Formula library
Every value formula Roiva ships: what it calculates, its arithmetic, and the reading, baseline or rate each input comes from.
Answered from these docs only, by a model that cannot see your account. Check the pages it cites.
Generated from what Roiva ships, on every deploy.
The formulas an initiative template brings with it. Adopting a template clones these onto the initiative, where the account can change them; a clone nobody has touched follows the library when the library changes.
Formulas that suit any industry come first, grouped by the function they serve. The rest are written for one industry and grouped by it.
Counts as says which of Roiva's two figures a formula's value lands in. Cash is revenue gained, costs saved and costs avoided, and is what ROI is taken over. Freed capacity is people's time priced at what it costs: a formula that reads a labor rate, or a cost per ticket or per item that is mostly staff time, books freed capacity, and Roiva will not save it as cash. Freed capacity is reported beside cash and is in no ROI until a person shows, with a document, that it became cash. How a figure is classed and when capacity becomes cash is in How Roiva calculates value.
A formula that values added revenue books it at your contribution margin, what is left of each dollar after the costs that grow with that revenue, and records it as revenue gained. Roiva ships no default margin: the formula waits until you set one, for the account or for the initiative alone, and each formula's margin input says which costs grow with the revenue it values.
A formula that values a change in a business-wide outcome, such as a loss ratio, a churn rate or a win rate, multiplies it by the share of the change you credit to the initiative. That share is your judgment, not a measurement: Roiva ships no default for it and does not measure what caused the change, so the formula waits until someone on the initiative sets it, and every entry that reads it waits for a person's approval.
What a formula reads, when it runs and what its result has to clear before it counts is in How Roiva calculates value.
Value that isn't a formula
Not every dollar of value comes from this library. A value source link books what its connection syncs: won deals from HubSpot, Salesforce or Pipedrive, payments from Stripe, and invoices from QuickBooks, Xero or NetSuite, each from the amounts the platform records, in their own currency, and booked at your contribution margin. A total counts them in dollars at the European Central Bank's rate. Tickets an AI agent resolves in Zendesk, Freshdesk or Intercom, and conversations an Agentforce agent or Einstein Bot handles alone in Salesforce, are priced one by one by that connection's sync, so there is no formula for them here. The last table lists what that sync multiplies. See How deflection value is booked.
Linking GitHub, Jira, n8n, Make or Zapier as a value source adds a freed-capacity formula to that initiative. Those formulas aren't part of a template, so they aren't listed here either. Linking connections to initiatives says what each one counts.
Engineering
| Formula | Counts as | Arithmetic | Inputs |
|---|---|---|---|
| AI Cost per Million Output Tokens ReductionMeasures the spend avoided when each million output tokens costs less, all in, than it did before optimization — for a provider that reports tokens but no request count, such as Anthropic. Output tokens in the period × the drop in spend per million output tokens, with the current figure read straight from the provider's usage and spend. | Cash: cost savings | ai_output_tokens / 1000000 * (baseline_ai_cost_per_million_output_tokens_usd - current_ai_cost_per_million_output_tokens_usd) * 100 | ai_output_tokens — reading of ai.usage.output_tokenscurrent_ai_cost_per_million_output_tokens_usd — reading of ai.cost.per_million_output_tokensbaseline_ai_cost_per_million_output_tokens_usd — baseline of ai.cost.per_million_output_tokens |
| AI Cost per Request ReductionMeasures the spend avoided when each AI request costs less than it did before optimization. Requests in the period × the drop in cost per request, with the current cost per request read straight from the provider's usage and spend. | Cash: cost savings | ai_requests * (baseline_ai_cost_per_request_usd - current_ai_cost_per_request_usd) * 100 | ai_requests — reading of ai.usage.request_countcurrent_ai_cost_per_request_usd — reading of ai.cost.per_requestbaseline_ai_cost_per_request_usd — baseline of ai.cost.per_request |
| Asset Audit & Reconciliation Freed CapacityValues the IT team time freed by replacing manual software inventory audits and license reconciliation with automated discovery. (Hours per audit before − after) × the audits in the period (a year's audits spread evenly over its months) × labor rate. | Freed capacity | (baseline_audit_hours_per_cycle - current_audit_hours_per_cycle) * audit_cycles_per_year * months_in_period / 12 * labor_rate_cents_per_hour | months_in_periodaudit_cycles_per_year — assumptionlabor_rate_cents_per_hour — assumptioncurrent_audit_hours_per_cycle — reading of it.audits.hours_per_cyclebaseline_audit_hours_per_cycle — baseline of it.audits.hours_per_cycle |
| Breach Exposure Cost AvoidanceEstimates the expected breach cost avoided by reducing mean time to detect. Breaches that are detected faster have materially lower total costs — earlier containment limits data exfiltration, regulatory exposure, and remediation scope. Incidents in the period × hours of exposure saved per incident × cost per hour of exposure. | Cash: cost avoidance | incidents_in_period * (baseline_mttd_hours - current_mttd_hours) * avg_breach_cost_per_hour_of_exposure_cents | current_mttd_hours — reading of it.security.mean_time_to_detect_hoursbaseline_mttd_hours — baseline of it.security.mean_time_to_detect_hoursincidents_in_period — reading of it.security.incident_countavg_breach_cost_per_hour_of_exposure_cents — assumption, with no default |
| Cloud Spend per Subscription ReductionMeasures the reduction in cloud infrastructure spend from rightsizing, reserved capacity, and waste elimination, for a subscription business whose bill grows with its subscriber base. Spend is compared per active subscription, read from billing, so a growing base is not read as waste nor a shrinking one as a saving: the fall in cloud cost per subscription × this period's active subscriptions. A shift in plan mix, toward a few large accounts or many small ones, moves cost per subscription too. | Cash: cost savings | (baseline_cloud_cost_per_subscription_usd - current_cloud_cost_per_subscription_usd) * current_subscriptions * 100 | current_subscriptions — reading of billing.subscriptions.active_countbaseline_subscriptions — baseline of billing.subscriptions.active_countcurrent_monthly_cloud_spend_usd — reading of cloud.cost.total_spendbaseline_monthly_cloud_spend_usd — baseline of cloud.cost.total_spendcurrent_cloud_cost_per_subscription_usdbaseline_cloud_cost_per_subscription_usd |
| Cloud Spend per Transaction ReductionMeasures the reduction in cloud infrastructure spend from rightsizing, reserved capacity, and waste elimination, for a business whose bill grows with the payments it takes. Spend is compared per payment charge, read from billing, so a busy month is not read as waste nor a quiet one as a saving: the fall in cloud cost per transaction × this period's transactions. Billing counts the charges in the currency the account settles in, so a shift in how much is sold in other currencies moves cost per transaction too. | Cash: cost savings | (baseline_cloud_cost_per_transaction_usd - current_cloud_cost_per_transaction_usd) * current_transactions * 100 | current_transactions — reading of billing.charges.countbaseline_transactions — baseline of billing.charges.countcurrent_monthly_cloud_spend_usd — reading of cloud.cost.total_spendbaseline_monthly_cloud_spend_usd — baseline of cloud.cost.total_spendcurrent_cloud_cost_per_transaction_usdbaseline_cloud_cost_per_transaction_usd |
| Deployment Engineering Freed CapacityValues the engineering time freed by automating deployment pipelines, testing, and rollout processes. Deployments × hours saved per deployment × engineering labor rate. | Freed capacity | deployments_per_period * (baseline_hours_per_deployment - current_hours_per_deployment) * labor_rate_cents_per_hour | deployments_per_period — reading of it.deployments.countlabor_rate_cents_per_hour — assumptioncurrent_hours_per_deployment — reading of it.deployments.hours_per_deploymentbaseline_hours_per_deployment — baseline of it.deployments.hours_per_deployment |
