	Fed by
healthcare.appointments.avg_length	Scheduled minutes of a patient visit of the type recovered clinician time would be booked as.	duration	minutes	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
healthcare.appointments.no_show_rate	Percentage of scheduled appointments the patient did not attend.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
healthcare.appointments.scheduled_count	Appointments scheduled in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
healthcare.audits.prep_hours_per_cycle	Staff hours preparing documentation for one quality audit or regulatory review.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
healthcare.claims.denial_rate	Percentage of submitted claims denied on first submission.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
healthcare.claims.denied_count	Claims denied on first submission in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
healthcare.coding.charts_coded	Charts coded in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
healthcare.coding.charts_per_coder_per_day	Charts coded per medical coder per day.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
healthcare.coding.coder_count	Medical coders using AI-assisted coding.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
healthcare.coding.error_rate	Percentage of coded charts with an error.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
healthcare.denials.appeal_success_rate	Share of appealed denials overturned.	ratio	0–1	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
healthcare.documentation.after_hours_charting_hours_per_week	Hours per week clinicians spend on documentation outside clinical hours.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
healthcare.documentation.clinician_count	Clinicians using ambient documentation.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
healthcare.documentation.encounters_per_clinician_per_day	Patient encounters per clinician per clinical day.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
healthcare.documentation.minutes_per_encounter	Clinician documentation minutes per patient encounter.	duration	minutes	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
healthcare.outreach.hours_per_week	Staff hours per week on appointment reminders, care gap calls and other patient outreach done by hand.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
healthcare.quality.care_gap_closure_rate	Percentage of identified care gaps closed.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
healthcare.quality.value_based_revenue_at_risk	Revenue at risk or bonus-eligible under value-based contracts, for a performance year.	currency	USD	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
healthcare.revenue_cycle.days_in_ar	Average days from claim submission to payment.	duration	days	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)

HR

Key	What it measures	Type	Unit	Fed by
hr.hiring.days_to_first_interview	Average days from job posting to first interview.	duration	days	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
hr.hiring.hires_count	Positions filled in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
hr.hiring.recruiter_hours_per_hire	Recruiter hours on resume review and screening per hire.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
hr.inquiries.avg_response_hours	Average hours from employee inquiry to HR response for routine questions.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
hr.inquiries.chatbot_deflection_rate	Share of employee inquiries fully resolved by the chatbot without HR involvement.	ratio	0–1	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
hr.inquiries.minutes_per_inquiry	HR staff minutes to handle one tier-1 inquiry by hand, answer, follow-up and documentation included, from a time study or your case tool's time tracking.	duration	minutes	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
hr.inquiries.tier1_per_month	Routine HR inquiries received per month (policy, PTO, benefits, payroll).	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
hr.onboarding.admin_hours_per_hire	HR and IT staff hours on onboarding tasks per new hire.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
hr.onboarding.completion_rate	Percentage of required onboarding tasks completed within the target window.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
hr.onboarding.days_to_productivity	Days from start date until a new hire is independently productive.	duration	days	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
hr.onboarding.hires_count	New employees who completed onboarding in the period.	count	hires	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
hr.onboarding.tasks_automated	Onboarding checklist items (provisioning, welcome communications, acknowledgments) now handled automatically.	count	tasks	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
hr.reporting.hours_per_month	Staff hours per month building workforce reports.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
hr.reviews.admin_hours_per_cycle	HR and manager hours coordinating, collecting and calibrating one performance review cycle.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
hr.reviews.admin_hours_per_employee	HR and manager admin hours per employee per review cycle.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
hr.reviews.completion_rate	Percentage of required performance reviews completed on time.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
hr.reviews.employees_reviewed	Employees included in the performance review cycle.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
hr.workforce.headcount	Total employee headcount in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
hr.workforce.voluntary_attrition_rate	Annualized voluntary attrition rate.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)

Supply chain

Key	What it measures	Type	Unit	Fed by
supply_chain.deliveries.on_time_rate	Percentage of orders delivered on or before the promised date.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
supply_chain.deliveries.revenue_at_risk	Revenue exposed to late deliveries in the period through penalties, cancellations and credits.	currency	USD	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
supply_chain.forecasting.accuracy	Demand forecast accuracy against actual demand.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
supply_chain.inventory.avg_value	Average inventory value on hand during the period.	currency	USD	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
supply_chain.inventory.excess_value	Value of inventory held above target levels.	currency	USD	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
supply_chain.inventory.stockout_events	Stockout incidents in the period.	count	events	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
supply_chain.inventory.stockout_rate	Percentage of SKUs or order lines that were out of stock.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
supply_chain.procurement.hours_per_po	Staff hours per purchase-order cycle, from creation through invoice matching.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
supply_chain.procurement.invoice_matching_hours_per_week	AP staff hours per week matching purchase orders to invoices.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
supply_chain.procurement.po_cycle_days	Average calendar days from purchase requisition to approved purchase order.	duration	days	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
supply_chain.procurement.purchase_orders_processed	Purchase orders processed in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
supply_chain.shipments.cost_per_shipment	Average total cost per shipment.	currency	USD	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
supply_chain.shipments.count	Outbound shipments in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
supply_chain.suppliers.active_count	Suppliers the business is actively buying from.	count	suppliers	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
supply_chain.suppliers.disruptions_per_year	Supply disruptions per year.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
supply_chain.suppliers.review_hours_per_month	Staff hours per month on supplier health reviews and risk assessments.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
supply_chain.warehouse.pick_error_rate	Percentage of picks resulting in an error.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
supply_chain.warehouse.picks_count	Units picked in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
supply_chain.warehouse.picks_per_labor_hour	Units picked per warehouse labor hour.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)

Support

Key	What it measures	Type	Unit	Fed by
support.calls.avg_handle_time	Average minutes an agent spends on one inbound phone call — talk, hold and wrap-up. Watch out for Talk, hold and wrap-up together. A phone system report that leaves out wrap-up reads low. It moves with the mix of calls: once an AI takes the simple ones, the calls left for agents run longer, so the average can rise while total agent time falls. Good for Pricing the calls an AI or self-service channel keeps from reaching an agent, with your labor rate. Read the guide	duration	minutes	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
support.knowledge_base.article_count	Knowledge base articles being maintained.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
support.knowledge_base.avg_article_age_days	Average days since knowledge base articles were last reviewed or updated.	duration	days	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
support.knowledge_base.update_minutes_per_article	Minutes to review and update one knowledge base article per cycle.	duration	minutes	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
support.quality.review_minutes_per_interaction	Minutes for a QA analyst to review and score one interaction.	duration	minutes	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
support.quality.sample_rate	Share of support interactions reviewed for quality.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
support.tickets.agent_minutes_per_ticket	Minutes of agent work one resolved ticket takes, from a time study or your help desk's time tracking. Not how long the ticket was open. Watch out for It's hands-on minutes per resolved ticket, not how long the ticket was open, which is mostly waiting. Time the same task before and after, from the same start to the same finish, including the minutes spent checking what the AI drafted. Good for Valuing the agent time an AI routing or drafting tool saves on the tickets agents still resolve. Read the guide	duration	minutes	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
support.tickets.agent_resolved_count	Tickets resolved by an agent: every resolved ticket that no AI deflection rule on the help desk matched, plus the ones a rule matched whose customer came back. Watch out for On Zendesk, Freshdesk and Intercom it leaves out tickets an AI deflection rule matched, unless the customer came back, so a ticket is agent work or deflection value, never both. HubSpot tickets and Salesforce cases don't show whether an AI resolved them, so there it's counted by hand. Leave out what the AI handled alone. Good for The tickets the agent minutes saved per ticket are multiplied by. Read the guide	count	tickets	Freshdesk, Intercom, Zendesk
support.tickets.ai_resolution_rate	Percentage of the tickets resolved in the period that AI resolved, without human escalation.	percentage	%	Freshdesk, Intercom, Zendesk
support.tickets.ai_resolved_count	Support tickets fully resolved by AI without human escalation in the period.	count	tickets	Freshdesk, Intercom, Zendesk
support.tickets.avg_first_response_hours	Average hours from ticket creation to the first agent reply, across tickets opened in the period.	duration	hours	Freshdesk, Intercom, Zendesk
support.tickets.avg_resolution_days	Average days from ticket creation to resolution, waiting included: how long a ticket was open, not how long anyone worked it. Zendesk, Freshdesk and Intercom report hours, converted ÷ 24. Watch out for It counts waiting as well as work: a ticket left open over a weekend gains two days. Don't price it as agent time. It's an average, so a few tickets that ran for weeks can move it more than everything else in the month. Good for Valuing the time people wait less for an IT ticket to be resolved, when AI triage speeds it up.	duration	days	Freshdesk, HubSpot, Intercom, Salesforce, Zendesk
support.tickets.closed_count	Total support tickets closed/resolved in the period.	count	tickets	Freshdesk, HubSpot, Intercom, Salesforce, Zendesk
support.tickets.cost_per_ticket	Blended cost to resolve one support ticket (agent labor plus tooling). Watch out for It's mostly agent time, so value priced from it is freed capacity, not cash. Price a ticket an agent handles: agent minutes × loaded agent cost, plus overhead. Total support spend ÷ every ticket includes the tickets the AI already resolves, and reads low. Good for Pricing the tickets a knowledge base or self-service channel keeps from reaching an agent. Read the guide	currency	USD	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
support.tickets.csat_score	Average customer satisfaction on closed tickets, normalized to 0–1 (Zendesk good/bad → 0/1; Intercom 1–5 → (rating − 1) ÷ 4).	score	0–1	Intercom, Zendesk
support.tickets.deflected_count	Tickets resolved by self-service or AI without an agent in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
support.tickets.resolution_rate	Percentage of tickets opened in the period that were also closed.	percentage	%	HubSpot, Salesforce
support.tickets.self_serve_deflection_rate	Percentage of support inquiries resolved without agent involvement. Watch out for Count it the same way before and after: the same channels, and the same test for resolved without an agent. A customer who gave up looks resolved, so the rate can rise while customers are served worse. Good for Valuing a knowledge base or self-service launch by the change in the share of inquiries resolved without an agent, at this period's ticket volume. Read the guide	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
support.tickets.volume	Total support tickets opened in the period (help-desk tickets, Intercom conversations, Salesforce cases). Watch out for It rises and falls with your customer base, launches and incidents, whatever the AI does. Compare a count per ticket rather than raw totals. Each source counts its own unit: a help desk counts tickets, Intercom conversations, Salesforce cases. Good for The volume a change in a rate is multiplied by, so a busy month isn't read as a loss. Read the guide	count	tickets	Freshdesk, HubSpot, Intercom, Salesforce, Zendesk