| Developer Capacity Freed by an AI Coding AssistantValues the developer time an AI coding assistant saves: suggestions developers accepted in the period × minutes saved per acceptance × the fully loaded developer hourly rate. Acceptances come from whichever assistant reports them; the minutes and the rate are Assumptions an account can override. | Freed capacity | acceptances * minutes_per_acceptance * developer_hourly_rate / 60 | acceptances — reading of engineering.coding_assistant.acceptancesdeveloper_hourly_rate — assumptionminutes_per_acceptance — assumption |
| Developer Capacity Freed by an AI Coding Assistant (by lines of code)Values the developer time an AI coding assistant saves where the assistant reports no acceptance count: lines of code it wrote that developers kept in the period × minutes saved per line × the fully loaded developer hourly rate. For an assistant that does count acceptances, the acceptances formula is the better measure and an initiative gets that one instead. | Freed capacity | lines_accepted * minutes_per_line * developer_hourly_rate / 60 | lines_accepted — reading of engineering.coding_assistant.lines_acceptedminutes_per_line — assumptiondeveloper_hourly_rate — assumption |
| Developer Capacity Freed by GitHub Copilot and Claude CodeValues the developer time two coding assistants save together, each in its own unit: Copilot acceptances × minutes saved per Copilot acceptance, plus Claude Code accepted edits × minutes saved per Claude Code edit, at the fully loaded developer hourly rate. The two are priced separately because a Copilot acceptance can bundle several completions while a Claude Code one is a single edit. | Freed capacity | (copilot_acceptances * minutes_per_copilot_acceptance + claude_code_acceptances * minutes_per_claude_code_acceptance) * developer_hourly_rate / 60 | copilot_acceptances — reading of github.copilot.suggestions_accepteddeveloper_hourly_rate — assumptionclaude_code_acceptances — reading of anthropic.claude_code.acceptancesminutes_per_copilot_acceptance — assumptionminutes_per_claude_code_acceptance — assumption |
| Developer Capacity Freed by GitHub Copilot and Claude Code on Claude EnterpriseValues the developer time two coding assistants save together, each in its own unit: Copilot acceptances × minutes saved per Copilot acceptance, plus edits accepted in Claude Code across the Claude Enterprise organization × minutes saved per Claude Code edit, at the fully loaded developer hourly rate. The two are priced separately because a Copilot acceptance can bundle several completions while a Claude Code one is a single edit. | Freed capacity | (copilot_acceptances * minutes_per_copilot_acceptance + claude_code_acceptances * minutes_per_claude_code_acceptance) * developer_hourly_rate / 60 | copilot_acceptances — reading of github.copilot.suggestions_accepteddeveloper_hourly_rate — assumptionclaude_code_acceptances — reading of claude_enterprise.claude_code.acceptancesminutes_per_copilot_acceptance — assumptionminutes_per_claude_code_acceptance — assumption |
| Direct Cloud Spend ReductionMeasures the reduction in cloud infrastructure spend from rightsizing, reserved capacity, and waste elimination. Spend is compared per unit of the demand the bill scales with (requests, transactions or active customers, counted the same way every period), so growth in usage is not read as waste nor a slow month as a saving: the fall in cloud cost per unit × this period's units. | Cash: cost savings | (baseline_cloud_cost_per_unit_usd - current_cloud_cost_per_unit_usd) * current_cloud_units * 100 | current_cloud_units — reading of cloud.usage.unit_countbaseline_cloud_units — baseline of cloud.usage.unit_countcurrent_cloud_cost_per_unit_usdcurrent_monthly_cloud_spend_usd — reading of cloud.cost.total_spendbaseline_cloud_cost_per_unit_usdbaseline_monthly_cloud_spend_usd — baseline of cloud.cost.total_spend |
| End-User Downtime Freed CapacityValues the working time end-users get back when IT issues are resolved faster. Incidents × days of resolution time cut × working hours per calendar day (the week's working hours ÷ 7, since resolution days are calendar days, nights and weekends included) × the share of that time affected users are actually blocked × users affected × the hourly cost of an end-user's time. Both resolution times are the help desk's average days from ticket creation to resolution, so the before-value is the account's own months before the initiative. The blocked share has no default: a ticket open for days rarely stops anyone working for all of it, and only the account can say how much it does. | Freed capacity | incidents_per_period * (baseline_resolution_days - current_resolution_days) * working_hours_per_week / 7 * blocked_share_decimal * avg_affected_users_per_incident * avg_hourly_productivity_value_cents | incidents_per_period — reading of support.tickets.volumeblocked_share_decimal — the share of the time users were blocked, with no defaultworking_hours_per_week — assumptioncurrent_resolution_days — reading of support.tickets.avg_resolution_daysbaseline_resolution_days — baseline of support.tickets.avg_resolution_daysavg_affected_users_per_incident — reading of it.helpdesk.avg_users_per_incidentavg_hourly_productivity_value_cents — assumption, with no default |
| Failed Deployment Incident Cost ReductionEstimates the cost avoided when a lower change failure rate means fewer production incidents. Deployments × reduction in failure rate × average cost to investigate and remediate one production incident. | Cash: cost avoidance | deployments_per_period * (baseline_change_failure_rate_pct - current_change_failure_rate_pct) / 100 * avg_incident_cost_cents | deployments_per_period — reading of it.deployments.countavg_incident_cost_cents — assumption, with no defaultcurrent_change_failure_rate_pct — reading of it.deployments.change_failure_ratebaseline_change_failure_rate_pct — baseline of it.deployments.change_failure_rate |
| Incident Response Freed CapacityValues the security analyst time freed by automated detection and response playbooks. Faster containment means fewer analyst hours per incident. Incidents × hours saved per incident × blended SOC rate. | Freed capacity | incidents_per_period * (baseline_mttc_hours - current_mttc_hours) * labor_rate_cents_per_hour | current_mttc_hours — reading of it.security.mean_time_to_contain_hoursbaseline_mttc_hours — baseline of it.security.mean_time_to_contain_hoursincidents_per_period — reading of it.security.incident_countlabor_rate_cents_per_hour — assumption |
| Software License Waste EliminatedMonetizes the direct savings from eliminating unused, duplicate, or over-licensed software identified through automated asset discovery. Annual software spend × reduction in unused license percentage × the period's share of the year. | Cash: cost savings | annual_software_spend_usd * (baseline_unused_license_pct - current_unused_license_pct) / 100 * months_in_period / 12 * 100 | months_in_periodannual_software_spend_usd — baseline of it.software.annual_spendcurrent_unused_license_pct — reading of it.software.unused_license_sharebaseline_unused_license_pct — baseline of it.software.unused_license_share |
Finance
| Formula | Counts as | Arithmetic | Inputs |
|---|---|---|---|
| Adjustment & Restatement Rework Freed CapacityValues the correction work freed by fewer revenue recognition errors. Adjustments are compared per invoice issued, so a heavier billing month is not read as more errors: the fall in adjustments per invoice × this period's invoices × average fully-loaded cost to identify, correct, and document one recognition adjustment. | Freed capacity | (baseline_adjustments_per_invoice - current_adjustments_per_invoice) * current_invoices * avg_adjustment_cost_cents | current_invoices — reading of finance.invoices.countbaseline_invoices — baseline of finance.invoices.countcurrent_adjustment_count — reading of finance.revenue_recognition.adjustment_countavg_adjustment_cost_centsbaseline_adjustment_count — baseline of finance.revenue_recognition.adjustment_countlabor_rate_cents_per_hour — assumptionbaseline_minutes_per_adjustment — baseline of finance.revenue_recognition.minutes_per_adjustmentcurrent_adjustments_per_invoicebaseline_adjustments_per_invoice |
| Billing Dispute Credit & Write-Off AvoidanceValues the money fewer billing errors keep: credits granted to settle a dispute, and invoiced amounts written off or never collected because of one. Invoices sent × reduction in the billing error rate × average credits and write-offs per billing error. Correcting an overcharge isn't counted, since that money was never owed. The staff time a dispute takes is freed capacity, in Dispute Resolution Freed Capacity. | Cash: cost avoidance | invoices_sent * (baseline_error_rate_pct - current_error_rate_pct) / 100 * avg_credit_write_off_per_billing_error_cents | invoices_sent — reading of finance.invoices.countcurrent_error_rate_pct — reading of finance.invoices.error_ratebaseline_error_rate_pct — baseline of finance.invoices.error_rateavg_credit_write_off_per_billing_error_cents — assumption, with no default |