Real estate

Key	What it measures	Type	Unit	Fed by
real_estate.leasing.annual_renewal_rate	Percentage of expiring leases renewed.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
real_estate.leasing.days_to_lease	Average days from inquiry to signed lease.	duration	days	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
real_estate.leasing.staff_hours_per_lease	Leasing staff hours per signed lease (lead response, tours, application processing).	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
real_estate.leasing.units_leased	Units signed to new leases in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
real_estate.leasing.units_up_for_renewal	Units with lease expirations in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
real_estate.maintenance.emergency_repairs_per_month	Unplanned emergency repair incidents per month.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
real_estate.portfolio.annual_yield	Net operating income over a year as a percentage of assets under management.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
real_estate.portfolio.aum	Portfolio value under management at the time of measurement.	currency	USD	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
real_estate.portfolio.property_count	Properties in the portfolio.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
real_estate.portfolio.unit_count	Residential or commercial units in the portfolio.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
real_estate.reporting.hours_per_property_monthly	Staff hours per property per month compiling performance and financial reports.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
real_estate.reporting.review_cycle_days	Calendar days between formal portfolio performance reviews.	duration	days	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
real_estate.tenants.contacts_per_unit_monthly	Inbound phone and email contacts per unit per month requiring staff handling.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
real_estate.tenants.minutes_per_contact	Staff minutes to handle one inbound tenant contact.	duration	minutes	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
real_estate.tenants.satisfaction_score	Average tenant satisfaction score from periodic surveys.	score	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
real_estate.work_orders.admin_minutes_per_order	Staff minutes per work order on creation, vendor calls and status follow-ups.	duration	minutes	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
real_estate.work_orders.created_count	Work orders created in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
real_estate.work_orders.resolution_days	Average days from work order submission to resolution.	duration	days	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)

Legal

Key	What it measures	Type	Unit	Fed by
legal.compliance.change_response_days	Days from a regulatory change being published to controls updated and staff notified.	duration	days	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
legal.compliance.gaps_prevented	Compliance control gaps identified and closed before becoming findings, in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
legal.compliance.monitoring_hours_per_week	Staff hours per week tracking regulatory publications and updates.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
legal.contracts.missed_renewals_per_year	Contracts that auto-renewed or lapsed unintentionally per year.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
legal.contracts.renewals_due_per_year	Contracts reaching a renewal or expiry date per year.	count	contracts	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
legal.contracts.search_hours_per_week	Staff hours per week locating and retrieving contracts.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
legal.matters.closed_count	Legal matters handled to completion in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
legal.matters.open_count	Legal matters open during the period.	count	matters	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
legal.matters.research_hours_per_matter	Legal research hours per matter.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
legal.outside_counsel.monthly_spend	Monthly spend on outside counsel for matters that could be handled in-house.	currency	USD	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
legal.policies.attestation_completion_rate	Percentage of employees who complete required policy attestations by the deadline.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
legal.policies.attestation_findings_per_year	Regulatory or audit findings over a year that cite employees not attesting to required policies, from your examination and audit history.	count	findings	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
legal.training.admin_hours_per_cycle	Staff hours per compliance training cycle on enrollment, reminders, tracking and attestation.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
legal.vendors.onboarded_count	New vendors onboarded through due diligence in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
legal.vendors.onboarding_days	Average days from vendor selection to fully approved and active.	duration	days	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
legal.vendors.portfolio_count	Active vendors in the third-party risk portfolio.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
legal.vendors.review_hours_per_vendor	Staff hours per vendor for a periodic risk reassessment.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)

Marketing

Key	What it measures	Type	Unit	Fed by
marketing.budget.destination_pipeline_per_dollar	Pipeline created per dollar of spend in the channels budget was moved to, from the attribution model.	ratio	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
marketing.budget.reallocated	The period's marketing spend that went to the channels attribution favored instead of the channels it was taken from.	currency	USD	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
marketing.budget.source_pipeline_per_dollar	Pipeline created per dollar of spend in the channels budget was taken from, from the attribution model.	ratio	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
marketing.campaigns.avg_list_size	Average recipients per campaign send.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
marketing.campaigns.conversion_rate	Percentage of campaign recipients who completed the campaign's goal.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
marketing.campaigns.hours_per_launch	Staff hours to configure and launch one campaign.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
marketing.campaigns.launched_count	Marketing campaigns with a start date in the period.	count	campaigns	HubSpot
marketing.content.hours_per_piece	Staff hours from brief to published asset.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
marketing.content.pieces_per_month	Content pieces produced per month.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
marketing.content.published_count	Blog posts published in the period on the connected CMS.	count	posts	HubSpot
marketing.emails.open_rate	Opens as a percentage of deliveries across marketing emails published in the period.	percentage	%	HubSpot
marketing.emails.open_to_conversion_rate	Conversions from marketing email as a percentage of its opens in the period, from your email platform or CRM.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
marketing.nurture.manual_hours_per_week	Staff hours per week on lead follow-up and segmentation done by hand.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
marketing.pipeline.sourced_share	Percentage of total sales pipeline attributed to marketing-sourced leads.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
marketing.reporting.hours_per_week	Staff hours per week on marketing performance reports.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
marketing.seo.research_hours_per_month	Staff hours per month on keyword research and on-page optimization.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
marketing.web.organic_sessions	Website sessions from organic search in the period.	count	sessions	HubSpot

Insurance

Key	What it measures	Type	Unit	Fed by
insurance.claims.adjuster_hours_per_claim	Average adjuster hours of hands-on work per claim closed, a straight-through claim counting as none — not the days from first notice of loss to close.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
insurance.claims.auto_adjudication_rate	Share of claims processed straight-through without adjuster review.	ratio	0–1	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
insurance.claims.avg_cycle_days	Average days from first notice of loss to claim closure.	duration	days	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
insurance.claims.incurred_losses	Incurred losses on the affected book in the period.	currency	USD	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
insurance.claims.lae_ratio	Loss adjustment expense as a percentage of incurred losses.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
insurance.claims.leakage_rate	Paid loss attributable to fraud or excess adjustments as a percentage of claims paid.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
insurance.claims.processed_count	Claims submitted and processed in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
insurance.fraud.false_positive_siu_referrals	Special investigations unit referrals per month that do not substantiate fraud.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
insurance.fraud.hours_per_siu_investigation	Investigator hours one SIU investigation takes, from your SIU case system's time tracking or your own timing. Not the days the case stays open.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
insurance.policy_servicing.calls_per_month	Inbound policy-servicing call volume per month.	count	calls	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
insurance.policy_servicing.requests_per_month	Policy-servicing requests per month across every channel (phone, portal, app and chat), as the policy administration system counts them.	count	requests	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
insurance.underwriting.earned_premium	Earned premium on the targeted book in the period.	currency	USD	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
insurance.underwriting.loss_ratio	Incurred losses as a percentage of earned premium.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
insurance.underwriting.quote_hours	Average underwriter hours from application to bindable quote.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
insurance.underwriting.submission_count	New business submissions reviewed in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)

Manufacturing

Key	What it measures	Type	Unit	Fed by
manufacturing.data.reconciliation_hours_per_week	Staff hours per week reconciling data between shop floor systems and the ERP.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
manufacturing.equipment.oee	Overall equipment effectiveness.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
manufacturing.equipment.oee_reporting_hours_per_week	Staff hours per week collecting and compiling OEE data.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
manufacturing.equipment.scheduled_hours_per_month	Hours per month the monitored equipment was scheduled to run.	count	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
manufacturing.equipment.unplanned_downtime_hours_per_month	Unplanned equipment downtime hours per month.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
manufacturing.maintenance.annual_cost_per_asset	Average annual maintenance cost per asset including reactive repairs and planned servicing.	currency	USD	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
manufacturing.maintenance.annual_spend	Annual maintenance spend across monitored assets, planned and reactive.	currency	USD	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
manufacturing.maintenance.cost_reduction_rate	Percentage reduction in total maintenance spend from condition-based servicing.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
manufacturing.materials.shortage_incidents_per_month	Production stoppages caused by material shortages per month.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
manufacturing.production.theoretical_capacity_units	Units producible in the period at nameplate capacity.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
manufacturing.production.units_produced	Units produced in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
manufacturing.quality.defect_escape_rate	Percentage of produced units with a defect that reaches the customer.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
manufacturing.quality.first_pass_yield	Percentage of units completing production without defect, rework or scrap.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
manufacturing.quality.scrap_cost_per_month	Monthly scrap and rework cost.	currency	USD	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
manufacturing.scheduling.hours_per_week	Staff hours per week building and adjusting production schedules.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)

Anthropic

Key	What it measures	Type	Unit	Fed by
anthropic.claude_code.acceptance_rate	Accepted tool actions as a share of those proposed in Anthropic's Claude Code report for the month. Read as engineering.coding_assistant.acceptance_rate when Anthropic is the primary coding-assistant source.	percentage	%	Anthropic
anthropic.claude_code.acceptances	Tool actions developers accepted in Anthropic's Claude Code report for the month — one accepted edit or write each, not a bundled activity as GitHub counts them. Read as engineering.coding_assistant.acceptances when Anthropic is the primary coding-assistant source. Watch out for One accepted edit or write each, a smaller unit than a Copilot acceptance, so the two are priced at separate minutes saved, never added up. It counts edits a developer took, not edits that shipped. It covers the Claude Code your Anthropic Console organization reports. Claude Code used on Claude Enterprise seats reports separately. Good for Valuing Claude Code's share of the time saved where a team runs it beside GitHub Copilot, each at its own minutes saved per acceptance. Read the guide	count	acceptances	Anthropic
anthropic.claude_code.active_users_avg	Developers who ran Claude Code on an average day in the month. Read as engineering.coding_assistant.active_users when Anthropic is the primary coding-assistant source.	average	users	Anthropic
anthropic.claude_code.lines_accepted	Lines of code Claude Code added in the month. Read as engineering.coding_assistant.lines_accepted when Anthropic is the primary coding-assistant source.	count	lines	Anthropic
anthropic.claude_code.proposals	Tool actions Claude Code proposed in the month, accepted and rejected together. Read as engineering.coding_assistant.suggestions when Anthropic is the primary coding-assistant source.	count	suggestions	Anthropic
anthropic.usage.cost_per_million_output_tokens	Anthropic's spend for the month over the output tokens it generated, per million. Written to ai.cost.per_million_output_tokens instead when this connection is the primary source for AI usage.	currency	USD	Anthropic
anthropic.usage.input_tokens	Anthropic's input tokens for the month. Written to ai.usage.input_tokens instead when this connection is the primary source.	count	tokens	Anthropic
anthropic.usage.model_input_tokens	Input tokens sent to one Claude model in the month; the model is in the observation's dimensions.	count	tokens	Anthropic
anthropic.usage.model_output_tokens	Output tokens generated by one Claude model in the month; the model is in the observation's dimensions.	count	tokens	Anthropic
anthropic.usage.model_spend	Anthropic API spend on one model for the month; the model is in the observation's dimensions.	currency	USD	Anthropic
anthropic.usage.model_total_tokens	Input plus output tokens for one Claude model in the month; the model is in the observation's dimensions.	count	tokens	Anthropic
anthropic.usage.output_tokens	Anthropic's output tokens for the month. Written to ai.usage.output_tokens instead when this connection is the primary source.	count	tokens	Anthropic
anthropic.usage.spend	Anthropic's usage spend for the month.	currency	USD	Anthropic
anthropic.usage.total_tokens	Anthropic's total tokens for the month. Written to ai.usage.total_tokens instead when this connection is the primary source.	count	tokens	Anthropic

IT

Key	What it measures	Type	Unit	Fed by
it.audits.hours_per_cycle	IT staff hours per software inventory and license reconciliation audit cycle.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
it.deployments.change_failure_rate	Percentage of deployments causing a rollback or incident.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
it.deployments.count	Production deployments in the period.	count	—	GitHub
it.deployments.hours_per_deployment	Engineer hours per production deployment, including manual steps, monitoring and validation.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
it.deployments.per_week	Production deployments per week.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
it.helpdesk.avg_users_per_incident	End-users a typical IT incident affects, whether or not it stops them working.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
it.helpdesk.mean_time_to_resolution_hours	Mean hours to resolve a tier-1 IT issue.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
it.helpdesk.tickets_per_month	IT helpdesk tickets per month requiring agent handling.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
it.security.incident_count	Security incidents investigated in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
it.security.incidents_per_year	Security incidents per year.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
it.security.mean_time_to_contain_hours	Mean hours from detection of a security incident to its containment.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
it.security.mean_time_to_detect_hours	Mean hours from a security incident starting to its detection.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
it.software.annual_spend	Total annual software license spend.	currency	USD	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
it.software.unused_license_share	Percentage of purchased licenses that are unassigned or unused.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)

Retail

Key	What it measures	Type	Unit	Fed by
retail.customers.active_count	Active customers in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
retail.customers.repeat_purchase_rate	Percentage of customers making a second purchase within twelve months.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
retail.customers.retention_rate	Twelve-month customer retention rate.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
retail.inventory.accuracy	Percentage of inventory records matching physical stock.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
retail.inventory.markdown_eligible_sold_value	Full retail value of the seasonal or clearance-eligible stock that sold or cleared in the period, at target price or marked down.	currency	USD	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
retail.inventory.sell_through_rate	Of the seasonal or clearance-eligible stock that sold or cleared in the period, the percentage by retail value that sold at or above target price.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
retail.inventory.shrink_rate	Book inventory less what a physical count finds, as a percentage of sales over the same span, from your stock counts.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
retail.orders.avg_value	Average order value.	currency	USD	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
retail.orders.count	Orders placed in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
retail.pricing.avg_markdown_depth	Markdown dollars as a percentage of the full retail value of the sales made at a markdown price in the period, or how far below full price a marked-down sale went on average.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
retail.pricing.gross_margin	Gross margin as a percentage of revenue.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
retail.pricing.markdown_rate	Percentage of the period's sales, by full retail value, made at a markdown price.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
retail.web.conversion_rate	Orders as a percentage of website sessions.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
retail.web.session_count	Website sessions in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)

Energy

Key	What it measures	Type	Unit	Fed by
energy.assets.critical_count	Critical assets in service: the transformers, breakers, lines and generating units whose failures count as unplanned outages.	count	assets	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
energy.assets.deferred_capex	Asset replacement spend deferred by extending asset life.	currency	USD	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
energy.assets.unplanned_outages_per_year	Unplanned critical-asset outage events per year.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
energy.grid.peak_demand_mw	Peak system demand the reserve is held against, in megawatts.	count	MW	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
energy.grid.reserve_capacity_mw	Reserve generation capacity held, in megawatts.	count	MW	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
energy.load_forecasting.mape	Mean absolute percentage error of the load forecast.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
energy.rate_cases.count	Rate cases and major regulatory filings prepared in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
energy.rate_cases.external_counsel_hours	Outside counsel hours billed for a rate case or major regulatory filing.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
energy.rate_cases.prep_hours	Internal staff hours to prepare a rate case or major regulatory filing.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
energy.reliability.saidi_minutes	System Average Interruption Duration Index, in outage minutes per customer per year.	duration	minutes	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
energy.storms.call_volume_index	Inbound call volume during a typical major storm event.	count	index	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
energy.storms.events_per_year	Major storm or high-call-volume outage events per year.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
energy.storms.notification_hours_per_event	Staff hours spent communicating outage status to customers during one major storm event.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)