| Cash Collection Acceleration ValueEstimates the working capital value of resolving disputes faster. Disputed invoices represent delayed cash. Reduction in dispute resolution days × volume of disputed invoices × average invoice value × annual cost of capital ÷ 365. | Cash: cost savings | invoices_sent * (baseline_error_rate_pct / 100) * avg_invoice_value_cents * (baseline_dispute_days - current_dispute_days) * annual_cost_of_capital_decimal / 365 | invoices_sent — reading of finance.invoices.countinvoices_volume_usd — reading of finance.invoices.volumecurrent_dispute_days — reading of finance.disputes.avg_resolution_daysbaseline_dispute_days — baseline of finance.disputes.avg_resolution_daysavg_invoice_value_centsbaseline_error_rate_pct — baseline of finance.invoices.error_rateannual_cost_of_capital_decimal — assumption |
| Close Cycle Compression Freed CapacityEstimates the business value of a faster close. Each day of close cycle reduction allows management to act on financial data sooner and eases the month-end crunch. Days reduced × the close cycles in the period (a year's cycles spread evenly over its months) × estimated daily cost of delayed reporting. | Freed capacity | (baseline_close_days - current_close_days) * close_cycles_per_year * months_in_period / 12 * daily_cost_of_delayed_reporting_cents | months_in_periodcurrent_close_days — reading of finance.close.cycle_daysbaseline_close_days — baseline of finance.close.cycle_daysclose_cycles_per_year — assumptiondaily_cost_of_delayed_reporting_cents — assumption, with no default |
| Dispute Resolution Freed CapacityValues the finance staff time freed when fewer invoices go out wrong: fewer disputes to handle, customer calls, credit memos to process and correction cycles. Invoices sent × reduction in the billing error rate × the staff time to resolve one billing dispute, at what that time costs. Credits and write-offs a dispute ends in are cash, in Billing Dispute Credit & Write-Off Avoidance. | Freed capacity | invoices_sent * (baseline_error_rate_pct - current_error_rate_pct) / 100 * avg_dispute_staff_cost_cents | invoices_sent — reading of finance.invoices.countcurrent_error_rate_pct — reading of finance.invoices.error_ratebaseline_error_rate_pct — baseline of finance.invoices.error_ratelabor_rate_cents_per_hour — assumptionavg_dispute_staff_cost_centsbaseline_minutes_per_dispute — baseline of finance.invoices.minutes_per_dispute |
| Duplicate & Overpayment Loss AvoidanceValues the money fewer vendor bill errors keep from going out: duplicate and overpayments that would never have come back, what recovering the rest would have cost, and late fees on bills held for correction. Vendor bills × reduction in the bill error rate × average cash lost per bill error. The AP time an error takes is freed capacity, in Invoice Error & Rework Freed Capacity; this counts only the money. | Cash: cost avoidance | invoices_processed * (baseline_error_rate_pct - current_error_rate_pct) / 100 * avg_cash_lost_per_bill_error_cents | invoices_processed — reading of finance.bills.countcurrent_error_rate_pct — reading of finance.bills.error_ratebaseline_error_rate_pct — baseline of finance.bills.error_rateavg_cash_lost_per_bill_error_cents — assumption, with no default |
| Forecast Cycle Freed CapacityValues the analyst time freed by automating the forecast production process. The hours are priced at what they cost. | Freed capacity | (baseline_cycle_hours - current_cycle_hours) * forecast_runs_per_period * blended_labor_rate_cents_per_hour | current_cycle_hours — reading of operations.forecasting.hours_per_cyclebaseline_cycle_hours — baseline of operations.forecasting.hours_per_cycleforecast_runs_per_period — reading of operations.forecasting.cyclesblended_labor_rate_cents_per_hour — assumption |
| Inventory Cost AvoidanceEstimates the carrying cost and write-off reduction from holding less excess inventory due to improved forecast accuracy. Excess is compared as a share of the inventory on hand, so stocking up for a bigger business or a peak season is not read as worse forecasting: the fall in the excess share × this period's inventory × the period's share of the annual holding cost rate. | Cash: cost savings | (baseline_excess_share_decimal - current_excess_share_decimal) * current_inventory_usd * holding_cost_rate_decimal * months_in_period / 12 * 100 | months_in_periodcurrent_inventory_usd — reading of supply_chain.inventory.avg_valuebaseline_inventory_usd — baseline of supply_chain.inventory.avg_valueholding_cost_rate_decimal — assumptioncurrent_excess_inventory_usd — reading of supply_chain.inventory.excess_valuecurrent_excess_share_decimalbaseline_excess_inventory_usd — baseline of supply_chain.inventory.excess_valuebaseline_excess_share_decimal |
| Invoice Error & Rework Freed CapacityValues the AP staff time freed when fewer vendor bills are entered wrong: less time catching, investigating and correcting errors and reconciling them afterwards. Vendor bills × reduction in the bill error rate × the staff time to correct one error, at what that time costs. The error rate is AP's, on the bills it enters — not the billing error rate on invoices the company sends. Money an error lets out of the door, such as a duplicate payment that never comes back, is cash, in Duplicate & Overpayment Loss Avoidance. | Freed capacity | invoices_processed * (baseline_error_rate_pct - current_error_rate_pct) / 100 * avg_error_staff_cost_cents | invoices_processed — reading of finance.bills.countcurrent_error_rate_pct — reading of finance.bills.error_ratebaseline_error_rate_pct — baseline of finance.bills.error_ratelabor_rate_cents_per_hour — assumptionavg_error_staff_cost_centsbaseline_minutes_per_error — baseline of finance.bills.minutes_per_error |
| Invoice Processing Freed CapacityValues the AP team time freed by automating invoice extraction, validation, and 3-way PO matching. Invoice volume × minutes saved per invoice × blended AP labor rate. | Freed capacity | invoices_processed * (baseline_minutes_per_invoice - current_minutes_per_invoice) * labor_rate_cents_per_minute | invoices_processed — reading of finance.bills.countlabor_rate_cents_per_hour — assumptioncurrent_minutes_per_invoice — reading of finance.ap.minutes_per_invoicelabor_rate_cents_per_minutebaseline_minutes_per_invoice — baseline of finance.ap.minutes_per_invoice |
| Reconciliation Freed CapacityValues the accounting staff time freed by automating account reconciliations and journal entries. Hours saved per close cycle × the close cycles in the period (a year's cycles spread evenly over its months) × blended accounting rate. | Freed capacity | (baseline_reconciliation_hours - current_reconciliation_hours) * close_cycles_per_year * months_in_period / 12 * labor_rate_cents_per_hour | months_in_periodclose_cycles_per_year — assumptionlabor_rate_cents_per_hour — assumptioncurrent_reconciliation_hours — reading of finance.reconciliation.hoursbaseline_reconciliation_hours — baseline of finance.reconciliation.hours |
| Revenue Recognition Freed CapacityValues the accounting team time freed by replacing manual ASC 606 schedule building with automated recognition logic. Hours are compared per invoice issued, so a heavier billing month is not read as slower accounting: the fall in hours per invoice × this period's invoices × accounting labor rate. | Freed capacity | (baseline_rev_rec_hours_per_invoice - current_rev_rec_hours_per_invoice) * current_invoices * labor_rate_cents_per_hour | current_invoices — reading of finance.invoices.countbaseline_invoices — baseline of finance.invoices.countcurrent_rev_rec_hours — reading of finance.revenue_recognition.manual_hoursbaseline_rev_rec_hours — baseline of finance.revenue_recognition.manual_hourslabor_rate_cents_per_hour — assumptioncurrent_rev_rec_hours_per_invoicebaseline_rev_rec_hours_per_invoice |
| Treasury Forecast Assembly Freed CapacityValues the treasury team time freed by replacing manual spreadsheet forecasting with AI-driven models. Hours saved per forecast cycle × cycles per period × treasury labor rate. | Freed capacity | (baseline_treasury_hours - current_treasury_hours) * forecast_cycles_per_period * labor_rate_cents_per_hour | current_treasury_hours — reading of finance.treasury.manual_hours_per_cyclebaseline_treasury_hours — baseline of finance.treasury.manual_hours_per_cyclelabor_rate_cents_per_hour — assumptionforecast_cycles_per_period — reading of operations.forecasting.cycles |