Hospitality

Key	What it measures	Type	Unit	Fed by
hospitality.bookings.direct_share	Share of bookings made through direct channels.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
hospitality.food_beverage.cost_of_goods	Food and beverage cost of goods in the period.	currency	USD	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
hospitality.food_beverage.covers_served	Covers served in the period.	count	covers	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
hospitality.food_beverage.prep_hours	Kitchen prep hours in the period.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
hospitality.food_beverage.waste_share	Food cost lost to waste and spoilage as a percentage of food and beverage cost of goods.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
hospitality.guests.ancillary_spend_per_guest	Average ancillary spend per guest.	currency	USD	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
hospitality.guests.count	Guests in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
hospitality.housekeeping.minutes_per_room_turn	Housekeeping labor minutes per room turn.	duration	minutes	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
hospitality.housekeeping.rooms_turned	Room turns (checkouts and cleans) in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
hospitality.revenue_management.hours_per_week	Revenue manager hours per week on rate setting and reporting.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
hospitality.rooms.available_room_nights	Available room-nights in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
hospitality.rooms.revenue	Total room revenue across all channels in the period.	currency	USD	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
hospitality.rooms.revpar	Revenue per available room.	currency	USD	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)

Logistics

Key	What it measures	Type	Unit	Fed by
logistics.customer_service.minutes_per_wismo_contact	Minutes of agent work one "Where Is My Order" contact takes, from a time study or your help desk's time tracking on those contacts alone. Not every ticket's average.	duration	minutes	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
logistics.customer_service.wismo_contacts_per_month	Where-is-my-order inbound contacts per month.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
logistics.deliveries.on_time_rate	Percentage of deliveries completed within the promised window.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
logistics.drivers.count	Last-mile drivers in the measured operation.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
logistics.drivers.operating_hours	Driver operating hours in the period.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
logistics.drivers.stops_per_hour	Completed delivery stops per driver per labor hour.	count	stops	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
logistics.fleet.annual_insurance_premium	Annual commercial auto and cargo insurance premium across the fleet.	currency	USD	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
logistics.fleet.maintenance_hours_per_month	Technician hours per month on scheduled and reactive fleet maintenance.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
logistics.fleet.miles	Total fleet miles in the period.	count	miles	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
logistics.fleet.roadside_events_per_year	Unplanned roadside breakdown events per year.	count	events	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
logistics.fleet.stop_count	Delivery and pickup stops the fleet completed in the period.	count	stops	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
logistics.fleet.vehicle_count	Vehicles in service across the fleet.	count	vehicles	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
logistics.safety.preventable_accidents_per_million_miles	Preventable accident frequency per million miles driven.	count	accidents	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)

Salesforce

Key	What it measures	Type	Unit	Fed by
salesforce.cases.avg_resolution_days	Salesforce's average case resolution time for the month. Written to support.tickets.avg_resolution_days instead when this connection is the primary source.	duration	days	Salesforce
salesforce.cases.closed_count	Salesforce's support tickets closed for the month. Written to support.tickets.closed_count instead when this connection is the primary source.	count	tickets	Salesforce
salesforce.cases.open_count	Cases open in Salesforce at sync time, a snapshot rather than a period total, written once per month and rewritten on every sync.	count	—	Salesforce
salesforce.cases.resolution_rate	Salesforce's case resolution rate for the month. Written to support.tickets.resolution_rate instead when this connection is the primary source.	percentage	%	Salesforce
salesforce.cases.volume	Salesforce's support tickets opened for the month. Written to support.tickets.volume instead when this connection is the primary source.	count	tickets	Salesforce
salesforce.opportunities.avg_sales_cycle_days	Salesforce's average sales cycle for the month. Written to crm.deals.avg_sales_cycle_days instead when this connection is the primary source.	duration	days	Salesforce
salesforce.opportunities.avg_value	Salesforce's average won opportunity size for the month. Written to crm.deals.avg_value instead when this connection is the primary source.	currency	USD	Salesforce
salesforce.opportunities.lost_count	Salesforce's lost deals for the month. Written to crm.deals.lost_count instead when this connection is the primary source.	count	deals	Salesforce
salesforce.opportunities.open_count	Opportunities open in Salesforce at sync time, a snapshot rather than a period total, written once per month and rewritten on every sync.	count	—	Salesforce
salesforce.opportunities.open_value	Value of the opportunities open in Salesforce at sync time, a snapshot rather than a period total, written once per month and rewritten on every sync.	currency	USD	Salesforce
salesforce.opportunities.win_rate	Salesforce's deal win rate for the month. Written to crm.deals.win_rate instead when this connection is the primary source.	percentage	%	Salesforce
salesforce.opportunities.won_count	Salesforce's won deals for the month. Written to crm.deals.won_count instead when this connection is the primary source.	count	deals	Salesforce
salesforce.opportunities.won_value	Salesforce's won deal value for the month. Written to crm.deals.won_value instead when this connection is the primary source.	currency	USD	Salesforce

Education

Key	What it measures	Type	Unit	Fed by
education.admissions.admitted_count	Applicants offered admission in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
education.admissions.applicant_count	Applicants in the enrollment cycle.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
education.admissions.decision_days	Average days from application received to decision.	duration	days	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
education.admissions.review_minutes_per_application	Average staff minutes of hands-on work to review and process one application — not the days it waits for a decision.	duration	minutes	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
education.admissions.yield_rate	Percentage of admitted students who enroll.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
education.courses.dfw_rate	Percentage of students earning a D or F or withdrawing.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
education.enrollment.retention_rate	Year-over-year student retention rate.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
education.enrollment.student_count	Students enrolled in the affected courses or programs.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
education.faculty.admin_hours_per_week	Faculty hours per week on course administration.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
education.faculty.count	Faculty using AI course-operations tools.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
education.outreach.hours_per_week	Staff hours per week spent identifying at-risk students and reaching out to them.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
education.tutoring.cost_per_student	Cost of tutoring services per enrolled student in the period.	currency	USD	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)

GitHub

Key	What it measures	Type	Unit	Fed by
github.copilot.acceptance_rate	Accepted code activities as a share of code generation activities in GitHub's Copilot organization report for the month. Read as engineering.coding_assistant.acceptance_rate when GitHub is the primary coding-assistant source.	percentage	%	GitHub
github.copilot.active_users_avg	Daily active Copilot users in GitHub's organization report, averaged over the month. Read as engineering.coding_assistant.active_users when GitHub is the primary coding-assistant source.	average	—	GitHub
github.copilot.lines_accepted	Lines of code accepted in GitHub's Copilot organization report for the month. Read as engineering.coding_assistant.lines_accepted when GitHub is the primary coding-assistant source.	count	—	GitHub
github.copilot.suggestions_accepted	Code acceptance activities in GitHub's Copilot organization report for the month. Read as engineering.coding_assistant.acceptances when GitHub is the primary coding-assistant source. Watch out for One acceptance activity can bundle several completions, so it isn't the same unit as a Claude Code accepted edit. The two are priced at separate minutes saved, never added up. It counts code a developer took, not code that shipped: an accepted suggestion can be rewritten or deleted later. Good for Valuing Copilot's share of the time saved where a team runs it beside Claude Code, each at its own minutes saved per acceptance. Read the guide	count	—	GitHub
github.copilot.suggestions_shown	Code generation activities in GitHub's Copilot organization report for the month. Read as engineering.coding_assistant.suggestions when GitHub is the primary coding-assistant source.	count	suggestions	GitHub
github.cost.copilot_spend	GitHub's Copilot and AI-credit net spend for the month. Written as ai.cost.spend when this connection is the primary AI-cost source.	currency	USD	GitHub
github.cost.product_spend	GitHub spend on one product (Actions, Copilot, Packages, …) for the month; the product is in the observation's dimensions.	currency	USD	GitHub
github.cost.total_spend	GitHub's total spend for the month, every product included.	currency	USD	GitHub
github.deployments.count	Deployments GitHub recorded to a production environment in the month. Written to it.deployments.count instead when this connection is the primary source for deployments.	count	deployments	GitHub
github.pull_requests.additions_avg	GitHub's average lines added per pull request for the month.	average	—	GitHub
github.pull_requests.cycle_time_avg	GitHub's average pull request cycle time for the month.	duration	—	GitHub
github.pull_requests.merged_count	GitHub's pull requests merged count for the month.	count	—	GitHub

Operations

Key	What it measures	Type	Unit	Fed by
operations.applications.processed_count	Applications reviewed and decided in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
operations.documents.data_entry_minutes_per_document	Staff minutes to key the data from one document.	duration	minutes	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
operations.documents.extraction_accuracy	Percentage of extracted fields that are correct without human correction.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
operations.documents.processed_count	Documents (PDFs, forms, emails) processed by the extraction pipeline in the period.	count	documents	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
operations.forecasting.accuracy	Accuracy of the operating forecast (cash flow, demand or pipeline) against actuals.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
operations.forecasting.cycles	Forecast cycles run in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
operations.forecasting.hours_per_cycle	Staff hours to produce one complete forecast cycle.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
operations.processing.error_rate	Percentage of processed items that contained an error or needed manual correction.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
operations.reporting.delivery_lag_days	Days from period close to delivery of the period's report.	duration	days	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
operations.reporting.distribution_lag_hours	Hours between data availability and report delivery to stakeholders.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
operations.reporting.hours_per_week	Staff hours per week spent compiling and distributing reports by hand.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
operations.reporting.reports_automated	Distinct report types fully automated and removed from manual production.	count	reports	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)

OpenAI

Key	What it measures	Type	Unit	Fed by
openai.usage.cost_per_million_output_tokens	OpenAI's spend for the month over the output tokens it generated, per million. Written to ai.cost.per_million_output_tokens instead when this connection is the primary source for AI usage.	currency	USD	OpenAI
openai.usage.cost_per_request	OpenAI's spend for the month over the requests it served. Written to ai.cost.per_request instead when this connection is the primary source for AI usage.	currency	USD	OpenAI
openai.usage.input_tokens	OpenAI's input tokens for the month. Written to ai.usage.input_tokens instead when this connection is the primary source.	count	tokens	OpenAI
openai.usage.model_input_tokens	Input tokens sent to one OpenAI model in the month; the model is in the observation's dimensions.	count	tokens	OpenAI
openai.usage.model_output_tokens	Output tokens generated by one OpenAI model in the month; the model is in the observation's dimensions.	count	tokens	OpenAI
openai.usage.model_request_count	API requests to one OpenAI model in the month; the model is in the observation's dimensions.	count	requests	OpenAI
openai.usage.model_total_tokens	Input plus output tokens for one OpenAI model in the month; the model is in the observation's dimensions.	count	tokens	OpenAI
openai.usage.output_tokens	OpenAI's output tokens for the month. Written to ai.usage.output_tokens instead when this connection is the primary source.	count	tokens	OpenAI
openai.usage.request_count	OpenAI's API requests for the month. Written to ai.usage.request_count instead when this connection is the primary source.	count	requests	OpenAI
openai.usage.spend	OpenAI's usage spend for the month.	currency	USD	OpenAI
openai.usage.total_tokens	OpenAI's total tokens for the month. Written to ai.usage.total_tokens instead when this connection is the primary source.	count	tokens	OpenAI

Media

Key	What it measures	Type	Unit	Fed by
media.audience.avg_active_users	Average monthly active users on the service.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
media.audience.monthly_watch_hours_per_user	Average monthly watch hours per active user.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
media.localization.cost_per_episode	Average cost to localize one episode (dubbing, subtitling and QC).	currency	USD	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
media.localization.episodes_localized	Episodes localized into additional languages in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
media.post_production.days_per_episode	Days from picture lock to delivery per episode.	duration	days	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
media.post_production.episode_count	Episodes entering post-production in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
media.rights.admin_hours_per_week	Staff hours per week on rights tracking, metadata correction and catalog reconciliation.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
media.royalties.dispute_rate	Share of royalty statements requiring dispute or correction.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
media.royalties.statement_count	Royalty statements issued in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)

AI

Key	What it measures	Type	Unit	Fed by
ai.cost.per_million_output_tokens	All-in AI spend per million output tokens: everything the provider billed in the period (input, cache and output charges) divided by the output tokens it generated. The unit of AI Spend Optimization for a provider that reports tokens but no request count, such as Anthropic.	currency	USD	Anthropic, OpenAI
ai.cost.per_request	All-in AI spend per API request: everything the provider billed in the period divided by the requests made to it. The unit of AI Spend Optimization for a provider that reports request counts and its own spend, such as OpenAI.	currency	USD	OpenAI
ai.cost.spend	Total AI provider spend in the period — the AI-cost source's, or every one's added up when the account reads them combined.	currency	USD	Anthropic, GitHub, OpenAI
ai.outputs.human_review_rate	Percentage of AI outputs routed to a human reviewer before being acted on.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
ai.usage.input_tokens	Total prompt/input tokens consumed across the primary AI provider in the period.	count	tokens	Anthropic, OpenAI
ai.usage.output_tokens	Total completion/output tokens generated by the primary AI provider in the period.	count	tokens	Anthropic, OpenAI
ai.usage.request_count	Number of API requests made to the primary AI provider in the period. OpenAI and Gemini report it; Anthropic reports tokens but no request count.	count	requests	OpenAI
ai.usage.total_tokens	Total tokens (input + output) used through the primary AI provider in the period.	count	tokens	Anthropic, OpenAI

Government

Key	What it measures	Type	Unit	Fed by
government.cases.decision_days	Average days from case intake to eligibility decision.	duration	days	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
government.cases.processed_count	Benefit applications and cases processed in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
government.cases.staff_hours_per_case	Average caseworker hours of hands-on work per case decided — not the days it waits for a decision.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
government.constituent_services.calls_per_month	Inbound constituent contacts per month.	count	calls	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
government.constituent_services.requests_per_month	Constituent service requests per month across every channel (phone, web, app and chat), as the 311 or case system counts them.	count	requests	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
government.permits.decision_days	Average days from permit intake to decision.	duration	days	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
government.permits.processed_count	Permit applications processed in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
government.permits.staff_hours_per_permit	Average staff hours of hands-on work per permit decided — not the days it waits for a decision.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)

NetSuite

Key	What it measures	Type	Unit	Fed by
netsuite.financials.expenses	NetSuite's expenses for the month. Written to finance.pnl.expenses instead when this connection is the primary source.	currency	USD	NetSuite
netsuite.financials.net_income	NetSuite's net income for the month. Written to finance.pnl.net_income instead when this connection is the primary source.	currency	USD	NetSuite
netsuite.financials.revenue	NetSuite's revenue for the month. Written to finance.pnl.revenue instead when this connection is the primary source.	currency	USD	NetSuite
netsuite.invoices.count	NetSuite's invoices created for the month. Written to finance.invoices.count instead when this connection is the primary source.	count	invoices	NetSuite
netsuite.invoices.paid_count	NetSuite's invoices paid for the month. Written to finance.invoices.paid_count instead when this connection is the primary source.	count	invoices	NetSuite
netsuite.invoices.volume	NetSuite's invoice volume for the month. Written to finance.invoices.volume instead when this connection is the primary source.	currency	USD	NetSuite
netsuite.vendor_bills.count	NetSuite's bills received for the month. Written to finance.bills.count instead when this connection is the primary source.	count	bills	NetSuite
netsuite.vendor_bills.volume	NetSuite's bill volume for the month. Written to finance.bills.volume instead when this connection is the primary source.	currency	USD	NetSuite

Pipedrive

Key	What it measures	Type	Unit	Fed by
pipedrive.deals.avg_sales_cycle_days	Pipedrive's average sales cycle for the month. Written to crm.deals.avg_sales_cycle_days instead when this connection is the primary source.	duration	days	Pipedrive
pipedrive.deals.avg_value	Pipedrive's average won deal size for the month. Written to crm.deals.avg_value instead when this connection is the primary source.	currency	USD	Pipedrive
pipedrive.deals.lost_count	Pipedrive's lost deals for the month. Written to crm.deals.lost_count instead when this connection is the primary source.	count	deals	Pipedrive
pipedrive.deals.open_count	Deals open in Pipedrive at sync time, a snapshot rather than a period total, written once per month and rewritten on every sync.	count	—	Pipedrive
pipedrive.deals.open_value	Value of the deals open in Pipedrive at sync time, a snapshot rather than a period total, written once per month and rewritten on every sync.	currency	USD	Pipedrive
pipedrive.deals.win_rate	Pipedrive's deal win rate for the month. Written to crm.deals.win_rate instead when this connection is the primary source.	percentage	%	Pipedrive
pipedrive.deals.won_count	Pipedrive's won deals for the month. Written to crm.deals.won_count instead when this connection is the primary source.	count	deals	Pipedrive
pipedrive.deals.won_value	Pipedrive's won deal value for the month. Written to crm.deals.won_value instead when this connection is the primary source.	currency	USD	Pipedrive

QuickBooks

Key	What it measures	Type	Unit	Fed by
quickbooks.bills.count	QuickBooks's bills received for the month. Written to finance.bills.count instead when this connection is the primary source.	count	bills	QuickBooks
quickbooks.bills.volume	QuickBooks's bill volume for the month. Written to finance.bills.volume instead when this connection is the primary source.	currency	USD	QuickBooks
quickbooks.financials.expenses	QuickBooks's expenses for the month. Written to finance.pnl.expenses instead when this connection is the primary source.	currency	USD	QuickBooks
quickbooks.financials.net_income	QuickBooks's net income for the month. Written to finance.pnl.net_income instead when this connection is the primary source.	currency	USD	QuickBooks
quickbooks.financials.revenue	QuickBooks's revenue for the month. Written to finance.pnl.revenue instead when this connection is the primary source.	currency	USD	QuickBooks
quickbooks.invoices.count	QuickBooks's invoices created for the month. Written to finance.invoices.count instead when this connection is the primary source.	count	invoices	QuickBooks
quickbooks.invoices.paid_count	QuickBooks's invoices paid for the month. Written to finance.invoices.paid_count instead when this connection is the primary source.	count	invoices	QuickBooks
quickbooks.invoices.volume	QuickBooks's invoice volume for the month. Written to finance.invoices.volume instead when this connection is the primary source.	currency	USD	QuickBooks