| Working Capital Optimization ValueEstimates the cost-of-capital benefit from better forecast accuracy. More accurate forecasts allow treasury to reduce idle cash, optimize short-term borrowing, and earn more on deployable liquidity. Forecast accuracy improvement × average cash balance × the period's share of the annual cost of capital. Only the share of that change you credit to this initiative counts: how volatile cash flows are, large one-off payments and customers' payment habits move forecast accuracy too. | Cash: cost savings | (current_forecast_accuracy_pct - baseline_forecast_accuracy_pct) / 100 * avg_cash_balance_usd * annual_cost_of_capital_decimal * months_in_period / 12 * 100 * attributable_share_decimal | months_in_periodavg_cash_balance_usd — reading of finance.treasury.avg_cash_balanceattributable_share_decimal — your share of the change, a judgment with no defaultcurrent_forecast_accuracy_pct — reading of operations.forecasting.accuracyannual_cost_of_capital_decimal — assumptionbaseline_forecast_accuracy_pct — baseline of operations.forecasting.accuracy |
Sales
| Formula | Counts as | Arithmetic | Inputs |
|---|---|---|---|
| At-Risk Deal Recovery ValueEstimates revenue recovered by identifying and saving at-risk deals earlier. Reduction in the at-risk share × the pipeline that moves through in the period × the close rate, the CRM's win rate on closed deals. The open pipeline is a snapshot that turns over once a sales cycle, so the part of it the period accounts for is its value × days in the period ÷ average sales cycle days; the whole of it would be counted again every month a deal stayed open. Only the share of that change you credit to this initiative counts: buyers' budgets, the economy and competitors move the at-risk share too. Booked at your contribution margin: the revenue less the costs that grow with it. | Cash: revenue gain | (baseline_at_risk_deal_pct - current_at_risk_deal_pct) / 100 * total_pipeline_value_usd * days_in_period / avg_sales_cycle_days * close_rate_decimal * 100 * attributable_share_decimal * contribution_margin_decimal | days_in_periodclose_rate_decimalavg_sales_cycle_days — reading of crm.deals.avg_sales_cycle_dayscrm_deals_win_rate_pct — reading of crm.deals.win_ratecurrent_at_risk_deal_pct — reading of crm.deals.at_risk_sharetotal_pipeline_value_usd — reading of crm.deals.pipeline_valuebaseline_at_risk_deal_pct — baseline of crm.deals.at_risk_shareattributable_share_decimal — your share of the change, a judgment with no defaultcontribution_margin_decimal — your contribution margin, with no default |
| Contract Processing Freed CapacityValues the legal, sales, and ops time freed by automating contract drafting, redlining, and approval routing. Contracts processed × hours saved × blended labor rate. | Freed capacity | contracts_processed * (baseline_hours_per_contract - current_hours_per_contract) * labor_rate_cents_per_hour | contracts_processed — reading of crm.contracts.processed_countlabor_rate_cents_per_hour — assumptioncurrent_hours_per_contract — reading of crm.contracts.hours_per_contractbaseline_hours_per_contract — baseline of crm.contracts.hours_per_contract |
| Deal Cycle Compression ValueEstimates what a shorter sales cycle is worth. A deal that closes sooner is the same deal arriving earlier, not added revenue, so what it is worth is the cost of capital on its margin for the days saved. Deals won × days the cycle shortened × average won deal value × annual cost of capital ÷ 365. Only the share of that change you credit to this initiative counts: buyers' budgets and approval processes, deal size mix and the economy move the sales cycle too. Valued at your contribution margin, because a deal that closes sooner brings the costs of delivering it forward too: only the margin arrives early. | Cash: cost savings | deals_closed * (baseline_cycle_days - current_cycle_days) * avg_deal_value_cents * annual_cost_of_capital_decimal / 365 * attributable_share_decimal * contribution_margin_decimal | deals_closed — reading of crm.deals.won_countcurrent_cycle_days — reading of crm.deals.avg_sales_cycle_daysbaseline_cycle_days — baseline of crm.deals.avg_sales_cycle_daysavg_deal_value_centscrm_deals_won_value_usd — reading of crm.deals.won_valueattributable_share_decimal — your share of the change, a judgment with no defaultcontribution_margin_decimal — your contribution margin, with no defaultannual_cost_of_capital_decimal — assumption |
| Faster Contract Signature ValueEstimates what removing the legal bottlenecks that hold up signature is worth. Each deal won in the period signed that many days earlier. A deal that closes sooner is the same deal arriving earlier, not added revenue, so what it is worth is the cost of capital on its margin for the days saved. Deals won × contract cycle days saved × average won deal value × annual cost of capital ÷ 365. Valued at your contribution margin, because a deal that closes sooner brings the costs of delivering it forward too: only the margin arrives early. | Cash: cost savings | deals_per_period * (baseline_contract_cycle_days - current_contract_cycle_days) * avg_deal_value_cents * annual_cost_of_capital_decimal / 365 * contribution_margin_decimal | deals_per_period — reading of crm.deals.won_countavg_deal_value_centscrm_deals_won_value_usd — reading of crm.deals.won_valuecontribution_margin_decimal — your contribution margin, with no defaultcurrent_contract_cycle_days — reading of crm.contracts.avg_cycle_daysbaseline_contract_cycle_days — baseline of crm.contracts.avg_cycle_daysannual_cost_of_capital_decimal — assumption |
| Faster Proposal ValueEstimates what sending proposals sooner is worth. Each deal won in the period closed that many days earlier. A deal that closes sooner is the same deal arriving earlier, not added revenue, so what it is worth is the cost of capital on its margin for the days saved. Deals won × days saved between demo and proposal × average won deal value × annual cost of capital ÷ 365. A proposal that did not win brought nothing forward, so proposals sent are not the count. Valued at your contribution margin, because a deal that closes sooner brings the costs of delivering it forward too: only the margin arrives early. | Cash: cost savings | deals_won * (baseline_days_to_proposal - current_days_to_proposal) * avg_deal_value_cents * annual_cost_of_capital_decimal / 365 * contribution_margin_decimal | deals_won — reading of crm.deals.won_countavg_deal_value_centscrm_deals_won_value_usd — reading of crm.deals.won_valuecurrent_days_to_proposal — reading of crm.deals.avg_demo_to_proposal_daysbaseline_days_to_proposal — baseline of crm.deals.avg_demo_to_proposal_dayscontribution_margin_decimal — your contribution margin, with no defaultannual_cost_of_capital_decimal — assumption |
| Forecast Accuracy Revenue ValueEstimates the business value of more accurate forecasts. Better accuracy allows resource allocation and incentive planning to be better calibrated — reducing the cost of forecast miss (over-investment in deals that slip, under-resourcing deals that could close). Revenue × improvement in accuracy × capture rate. Only the share of that change you credit to this initiative counts: how volatile demand is, large deals slipping and changes to the sales team move forecast accuracy too. | Cash: cost avoidance | (current_forecast_accuracy_pct - baseline_forecast_accuracy_pct) / 100 * quarterly_revenue_cents * forecast_miss_cost_rate_decimal * attributable_share_decimal | financials_revenue_usd — reading of finance.pnl.revenuequarterly_revenue_centsattributable_share_decimal — your share of the change, a judgment with no defaultcurrent_forecast_accuracy_pct — reading of operations.forecasting.accuracybaseline_forecast_accuracy_pct — baseline of operations.forecasting.accuracyforecast_miss_cost_rate_decimal — assumption, with no default |
| Lead Qualification Freed CapacityValues the sales rep time freed by replacing manual lead review with AI scoring. The hours are priced at what they cost. | Freed capacity | leads_scored * (baseline_minutes_per_lead - current_minutes_per_lead) * labor_rate_cents_per_minute | leads_scored — reading of crm.contacts.new_countcurrent_minutes_per_lead — reading of crm.leads.avg_qualify_minutesbaseline_minutes_per_lead — baseline of crm.leads.avg_qualify_minuteslabor_rate_cents_per_hour — assumptionlabor_rate_cents_per_minute |
| Pipeline Revenue from Higher ConversionEstimates incremental revenue from converting more new leads into sales-qualified leads. Uplift in the lead-to-SQL rate × new leads × average deal value × close rate. Both rates are SQLs ÷ new leads from the CRM, so the before-value is the account's own months before the initiative. Only the share of that change you credit to this initiative counts: lead mix, SDR capacity, how an SQL is defined and the market move the lead-to-SQL rate too. Booked at your contribution margin: the revenue less the costs that grow with it. | Cash: revenue gain | (current_lead_to_sql_rate_pct - baseline_lead_to_sql_rate_pct) / 100 * leads_scored * avg_deal_value_cents * close_rate_decimal * attributable_share_decimal * contribution_margin_decimal | leads_scored — reading of crm.contacts.new_countclose_rate_decimalcrm_deals_won_count — reading of crm.deals.won_countavg_deal_value_centscrm_contacts_sql_count — reading of crm.contacts.sql_countcrm_deals_win_rate_pct — reading of crm.deals.win_ratecrm_deals_won_value_usd — reading of crm.deals.won_valueattributable_share_decimal — your share of the change, a judgment with no defaultcontribution_margin_decimal — your contribution margin, with no defaultcurrent_lead_to_sql_rate_pctbaseline_lead_to_sql_rate_pct — baseline of crm.leads.sql_rate |