Telecom

Key	What it measures	Type	Unit	Fed by
telecom.customer_care.calls_per_month	Inbound customer care calls per month.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
telecom.customer_care.contacts_per_month	Customer care contacts per month across every channel: calls, chat, app and self-service.	count	contacts	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
telecom.customer_care.cost_per_subscriber	Customer care cost per subscriber per period.	currency	USD	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
telecom.field_service.truck_rolls_per_month	Field dispatch events per month.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
telecom.network.incident_count	Network incidents in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
telecom.network.mttr_minutes	Mean minutes to recover from a network incident.	duration	minutes	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
telecom.retention.cost_per_retained_subscriber	Average cost of retention offers per subscriber retained.	currency	USD	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
telecom.retention.retained_subscribers	Subscribers retained through save offers in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)

Xero

Key	What it measures	Type	Unit	Fed by
xero.bills.count	Xero's bills received for the month. Written to finance.bills.count instead when this connection is the primary source.	count	bills	Xero
xero.bills.volume	Xero's bill volume for the month. Written to finance.bills.volume instead when this connection is the primary source.	currency	USD	Xero
xero.financials.expenses	Xero's expenses for the month. Written to finance.pnl.expenses instead when this connection is the primary source.	currency	USD	Xero
xero.financials.net_income	Xero's net income for the month. Written to finance.pnl.net_income instead when this connection is the primary source.	currency	USD	Xero
xero.financials.revenue	Xero's revenue for the month. Written to finance.pnl.revenue instead when this connection is the primary source.	currency	USD	Xero
xero.invoices.count	Xero's invoices created for the month. Written to finance.invoices.count instead when this connection is the primary source.	count	invoices	Xero
xero.invoices.paid_count	Xero's invoices paid for the month. Written to finance.invoices.paid_count instead when this connection is the primary source.	count	invoices	Xero
xero.invoices.volume	Xero's invoice volume for the month. Written to finance.invoices.volume instead when this connection is the primary source.	currency	USD	Xero

Billing

Key	What it measures	Type	Unit	Fed by
billing.charges.count	Charges in the period that kept revenue, in the currency the account settles in.	count	charges	Stripe
billing.charges.volume	What the period's charges kept, captured less refunded, tax included, in the currency the account settles in.	currency	USD	Stripe
billing.subscriptions.active_count	Subscriptions active at the end of the period. Watch out for A count at the end of the period, not an average over it. Check whether your billing system counts trials, paused or past-due subscriptions as active, and keep that the same before and after. Good for The base a change in churn, or in a cost per subscriber, is multiplied by. Read the guide	count	subscriptions	Stripe
billing.subscriptions.churn_rate	Subscriptions canceled in the period as a percentage of those active at its start. Absent for a period with nothing active at the start. Watch out for It moves with price, product and competitors, so a formula valuing its change takes only the share of it you credit to the initiative. It's monthly. Compare it with a monthly baseline, never an annual rate. On a small base a few cancellations move it a lot. Read it over several months. Good for Valuing churn prediction and save plays by the subscribers kept. Read the guide	percentage	%	Stripe
billing.subscriptions.churned_count	Subscriptions canceled in the period.	count	subscriptions	Stripe
billing.subscriptions.mrr	Monthly recurring revenue from subscriptions active at the end of the period. Watch out for A level at the end of the period, what active subscriptions bill each month, not the revenue the month earned. It's revenue, not return: a formula books what you keep of it at your contribution margin. Good for Pricing a change in churn or net retention in recurring revenue kept. Read the guide	currency	USD	Stripe
billing.subscriptions.new_count	Subscriptions created in the period.	count	subscriptions	Stripe

Engineering

Key	What it measures	Type	Unit	Fed by
engineering.coding_assistant.acceptance_rate	Accepted suggestions as a share of the suggestions offered in the period.	percentage	%	Anthropic, Claude Enterprise, GitHub
engineering.coding_assistant.acceptances	Suggestions developers accepted from an AI coding assistant in the period. Watch out for Copilot counts acceptance activities, which can bundle several completions, and Claude Code counts single accepted edits, so totals from two tools aren't like for like. Compare one tool with itself, month to month. It counts code a developer took, not code that shipped: an accepted suggestion can be rewritten or deleted later. It rises with the number of developers using the tool. Read it beside active users before calling a rise better use. Good for Valuing the developer time an assistant saves: accepted suggestions × minutes saved per acceptance × your developer hourly rate. Following adoption after a rollout, beside active users. Read the guide	count	acceptances	Anthropic, Claude Enterprise, GitHub
engineering.coding_assistant.active_users	Developers using an AI coding assistant on an average day in the period. Summed across assistants when read combined.	average	users	Anthropic, Claude Enterprise, GitHub
engineering.coding_assistant.lines_accepted	Lines of code developers accepted from an AI coding assistant in the period. Watch out for A line is a poor measure of effort: a hard line and an easy one count the same, and a line the assistant wrote isn't always one anyone would have written. Each tool counts its own lines: Copilot the lines accepted, Claude Code the lines it added, Codex the lines it added that developers kept. Don't add them up or compare them across tools. Good for Valuing the time saved by an assistant that reports no count of accepted suggestions, such as OpenAI's Codex. Where acceptances are reported, they're the better measure. Read the guide	count	lines	Anthropic, Claude Enterprise, GitHub, OpenAI Codex
engineering.coding_assistant.suggestions	Code suggestions an AI coding assistant offered developers in the period.	count	suggestions	Anthropic, Claude Enterprise, GitHub
engineering.pull_requests.cycle_time_avg	Average hours from a pull request being opened to being merged, over the pull requests merged in the period.	duration	hours	GitHub
engineering.pull_requests.merged_count	Pull requests merged in the period.	count	pull requests	GitHub

Stripe

Key	What it measures	Type	Unit	Fed by
stripe.charges.count	Stripe's charges for the month that kept revenue, in the currency the account settles in.	count	—	Stripe
stripe.charges.volume	What Stripe's charges kept for the month, captured less refunded, tax included, in the currency the account settles in. Charges in other currencies aren't counted.	currency	USD	Stripe
stripe.subscriptions.active_count	Stripe's active subscriptions for the month. Written to billing.subscriptions.active_count instead when this connection is the primary source.	count	subscriptions	Stripe
stripe.subscriptions.churn_rate	Stripe's subscription churn rate for the month. Written to billing.subscriptions.churn_rate instead when this connection is the primary source.	percentage	%	Stripe
stripe.subscriptions.churned_count	Stripe's churned subscriptions for the month. Written to billing.subscriptions.churned_count instead when this connection is the primary source.	count	subscriptions	Stripe
stripe.subscriptions.mrr	Stripe's monthly recurring revenue for the month. Written to billing.subscriptions.mrr instead when this connection is the primary source.	currency	USD	Stripe
stripe.subscriptions.new_count	Stripe's new subscriptions for the month. Written to billing.subscriptions.new_count instead when this connection is the primary source.	count	subscriptions	Stripe

Intercom

Key	What it measures	Type	Unit	Fed by
intercom.conversations.agent_resolved_count	Intercom's closed conversations for the month that no AI deflection rule on the connection matched, plus the ones a rule matched whose customer came back. Read as support.tickets.agent_resolved_count when this connection is the primary source.	count	tickets	Intercom
intercom.conversations.avg_first_response_hours	Intercom's average ticket first response time for the month. Written to support.tickets.avg_first_response_hours instead when this connection is the primary source.	duration	hours	Intercom
intercom.conversations.avg_resolution_days	Intercom's average time from a conversation opening to closing for the month, in days (Intercom reports hours; the computer converts). Read as support.tickets.avg_resolution_days when this connection is the primary source.	duration	days	Intercom
intercom.conversations.closed_count	Intercom's support tickets closed for the month. Written to support.tickets.closed_count instead when this connection is the primary source.	count	tickets	Intercom
intercom.conversations.csat_score	Intercom's average conversation rating for the month on the 0–1 scale the support keys use (Intercom rates 1–5; the computer converts). Read as support.tickets.csat_score when this connection is the primary source.	score	0–1	Intercom
intercom.conversations.volume	Intercom's support tickets opened for the month. Written to support.tickets.volume instead when this connection is the primary source.	count	tickets	Intercom

Zendesk

Key	What it measures	Type	Unit	Fed by
zendesk.tickets.agent_resolved_count	Zendesk's solved and closed tickets for the month that no AI deflection rule on the connection matched, plus the ones a rule matched whose customer came back. Read as support.tickets.agent_resolved_count when this connection is the primary source.	count	tickets	Zendesk
zendesk.tickets.avg_first_response_hours	Zendesk's average ticket first response time for the month. Written to support.tickets.avg_first_response_hours instead when this connection is the primary source.	duration	hours	Zendesk
zendesk.tickets.avg_resolution_days	Zendesk's average time from a ticket's creation to its solve for the month, in days (the computer converts from hours). Read as support.tickets.avg_resolution_days when this connection is the primary source.	duration	days	Zendesk
zendesk.tickets.csat_score	Zendesk's ticket CSAT score for the month. Written to support.tickets.csat_score instead when this connection is the primary source.	score	0–1	Zendesk
zendesk.tickets.solved_count	Zendesk's support tickets closed for the month. Written to support.tickets.closed_count instead when this connection is the primary source.	count	tickets	Zendesk
zendesk.tickets.volume	Zendesk's support tickets opened for the month. Written to support.tickets.volume instead when this connection is the primary source.	count	tickets	Zendesk

Claude Enterprise

Key	What it measures	Type	Unit	Fed by
claude_enterprise.claude_code.acceptance_rate	Accepted tool actions as a share of those proposed across the Claude Enterprise organization for the month. Read as engineering.coding_assistant.acceptance_rate when Claude Enterprise is the primary coding-assistant source.	percentage	%	Claude Enterprise
claude_enterprise.claude_code.acceptances	Edits and writes developers accepted in Claude Code across the Claude Enterprise organization for the month — one accepted tool action each, the same unit as Anthropic's Claude Code report. Read as engineering.coding_assistant.acceptances when Claude Enterprise is the primary coding-assistant source. Watch out for One accepted edit or write each, a smaller unit than a Copilot acceptance, so the two are priced at separate minutes saved, never added up. It covers Claude Code on Claude Enterprise seats only. If your developers also use Claude Code through an Anthropic Console organization, Roiva reads one of the two, not both. Good for Valuing Claude Code's share of the time saved where a team on Claude Enterprise runs it beside GitHub Copilot, each at its own minutes saved per acceptance. Read the guide	count	acceptances	Claude Enterprise
claude_enterprise.claude_code.active_users_avg	Members who used Claude Code on an average day in the month — members active only in other Claude products are not counted. Read as engineering.coding_assistant.active_users when Claude Enterprise is the primary coding-assistant source.	average	users	Claude Enterprise
claude_enterprise.claude_code.lines_accepted	Lines of code Claude Code added across the Claude Enterprise organization in the month. Read as engineering.coding_assistant.lines_accepted when Claude Enterprise is the primary coding-assistant source.	count	lines	Claude Enterprise
claude_enterprise.claude_code.proposals	Tool actions Claude Code proposed across the Claude Enterprise organization in the month, accepted and rejected together. Read as engineering.coding_assistant.suggestions when Claude Enterprise is the primary coding-assistant source.	count	suggestions	Claude Enterprise

Cloud

Key	What it measures	Type	Unit	Fed by
cloud.cost.budget_amount	Cloud budget ceiling for the period, across providers.	currency	USD	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
cloud.cost.forecasted_spend	Forecast end-of-period cloud spend based on the current usage trajectory.	currency	USD	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
cloud.cost.total_spend	Total cloud infrastructure spend for the period on the primary provider.	currency	USD	AWS, Azure, Databricks, Google Cloud, Snowflake
cloud.resources.idle_share	Percentage of provisioned cloud resources running at low or zero utilization.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
cloud.usage.unit_count	What your cloud bill grows with (customers, orders, transactions or requests) in the period, counted the same way every period.	count	units	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)

Customer success

Key	What it measures	Type	Unit	Fed by
customer_success.accounts.csm_count	Customer success managers covering the accounts.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
customer_success.accounts.per_csm	Customer accounts managed per customer success manager.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
customer_success.renewals.arr_up_for_renewal	Annual recurring revenue from accounts up for renewal in the period.	currency	USD	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
customer_success.renewals.rate	Percentage of customers up for renewal who renewed.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
customer_success.revenue.net_retention_rate	Revenue retained from existing customers including expansion, as a percentage of the starting base.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)

Freshdesk

Key	What it measures	Type	Unit	Fed by
freshdesk.tickets.agent_resolved_count	Freshdesk's resolved and closed tickets for the month that no AI deflection rule on the connection matched, plus the ones a rule matched whose customer came back. Read as support.tickets.agent_resolved_count when this connection is the primary source.	count	tickets	Freshdesk
freshdesk.tickets.avg_first_response_hours	Freshdesk's average ticket first response time for the month. Written to support.tickets.avg_first_response_hours instead when this connection is the primary source.	duration	hours	Freshdesk
freshdesk.tickets.avg_resolution_days	Freshdesk's average time from a ticket's creation to its resolution for the month, in days (the computer converts from hours). Read as support.tickets.avg_resolution_days when this connection is the primary source.	duration	days	Freshdesk
freshdesk.tickets.resolved_count	Freshdesk's support tickets closed for the month. Written to support.tickets.closed_count instead when this connection is the primary source.	count	tickets	Freshdesk
freshdesk.tickets.volume	Freshdesk's support tickets opened for the month. Written to support.tickets.volume instead when this connection is the primary source.	count	tickets	Freshdesk

Snowflake

Key	What it measures	Type	Unit	Fed by
snowflake.compute.credits_used	Snowflake warehouse credits consumed in the month.	count	credits	Snowflake
snowflake.cost.service_spend	The month's Snowflake bill split by usage type — compute, storage, cloud services and the rest — with Cortex AI under the service type it bills by (AI_FUNCTIONS, CORTEX_AGENTS and so on), the line in the observation's dimensions. What a cost link's service mappings match.	currency	USD	Snowflake
snowflake.cost.total_spend	What Snowflake billed the account for the month, at its own contract rates, excluding usage Snowflake did not charge for. Read as cloud.cost.total_spend when this connection is the primary cloud cost source.	currency	USD	Snowflake
snowflake.query.avg_execution_seconds	Mean execution time of Snowflake queries in the month.	duration	seconds	Snowflake
snowflake.query.count	Successful Snowflake queries in the month.	count	queries	Snowflake

Workflow automation

Key	What it measures	Type	Unit	Fed by
automation.runs.count	Runs of automated workflows that finished in the period, successful and failed.	count	runs	Make, Zapier, n8n
automation.runs.failed_count	Runs of automated workflows that failed in the period.	count	runs	Make, Zapier, n8n
automation.runs.manual_minutes_per_run	Minutes a person took to do the task one successful automated run now does, from a time study or your own timing. One figure for the initiative's workflows, so a study samples the tasks as often as each one runs.	duration	minutes	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
automation.runs.successful_count	Runs of automated workflows that succeeded in the period.	count	runs	Make, Zapier, n8n

Calendar

Key	What it measures	Type	Unit	Fed by
calendar.meetings.avg_attendees	Average attendees per meeting in the period on the calendars this connection syncs.	average	people	Google Workspace, Microsoft 365
calendar.meetings.avg_duration_mins	Average length in minutes of a meeting on the calendars this connection syncs.	duration	minutes	Google Workspace, Microsoft 365
calendar.meetings.count	Meetings in the period on the calendars this connection syncs — confirmed, timed events with two or more attendees. A meeting on more than one of those calendars counts once.	count	meetings	Google Workspace, Microsoft 365
calendar.meetings.hours	Hours of meetings in the period on the calendars this connection syncs, each meeting counted once however many attend and however many of those calendars it sits on.	duration	hours	Google Workspace, Microsoft 365

Google Workspace

Key	What it measures	Type	Unit	Fed by
google_workspace.calendar.avg_attendees	Average attendees per meeting on the Google calendars this connection syncs, for the month. Read as calendar.meetings.avg_attendees when this connection is the primary calendar source.	average	people	Google Workspace
google_workspace.calendar.avg_meeting_duration_mins	Average meeting length in minutes on the Google calendars this connection syncs, for the month. Read as calendar.meetings.avg_duration_mins when this connection is the primary calendar source.	duration	minutes	Google Workspace
google_workspace.calendar.meeting_count	Meetings on the Google calendars this connection syncs, for the month, a meeting on more than one of them counted once. Read as calendar.meetings.count when this connection is the primary calendar source.	count	meetings	Google Workspace
google_workspace.calendar.meeting_hours	Hours of meetings on the Google calendars this connection syncs, for the month, each meeting counted once. Read as calendar.meetings.hours when this connection is the primary calendar source.	duration	hours	Google Workspace

Make

Key	What it measures	Type	Unit	Fed by
make.scenarios.credits	Make credits one scenario used in the month, which is what Make bills. Standard modules use 1 credit per operation; Make's built-in AI features add credits for tokens. The scenario is in the observation's dimensions.	count	credits	Make
make.scenarios.executions	Total runs of one Make scenario in the month; the scenario is in the observation's dimensions. Read as automation.runs.count, every scenario's row added, when this connection is the primary workflow-automation source.	count	runs	Make
make.scenarios.failed_executions	Failed runs of one Make scenario in the month; the scenario is in the observation's dimensions. Read as automation.runs.failed_count, every scenario's row added, when this connection is the primary workflow-automation source.	count	runs	Make
make.scenarios.successful_executions	Successful runs of one Make scenario in the month; the scenario is in the observation's dimensions. Read as automation.runs.successful_count, every scenario's row added, when this connection is the primary workflow-automation source.	count	runs	Make