| Pipeline Revenue UpliftEstimates incremental pipeline value from routing more qualified leads to reps. Qualified leads are compared per inbound lead scored, so a campaign that doubles lead volume is not read as better qualification: the rise in qualified leads per lead scored × this period's leads scored × average deal value × close rate. Booked at your contribution margin: the revenue less the costs that grow with it. | Cash: revenue gain | (current_qualified_per_scored - baseline_qualified_per_scored) * scored_leads * avg_deal_value_cents * close_rate_decimal * contribution_margin_decimal | scored_leads — reading of crm.leads.scored_countqualified_leads — reading of crm.leads.qualified_countclose_rate_decimalcrm_deals_won_count — reading of crm.deals.won_countavg_deal_value_centsbaseline_scored_leads — baseline of crm.leads.scored_countcrm_deals_win_rate_pct — reading of crm.deals.win_ratecrm_deals_won_value_usd — reading of crm.deals.won_valuebaseline_qualified_leads — baseline of crm.leads.qualified_countcontribution_margin_decimal — your contribution margin, with no defaultcurrent_qualified_per_scoredbaseline_qualified_per_scored |
| Proposal Assembly Freed CapacityValues the rep and pre-sales time freed by automating proposal assembly. Proposals sent × hours saved per proposal × labor rate. | Freed capacity | proposals_sent * (baseline_hours_per_proposal - current_hours_per_proposal) * labor_rate_cents_per_hour | proposals_sent — reading of crm.proposals.sent_countlabor_rate_cents_per_hour — assumptioncurrent_hours_per_proposal — reading of crm.proposals.hours_per_proposalbaseline_hours_per_proposal — baseline of crm.proposals.hours_per_proposal |
| Sales Rep Screening Freed CapacityValues the rep time freed when AI scoring eliminates manual lead review. Volume of scored leads × time saved per lead × blended rep cost. | Freed capacity | leads_scored * (baseline_minutes_per_lead - current_minutes_per_lead) * labor_rate_cents_per_minute | leads_scored — reading of crm.contacts.new_countcurrent_minutes_per_lead — reading of crm.leads.avg_qualify_minutesbaseline_minutes_per_lead — baseline of crm.leads.avg_qualify_minuteslabor_rate_cents_per_hour — assumptionlabor_rate_cents_per_minute |
| Win Rate Revenue UpliftTranslates the improvement in win rate into the revenue it won in the period: deals decided in the period (won plus lost) × the win-rate gain × average won deal value — the deals won that the old win rate would have lost. The open pipeline is not the base: it is a snapshot of deals that stay open for a whole sales cycle, so it would be counted again every month until they closed. Only the share of that change you credit to this initiative counts: competitors, pricing, deal mix and the economy move the win rate too. Booked at your contribution margin: the revenue less the costs that grow with it. | Cash: revenue gain | decided_deals * (current_win_rate_pct - baseline_win_rate_pct) / 100 * avg_deal_value_cents * attributable_share_decimal * contribution_margin_decimal | decided_dealscrm_deals_won_count — reading of crm.deals.won_countavg_deal_value_centscrm_deals_lost_count — reading of crm.deals.lost_countcurrent_win_rate_pct — reading of crm.deals.win_ratebaseline_win_rate_pct — baseline of crm.deals.win_ratecrm_deals_won_value_usd — reading of crm.deals.won_valueattributable_share_decimal — your share of the change, a judgment with no defaultcontribution_margin_decimal — your contribution margin, with no default |
Legal compliance
| Formula | Counts as | Arithmetic | Inputs |
|---|---|---|---|
| Compliance Monitoring Freed CapacityValues the compliance team time freed by automating regulatory change tracking and control mapping. Hours saved per week × weeks × blended compliance analyst rate. | Freed capacity | (baseline_monitoring_hours_per_week - current_monitoring_hours_per_week) * weeks_in_period * labor_rate_cents_per_hour | weeks_in_periodlabor_rate_cents_per_hour — assumptioncurrent_monitoring_hours_per_week — reading of legal.compliance.monitoring_hours_per_weekbaseline_monitoring_hours_per_week — baseline of legal.compliance.monitoring_hours_per_week |
| Compliance Penalty Risk ReductionEstimates the expected regulatory penalty cost avoided by achieving near-100% policy attestation completion — ensuring no regulator can point to employees who were unaware of required policies. The fall in findings a year that cite incomplete attestation, from before the initiative to now, spread evenly over the year's months, × average fine per finding. Only the share of that change you credit to this initiative counts: how often you are examined, what examiners look at and the rules themselves move findings too. | Cash: cost avoidance | (baseline_findings_per_year - current_findings_per_year) * months_in_period / 12 * avg_regulatory_fine_cents * attributable_share_decimal | months_in_periodavg_regulatory_fine_cents — assumption, with no defaultcurrent_findings_per_year — reading of legal.policies.attestation_findings_per_yearattributable_share_decimal — your share of the change, a judgment with no defaultbaseline_findings_per_year — baseline of legal.policies.attestation_findings_per_year |
| Contract Search Freed CapacityValues the legal and business team time freed when AI search replaces manual hunting through disparate contract storage locations. Hours saved per week × weeks × blended rate of staff searching contracts. | Freed capacity | (baseline_search_hours_per_week - current_search_hours_per_week) * weeks_in_period * labor_rate_cents_per_hour | weeks_in_periodlabor_rate_cents_per_hour — assumptioncurrent_search_hours_per_week — reading of legal.contracts.search_hours_per_weekbaseline_search_hours_per_week — baseline of legal.contracts.search_hours_per_week |
| Legal Research Freed CapacityValues the in-house attorney and paralegal time freed by AI-accelerated research and document review. Matters closed × hours saved per matter × blended in-house legal rate. | Freed capacity | matters_per_period * (baseline_research_hours_per_matter - current_research_hours_per_matter) * labor_rate_cents_per_hour | matters_per_period — reading of legal.matters.closed_countlabor_rate_cents_per_hour — assumptioncurrent_research_hours_per_matter — reading of legal.matters.research_hours_per_matterbaseline_research_hours_per_matter — baseline of legal.matters.research_hours_per_matter |
| Missed Renewal Cost AvoidanceEstimates the cost avoided when automated renewal alerts prevent contracts from auto-renewing on unfavorable terms or lapsing without negotiation. Each missed renewal can lock in below-market pricing or create coverage gaps. Missed renewals are compared per renewal coming due, so a larger contract portfolio is not read as more misses: the fall in the share of renewals missed × the renewals due a year now, spread evenly over the year's months, × average cost per missed renewal. | Cash: cost avoidance | (baseline_missed_per_renewal_due - current_missed_per_renewal_due) * current_renewals_due_per_year * months_in_period / 12 * avg_missed_renewal_cost_cents | months_in_periodavg_missed_renewal_cost_cents — assumption, with no defaultcurrent_renewals_due_per_year — reading of legal.contracts.renewals_due_per_yearbaseline_renewals_due_per_year — baseline of legal.contracts.renewals_due_per_yearcurrent_missed_per_renewal_duebaseline_missed_per_renewal_duecurrent_missed_renewals_per_year — reading of legal.contracts.missed_renewals_per_yearbaseline_missed_renewals_per_year — baseline of legal.contracts.missed_renewals_per_year |
| Outside Counsel Spend ReductionEstimates direct savings from shifting routine legal research and matter work in-house using AI, reducing reliance on expensive outside counsel billing rates. Spend is compared per open matter, so a heavier caseload is not read as a loss nor a light one as a saving: the fall in outside counsel spend per matter × the matters open now × months. | Cash: cost savings | (baseline_outside_counsel_per_matter_usd - current_outside_counsel_per_matter_usd) * current_open_matters * months_in_period * 100 | months_in_periodcurrent_open_matters — reading of legal.matters.open_countbaseline_open_matters — baseline of legal.matters.open_countcurrent_outside_counsel_monthly_usd — reading of legal.outside_counsel.monthly_spendbaseline_outside_counsel_monthly_usd — baseline of legal.outside_counsel.monthly_spendcurrent_outside_counsel_per_matter_usdbaseline_outside_counsel_per_matter_usd |
| Periodic Vendor Review Freed CapacityValues the procurement and legal time freed by replacing manual periodic reassessments with continuous automated monitoring and risk scoring. Vendors × review hours saved per vendor × the review cycles in the period (a year's cycles spread evenly over its months) × blended rate. | Freed capacity | vendors_in_portfolio * (baseline_review_hours_per_vendor - current_review_hours_per_vendor) * review_cycles_per_year * months_in_period / 12 * labor_rate_cents_per_hour | months_in_periodvendors_in_portfolio — reading of legal.vendors.portfolio_countreview_cycles_per_year — assumptionlabor_rate_cents_per_hour — assumptioncurrent_review_hours_per_vendor — reading of legal.vendors.review_hours_per_vendorbaseline_review_hours_per_vendor — baseline of legal.vendors.review_hours_per_vendor |
| Regulatory Penalty & Examination Cost AvoidanceEstimates the expected value of penalties avoided by closing compliance gaps before they become findings. Most gaps left open never draw a finding, so each gap closed is worth the chance it would have become one times what a finding costs: gaps prevented × share that would have become a penalized finding × average cost of a finding. | Cash: cost avoidance | compliance_gaps_prevented_in_period * gap_to_finding_rate_decimal * avg_regulatory_penalty_cents | gap_to_finding_rate_decimal — assumption, with no defaultavg_regulatory_penalty_cents — assumption, with no defaultcompliance_gaps_prevented_in_period — reading of legal.compliance.gaps_prevented |
| Supplier Review Freed CapacityValues the procurement team time freed by automating the continuous supplier health monitoring that previously required periodic manual assessments. Hours are compared per active supplier, so a wider supplier base is not read as slower reviews: the fall in monthly review hours per supplier × the suppliers active now × months × analyst rate. | Freed capacity | (baseline_review_hours_per_supplier - current_review_hours_per_supplier) * current_active_suppliers * months_in_period * labor_rate_cents_per_hour | months_in_periodcurrent_active_suppliers — reading of supply_chain.suppliers.active_countbaseline_active_suppliers — baseline of supply_chain.suppliers.active_countlabor_rate_cents_per_hour — assumptioncurrent_review_hours_per_supplierbaseline_review_hours_per_suppliercurrent_supplier_review_hours_per_month — reading of supply_chain.suppliers.review_hours_per_monthbaseline_supplier_review_hours_per_month — baseline of supply_chain.suppliers.review_hours_per_month |
| Supply Disruption Cost AvoidanceEstimates the cost avoided by detecting supplier risk early enough to activate alternatives before a disruption occurs. Each prevented disruption avoids production stoppages, emergency sourcing premiums, and customer impact costs. Disruptions are compared per active supplier, so a wider supplier base is not read as more disruption: the fall in disruptions per supplier a year × the suppliers active now, spread evenly over the year's months, × average cost per disruption. | Cash: cost avoidance | (baseline_disruptions_per_supplier_per_year - current_disruptions_per_supplier_per_year) * current_active_suppliers * months_in_period / 12 * avg_disruption_cost_cents | months_in_periodcurrent_active_suppliers — reading of supply_chain.suppliers.active_countavg_disruption_cost_cents — assumption, with no defaultbaseline_active_suppliers — baseline of supply_chain.suppliers.active_countcurrent_disruption_incidents_per_year — reading of supply_chain.suppliers.disruptions_per_yearbaseline_disruption_incidents_per_year — baseline of supply_chain.suppliers.disruptions_per_yearcurrent_disruptions_per_supplier_per_yearbaseline_disruptions_per_supplier_per_year |
| Training Administration Freed CapacityValues the HR and compliance team time freed by automating training enrollment, reminders, completion tracking, and attestation collection. Admin hours saved per cycle × the training cycles in the period (a year's cycles spread evenly over its months) × blended rate. | Freed capacity | (baseline_training_admin_hours - current_training_admin_hours) * training_cycles_per_year * months_in_period / 12 * labor_rate_cents_per_hour | months_in_periodtraining_cycles_per_year — assumptionlabor_rate_cents_per_hour — assumptioncurrent_training_admin_hours — reading of legal.training.admin_hours_per_cyclebaseline_training_admin_hours — baseline of legal.training.admin_hours_per_cycle |
| Vendor Onboarding Delay Freed CapacityEstimates the business value unlocked by onboarding vendors faster. Each day of onboarding delay is a day the vendor relationship isn't generating value — whether that's a new software deployment, a new supplier, or a new service contract. | Freed capacity | vendors_onboarded * (baseline_onboarding_days - current_onboarding_days) * avg_daily_blocked_value_cents | vendors_onboarded — reading of legal.vendors.onboarded_countcurrent_onboarding_days — reading of legal.vendors.onboarding_daysbaseline_onboarding_days — baseline of legal.vendors.onboarding_daysavg_daily_blocked_value_cents — assumption, with no default |
Marketing
| Formula | Counts as | Arithmetic | Inputs |
|---|---|---|---|
| Attribution Reporting Freed CapacityValues the marketing analyst time freed by replacing manual multi-channel reporting with automated attribution dashboards. The hours are priced at what they cost. | Freed capacity | (baseline_reporting_hours_per_week - current_reporting_hours_per_week) * weeks_in_period * labor_rate_cents_per_hour | weeks_in_periodlabor_rate_cents_per_hour — assumptioncurrent_reporting_hours_per_week — reading of marketing.reporting.hours_per_weekbaseline_reporting_hours_per_week — baseline of marketing.reporting.hours_per_week |
| Budget Reallocation Revenue UpliftEstimates incremental revenue from reallocating spend from low-performing to high-ROI channels. A reallocated dollar adds the difference between what it earns where it went and what it earned where it was, not the whole of what it earns now: spend reallocated × (pipeline per dollar in the channels it went to − pipeline per dollar in the channels it left) × the close rate, the CRM's win rate on closed deals. Booked at your contribution margin: the revenue less the costs that grow with it. | Cash: revenue gain | budget_reallocated_usd * (destination_pipeline_per_dollar - source_pipeline_per_dollar) * close_rate_decimal * 100 * contribution_margin_decimal | close_rate_decimalbudget_reallocated_usd — reading of marketing.budget.reallocatedcrm_deals_win_rate_pct — reading of crm.deals.win_ratesource_pipeline_per_dollar — reading of marketing.budget.source_pipeline_per_dollarcontribution_margin_decimal — your contribution margin, with no defaultdestination_pipeline_per_dollar — reading of marketing.budget.destination_pipeline_per_dollar |
| Campaign Setup Freed CapacityValues the marketing ops time freed by automating campaign configuration, audience segmentation, and A/B test setup. Campaigns launched × hours saved per campaign × labor rate. | Freed capacity | campaigns_launched * (baseline_hours_per_campaign - current_hours_per_campaign) * labor_rate_cents_per_hour | campaigns_launched — reading of marketing.campaigns.launched_countlabor_rate_cents_per_hour — assumptioncurrent_hours_per_campaign — reading of marketing.campaigns.hours_per_launchbaseline_hours_per_campaign — baseline of marketing.campaigns.hours_per_launch |
| Content Production Freed CapacityValues the writer and designer time freed when AI drafting and repurposing accelerates content production. Pieces produced × hours saved per piece × blended content team rate. | Freed capacity | content_pieces_produced * (baseline_hours_per_piece - current_hours_per_piece) * labor_rate_cents_per_hour | content_pieces_produced — reading of marketing.content.published_countcurrent_hours_per_piece — reading of marketing.content.hours_per_piecebaseline_hours_per_piece — baseline of marketing.content.hours_per_piecelabor_rate_cents_per_hour — assumption |
| Content Volume Outsourcing Cost AvoidanceEstimates the agency or freelancer spend avoided by producing more content in-house with AI assistance. Blog posts published in the period above the monthly count before AI × average cost to outsource an equivalent post. Both counts are posts published on the connected CMS, so the before-value is the account's own months before the initiative. | Cash: cost avoidance | (current_published_count - baseline_published_count) * avg_outsourced_content_cost_cents | current_published_count — reading of marketing.content.published_countbaseline_published_count — baseline of marketing.content.published_countavg_outsourced_content_cost_cents — assumption, with no default |