Microsoft 365

Key	What it measures	Type	Unit	Fed by
microsoft_365.calendar.avg_attendees	Average attendees per meeting on the Outlook calendars this connection syncs, for the month. Read as calendar.meetings.avg_attendees when this connection is the primary calendar source.	average	people	Microsoft 365
microsoft_365.calendar.avg_meeting_duration_mins	Average meeting length in minutes on the Outlook calendars this connection syncs, for the month. Read as calendar.meetings.avg_duration_mins when this connection is the primary calendar source.	duration	minutes	Microsoft 365
microsoft_365.calendar.meeting_count	Meetings on the Outlook calendars this connection syncs, for the month, a meeting on more than one of them counted once. Read as calendar.meetings.count when this connection is the primary calendar source.	count	meetings	Microsoft 365
microsoft_365.calendar.meeting_hours	Hours of meetings on the Outlook calendars this connection syncs, for the month, each meeting counted once. Read as calendar.meetings.hours when this connection is the primary calendar source.	duration	hours	Microsoft 365

Work

Key	What it measures	Type	Unit	Fed by
work.issues.cycle_time_avg	Average hours from an issue's start to its resolution, over the issues resolved in the period.	duration	hours	Jira
work.issues.minutes_per_issue	Minutes of triage, coordination and hand-off one resolved issue takes, from a time study or your own timing. Not how long the issue was open.	duration	minutes	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
work.issues.resolved_count	Issues resolved in the period in the team's issue tracker.	count	issues	Jira
work.issues.story_points_completed	Story points on the issues resolved in the period.	count	points	Jira

AI assistants

Key	What it measures	Type	Unit	Fed by
ai_assistant.seats.licensed	People holding a license for a workplace AI assistant, as of the period's latest report. Shown beside active users for adoption; never priced.	count	users	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
ai_assistant.tasks.minutes_per_task	Hands-on minutes one of the tasks a time study timed takes, without the AI assistant before it and with it now. A time study on this key records both.	duration	minutes	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
ai_assistant.users.monthly_active	People who used a workplace AI assistant at least once in the period, each counted once however often they used it.	count	users	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)

BigQuery

Key	What it measures	Type	Unit	Fed by
bigquery.jobs.bytes_processed	Bytes processed by BigQuery jobs in the month.	count	bytes	BigQuery
bigquery.jobs.count	BigQuery jobs run in the month.	count	jobs	BigQuery
bigquery.jobs.slot_ms	Slot milliseconds consumed by BigQuery jobs in the month.	count	slot-ms	BigQuery

Google Cloud

Key	What it measures	Type	Unit	Fed by
gcp.billing.budget_amount	The amount of one GCP budget for the month; the budget is in the row's raw payload.	currency	USD	Google Cloud
gcp.billing.budget_spend	Spend against one GCP budget for the month, before tax; the budget is in the row's raw payload. Feeds cost entries through the initiative's budget mappings.	currency	USD	Google Cloud
gcp.billing.total_spend	Google Cloud's total billed spend for the month.	currency	USD	Google Cloud

Jira

Key	What it measures	Type	Unit	Fed by
jira.issues.cycle_time_avg	Jira's average issue cycle time for the month. Read as work.issues.cycle_time_avg when Jira is the primary issue-tracking source.	duration	—	Jira
jira.issues.resolved_count	Jira's issues resolved in the month. Read as work.issues.resolved_count when Jira is the primary issue-tracking source.	count	—	Jira
jira.issues.story_points_completed	Jira's issues story points completed for the month. Read as work.issues.story_points_completed when Jira is the primary issue-tracking source.	count	—	Jira

n8n

Key	What it measures	Type	Unit	Fed by
n8n.workflows.executions	Production executions of one n8n workflow that started in the month and finished, successful plus failed. Counted as n8n's Insights counts them, except evaluation runs: no manual test runs or sub-workflow runs, and a retry counts as an execution of its own. The workflow is in the observation's dimensions. Read as automation.runs.count, every workflow's row added, when this connection is the primary workflow-automation source.	count	executions	n8n
n8n.workflows.failed_executions	Production executions of one n8n workflow in the month that ended in error or crashed, including runs a later retry fixed; the workflow is in the observation's dimensions. Read as automation.runs.failed_count, every workflow's row added, when this connection is the primary workflow-automation source.	count	executions	n8n
n8n.workflows.successful_executions	Production executions of one n8n workflow in the month that succeeded, on the first try or a retry; the workflow is in the observation's dimensions. Read as automation.runs.successful_count, every workflow's row added, when this connection is the primary workflow-automation source.	count	executions	n8n

OpenAI Codex

Key	What it measures	Type	Unit	Fed by
openai_codex.usage.lines_accepted	Lines of code Codex added that developers kept, for the month. Read as engineering.coding_assistant.lines_accepted when Codex is the primary coding-assistant source. Codex reports no count of accepted suggestions, so it feeds none of the other coding-assistant keys.	count	lines	OpenAI Codex
openai_codex.usage.lines_committed	Lines attributed to Codex that reached a commit, for the month — the stronger of the two line counts, but one the other assistants do not report, so it stays a platform key.	count	lines	OpenAI Codex
openai_codex.usage.turns	Codex turns in the workspace for the month. Stands in for an active-developer count, which exists only in per-developer rows Roiva deliberately does not request.	count	turns	OpenAI Codex

Zapier

Key	What it measures	Type	Unit	Fed by
zapier.zaps.failed_runs	Failed Zap runs reported to Roiva's Zapier webhook in the month. Read as automation.runs.failed_count when this connection is the primary workflow-automation source.	count	runs	Zapier
zapier.zaps.runs	Total Zap runs reported to Roiva's Zapier webhook in the month. Read as automation.runs.count when this connection is the primary workflow-automation source.	count	runs	Zapier
zapier.zaps.successful_runs	Successful Zap runs reported to Roiva's Zapier webhook in the month. Read as automation.runs.successful_count when this connection is the primary workflow-automation source.	count	runs	Zapier

AWS

Key	What it measures	Type	Unit	Fed by
aws.cost.service_spend	One AWS service's unblended cost for the month; the service is in the observation's dimensions. Feeds cost entries through the initiative's service mappings.	currency	USD	AWS
aws.cost.total_spend	AWS's total spend for the month.	currency	USD	AWS

Azure

Key	What it measures	Type	Unit	Fed by
azure.cost.service_spend	One Azure service's cost for the month; the service is in the observation's dimensions. Feeds cost entries through the initiative's service mappings.	currency	USD	Azure
azure.cost.total_spend	Azure's total spend for the month.	currency	USD	Azure

Databricks

Key	What it measures	Type	Unit	Fed by
databricks.cost.product_spend	Databricks list-price spend for the month on one product (billing_origin_product: MODEL_SERVING, JOBS, SQL, ...).	currency	USD	Databricks
databricks.cost.total_spend	Databricks list-price spend for the month, from system.billing.usage priced at system.billing.list_prices. Read as cloud.cost.total_spend when this connection is the primary cloud cost source.	currency	USD	Databricks

Workforce

Key	What it measures	Type	Unit	Fed by
workforce.tasks.manual_per_week	Discrete manual tasks (data pulls, formatting, distribution) performed each week.	count	tasks	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)
workforce.time.hours_saved_per_week	Net staff hours recovered per week as a direct result of the initiative.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks, Snowflake or SQL Server)

American Express

Key	What it measures	Type	Unit	Fed by
amex.card.spend	What the American Express statements you uploaded charged in the month, in USD, for a month they cover end to end. A card is a payment rail rather than a provider billing for its own service, so this is not counted in provider-billed AI spend - a charge to a connected provider is already in that provider's own bill.	currency	USD	American Express

Brex

Key	What it measures	Type	Unit	Fed by
brex.card.spend	What the Brex card was charged in the month, in USD. A card is a payment rail rather than a provider billing for its own service, so this is not counted in provider-billed AI spend - a charge to a connected provider is already in that provider's own bill.	currency	USD	Brex (coming soon)

Looker

Key	What it measures	Type	Unit	Fed by
looker.looks.field_value	The monthly value of one numeric column of one Look; the Look and the column are in the observation's dimensions. Roiva cannot infer a unit from a Look, so the type is count unless a person overrides the definition.	count	—	Looker

Ramp

Key	What it measures	Type	Unit	Fed by
ramp.card.spend	What the Ramp card was charged in the month, in USD. A card is a payment rail rather than a provider billing for its own service, so this is not counted in provider-billed AI spend - a charge to a connected provider is already in that provider's own bill.	currency	USD	Ramp (coming soon)

Metric registry

CRM