| Email Performance Revenue UpliftEstimates incremental revenue driven by improved email open rates from send-time optimization and behavioral personalization. Campaigns × list size × open rate improvement × the share of opens that convert, measured each period by your email platform or CRM × average conversion value. Only the share of that change you credit to this initiative counts: mail privacy features that inflate opens, deliverability, list changes and seasonality move the open rate too. Booked at your contribution margin: the revenue less the costs that grow with it. | Cash: revenue gain | campaigns_launched * emails_per_campaign * (current_open_rate_pct - baseline_open_rate_pct) / 100 * click_to_conversion_rate_decimal * avg_conversion_value_cents * attributable_share_decimal * contribution_margin_decimal | campaigns_launched — reading of marketing.campaigns.launched_countemails_per_campaign — reading of marketing.campaigns.avg_list_sizecurrent_open_rate_pct — reading of marketing.emails.open_ratebaseline_open_rate_pct — baseline of marketing.emails.open_rateattributable_share_decimal — your share of the change, a judgment with no defaultavg_conversion_value_cents — assumption, with no defaultcontribution_margin_decimal — your contribution margin, with no defaultopen_to_conversion_rate_pct — reading of marketing.emails.open_to_conversion_rateclick_to_conversion_rate_decimal |
| MQL-to-SQL Conversion Revenue UpliftEstimates incremental pipeline revenue from improving the MQL-to-SQL conversion rate. Higher conversion means more opportunities in the pipeline without growing top-of-funnel spend. Only the share of that change you credit to this initiative counts: SDR capacity, how an MQL is defined, lead mix and the market move the MQL-to-SQL rate too. Booked at your contribution margin: the revenue less the costs that grow with it. | Cash: revenue gain | mqls_in_period * (current_mql_to_sql_rate_pct - baseline_mql_to_sql_rate_pct) / 100 * avg_deal_value_cents * close_rate_decimal * attributable_share_decimal * contribution_margin_decimal | mqls_in_period — reading of crm.contacts.mql_countclose_rate_decimalcrm_deals_won_count — reading of crm.deals.won_countavg_deal_value_centscrm_contacts_sql_count — reading of crm.contacts.sql_countcrm_deals_win_rate_pct — reading of crm.deals.win_ratecrm_deals_won_value_usd — reading of crm.deals.won_valueattributable_share_decimal — your share of the change, a judgment with no defaultcontribution_margin_decimal — your contribution margin, with no defaultcurrent_mql_to_sql_rate_pctbaseline_mql_to_sql_rate_pct — baseline of crm.contacts.mql_to_sql_rate |
| Nurture Program Freed CapacityValues the demand gen team time freed by replacing manual follow-up and segmentation tasks with automated nurture sequences. Hours are compared per new contact, so a campaign that floods the funnel is not read as slower follow-up: the fall in weekly nurture hours per new contact × this period's new contacts × weeks × staff rate. | Freed capacity | (baseline_nurture_hours_per_contact - current_nurture_hours_per_contact) * current_new_contacts * weeks_in_period * labor_rate_cents_per_hour | weeks_in_periodcurrent_new_contacts — reading of crm.contacts.new_countbaseline_new_contacts — baseline of crm.contacts.new_countlabor_rate_cents_per_hour — assumptioncurrent_nurture_hours_per_week — reading of marketing.nurture.manual_hours_per_weekbaseline_nurture_hours_per_week — baseline of marketing.nurture.manual_hours_per_weekcurrent_nurture_hours_per_contactbaseline_nurture_hours_per_contact |
| Paid Acquisition Cost AvoidanceEstimates the paid media spend saved by growing organic traffic. Each additional organic session replaces a paid click that would otherwise be needed to maintain total traffic. Incremental organic sessions × blended cost per equivalent paid click. Only the share of that growth you credit to this initiative counts: search demand, seasonality, brand campaigns, press and search engine algorithm changes move organic traffic too. | Cash: cost avoidance | (current_organic_sessions_per_month - baseline_organic_sessions_per_month) * months_in_period * cost_per_equivalent_paid_click_cents * attributable_share_decimal | months_in_periodattributable_share_decimal — your share of the change, a judgment with no defaultcurrent_organic_sessions_per_month — reading of marketing.web.organic_sessionsbaseline_organic_sessions_per_month — baseline of marketing.web.organic_sessionscost_per_equivalent_paid_click_cents — assumption, with no default |
| SEO Research & Optimization Freed CapacityValues the content and SEO team time freed by automating keyword research, gap analysis, and on-page optimization recommendations. The hours are priced at what they cost. | Freed capacity | (baseline_seo_research_hours_per_month - current_seo_research_hours_per_month) * months_in_period * labor_rate_cents_per_hour | months_in_periodlabor_rate_cents_per_hour — assumptioncurrent_seo_research_hours_per_month — reading of marketing.seo.research_hours_per_monthbaseline_seo_research_hours_per_month — baseline of marketing.seo.research_hours_per_month |
Customer success
| Formula | Counts as | Arithmetic | Inputs |
|---|---|---|---|
| Agent Handle Time Freed CapacityValues the agent time freed when AI routing and suggested responses reduce the work per ticket. Tickets resolved by agents × reduction in agent minutes per ticket ÷ 60 × agent labor rate. Agent minutes are the work on a ticket, measured by a time study or your help desk's time tracking, not how long the ticket was open. Tickets an AI agent resolved are left out, because a deflection link books them. A faster first response is the customer waiting less, not agents working less, and is not priced here. | Freed capacity | tickets_resolved_by_agents * (baseline_agent_minutes - current_agent_minutes) / 60 * labor_rate_cents_per_hour | current_agent_minutes — reading of support.tickets.agent_minutes_per_ticketbaseline_agent_minutes — baseline of support.tickets.agent_minutes_per_ticketlabor_rate_cents_per_hour — assumptiontickets_resolved_by_agents — reading of support.tickets.agent_resolved_count |
| CSM Capacity FreedThe customer success time freed when each CSM can manage more accounts. The book the team covers now (accounts per CSM × CSMs) would have needed more CSMs at the accounts per CSM before automation; that difference, in CSM-equivalents, × what a CSM costs you a year, for the period's share of the year. Freed capacity: the team is paid the same, so it isn't cash until a hire not made or a contract not renewed shows it is. | Freed capacity | csm_count * (current_accounts_per_csm / baseline_accounts_per_csm - 1) * csm_annual_cost_cents * months_in_period / 12 | csm_count — reading of customer_success.accounts.csm_countmonths_in_periodcsm_annual_cost_cents — assumption, with no defaultcurrent_accounts_per_csm — reading of customer_success.accounts.per_csmbaseline_accounts_per_csm — baseline of customer_success.accounts.per_csm |
| Documentation Maintenance Freed CapacityValues the time freed when AI keeps knowledge base articles fresh automatically, replacing the manual review and update process. Articles × minutes saved per article per review × the review cycles in the period (a year's cycles spread evenly over its months) × blended rate. | Freed capacity | kb_articles_maintained * (baseline_update_minutes_per_article - current_update_minutes_per_article) * update_cycles_per_year * months_in_period / 12 * labor_rate_cents_per_minute | months_in_periodkb_articles_maintained — reading of support.knowledge_base.article_countupdate_cycles_per_year — assumptionlabor_rate_cents_per_hour — assumptionlabor_rate_cents_per_minutecurrent_update_minutes_per_article — reading of support.knowledge_base.update_minutes_per_articlebaseline_update_minutes_per_article — baseline of support.knowledge_base.update_minutes_per_article |
| MRR Retained from Churn PreventionEstimates the recurring revenue retained in the period by reducing churn. Reduction in monthly churn × total MRR is the MRR that would have canceled this month and did not; the value is that month of revenue, not a year of it. Only the share of that change you credit to this initiative counts: pricing, product changes, the economy and competitors move the churn rate too. Booked at your contribution margin: the revenue less the costs that grow with it. Net revenue retention on the same template already counts the churn it prevents, so an initiative gets one of the two: this one where billing reports churn. | Cash: revenue gain | total_mrr_cents * (baseline_churn_rate_pct - current_churn_rate_pct) / 100 * attributable_share_decimal * contribution_margin_decimal | total_mrr_centscurrent_churn_rate_pct — reading of billing.subscriptions.churn_ratebaseline_churn_rate_pct — baseline of billing.subscriptions.churn_rateattributable_share_decimal — your share of the change, a judgment with no defaultcontribution_margin_decimal — your contribution margin, with no defaultbilling_subscriptions_mrr_usd — reading of billing.subscriptions.mrr |
| NRR Improvement Revenue ValueEstimates the recurring revenue added by improving net revenue retention. Each percentage point of NRR improvement lifts the revenue kept from existing customers; the value is one month of that lift, the period's MRR × the improvement, not the annual run rate. Only the share of that change you credit to this initiative counts: price increases, seat and usage growth, product launches and the economy move net revenue retention too. Booked at your contribution margin: the revenue less the costs that grow with it. | Cash: revenue gain | billing_subscriptions_mrr_usd * (current_nrr_pct - baseline_nrr_pct) / 100 * 100 * attributable_share_decimal * contribution_margin_decimal | current_nrr_pct — reading of customer_success.revenue.net_retention_ratebaseline_nrr_pct — baseline of customer_success.revenue.net_retention_rateattributable_share_decimal — your share of the change, a judgment with no defaultcontribution_margin_decimal — your contribution margin, with no defaultbilling_subscriptions_mrr_usd — reading of billing.subscriptions.mrr |
| QA Program Freed CapacityValues the QA analyst time freed by replacing manual interaction sampling with automated AI review. With AI QA covering 100% of interactions at a fraction of the cost, the manual sampling program is eliminated or dramatically reduced. | Freed capacity | interactions_per_period * baseline_qa_sample_rate_pct / 100 * avg_manual_review_minutes * labor_rate_cents_per_minute | interactions_per_period — reading of support.tickets.volumeavg_manual_review_minutes — reading of support.quality.review_minutes_per_interactionlabor_rate_cents_per_hour — assumptionbaseline_qa_sample_rate_pct — baseline of support.quality.sample_ratelabor_rate_cents_per_minute |
| Renewal Rate Revenue UpliftEstimates the recurring revenue retained by improving renewal rates through automated QBR prep and proactive renewal workflows. ARR up for renewal × improvement in renewal rate is the ARR kept; the value is the period's share of it, one month of revenue for a month's renewals. Only the share of that change you credit to this initiative counts: the product, pricing, customers' budgets and competitors move the renewal rate too. Booked at your contribution margin: the revenue less the costs that grow with it. | Cash: revenue gain | arr_up_for_renewal_usd * (current_renewal_rate_pct - baseline_renewal_rate_pct) / 100 * months_in_period / 12 * 100 * attributable_share_decimal * contribution_margin_decimal | months_in_periodarr_up_for_renewal_usd — reading of customer_success.renewals.arr_up_for_renewalcurrent_renewal_rate_pct — reading of customer_success.renewals.ratebaseline_renewal_rate_pct — baseline of customer_success.renewals.rateattributable_share_decimal — your share of the change, a judgment with no defaultcontribution_margin_decimal — your contribution margin, with no default |
| Self-Serve Ticket Deflection Freed CapacityValues the support team time freed when customers resolve issues via the knowledge base rather than submitting tickets. Increase in deflection rate × total ticket volume × cost per handled ticket. | Freed capacity | total_tickets_per_period * (current_deflection_rate_pct - baseline_deflection_rate_pct) / 100 * avg_cost_per_handled_ticket_usd * 100 | total_tickets_per_period — reading of support.tickets.volumecurrent_deflection_rate_pct — reading of support.tickets.self_serve_deflection_ratebaseline_deflection_rate_pct — baseline of support.tickets.self_serve_deflection_rateavg_cost_per_handled_ticket_usd — reading of support.tickets.cost_per_ticket |
Supply chain
| Formula | Counts as | Arithmetic | Inputs |
|---|---|---|---|
| Excess Inventory Carrying Cost ReductionEstimates the carrying cost saved by holding less inventory for the same sales. Better demand forecasts reduce safety stock requirements and overbuying. Inventory is compared per dollar of monthly sales, so stock that grows with the business books no loss and a quiet month no saving: (inventory per dollar of monthly sales before − now) × this period's monthly sales × the period's share of the annual carrying cost rate. Only the share of that change you credit to this initiative counts: how volatile demand is, promotions, supplier lead times and working-capital policy move inventory too. | Cash: cost savings | (baseline_inventory_per_sales_dollar - current_inventory_per_sales_dollar) * current_revenue_usd / months_in_period * annual_carrying_cost_rate_decimal * months_in_period / 12 * 100 * attributable_share_decimal | months_in_periodcurrent_revenue_usd — reading of finance.pnl.revenueattributable_share_decimal — your share of the change, a judgment with no defaultbaseline_monthly_revenue_usd — baseline of finance.pnl.revenuecurrent_avg_inventory_value_usd — reading of supply_chain.inventory.avg_valuebaseline_avg_inventory_value_usd — baseline of supply_chain.inventory.avg_valueannual_carrying_cost_rate_decimal — assumptioncurrent_inventory_per_sales_dollarbaseline_inventory_per_sales_dollar |
| Invoice Matching Freed CapacityValues the AP team time freed by automating three-way PO-receipt-invoice matching and exception handling. Hours are compared per vendor bill received, so a heavy month of bills is not read as slower matching: the fall in weekly matching hours per bill × this period's bills × weeks × AP rate. | Freed capacity | (baseline_matching_hours_per_bill - current_matching_hours_per_bill) * current_bills * weeks_in_period * labor_rate_cents_per_hour | current_bills — reading of finance.bills.countbaseline_bills — baseline of finance.bills.countweeks_in_periodlabor_rate_cents_per_hour — assumptioncurrent_matching_hours_per_billbaseline_matching_hours_per_billcurrent_invoice_matching_hours_per_week — reading of supply_chain.procurement.invoice_matching_hours_per_weekbaseline_invoice_matching_hours_per_week — baseline of supply_chain.procurement.invoice_matching_hours_per_week |
| Late Delivery Penalty & Expedite Cost AvoidanceEstimates the cost avoided when better route planning improves on-time delivery — avoiding SLA penalties, customer credits, and expensive expedite orders. Shipments × on-time delivery improvement × average cost per late delivery. Only the share of that change you credit to this initiative counts: carriers, weather, peak season and order volume move the on-time delivery rate too. | Cash: cost avoidance | shipments_per_period * (current_on_time_delivery_rate_pct - baseline_on_time_delivery_rate_pct) / 100 * avg_late_delivery_cost_cents * attributable_share_decimal | shipments_per_period — reading of supply_chain.shipments.countattributable_share_decimal — your share of the change, a judgment with no defaultavg_late_delivery_cost_cents — assumption, with no defaultcurrent_on_time_delivery_rate_pct — reading of supply_chain.deliveries.on_time_ratebaseline_on_time_delivery_rate_pct — baseline of supply_chain.deliveries.on_time_rate |
| Pick Error Correction Cost SavingsEstimates the cost avoided from fewer mispicks. Each pick error triggers a customer return, reshipping, and internal correction cycle — all of which are eliminated with more accurate directed-pick workflows. | Cash: cost savings | total_picks_per_period * (baseline_pick_error_rate_pct - current_pick_error_rate_pct) / 100 * avg_pick_error_cost_cents | total_picks_per_period — reading of supply_chain.warehouse.picks_countavg_pick_error_cost_cents — assumption, with no defaultcurrent_pick_error_rate_pct — reading of supply_chain.warehouse.pick_error_ratebaseline_pick_error_rate_pct — baseline of supply_chain.warehouse.pick_error_rate |
| Pick Labor Freed CapacityValues the warehouse labor hours freed by improved pick productivity. Directed picking and optimized slotting allow the same number of picks to be completed with fewer labor hours. Total picks × labor hours saved per pick (1 ÷ picks per hour before, less 1 ÷ picks per hour now) × labor rate. | Freed capacity | total_picks_per_period * (1 / baseline_picks_per_labor_hour - 1 / current_picks_per_labor_hour) * labor_rate_cents_per_hour | total_picks_per_period — reading of supply_chain.warehouse.picks_countlabor_rate_cents_per_hour — assumptioncurrent_picks_per_labor_hour — reading of supply_chain.warehouse.picks_per_labor_hourbaseline_picks_per_labor_hour — baseline of supply_chain.warehouse.picks_per_labor_hour |
| PO Cycle Freed CapacityValues the procurement staff time freed by automating purchase order creation, approval routing, and invoice matching. POs processed × (hours per PO before − after) × blended procurement labor rate. | Freed capacity | pos_processed_per_period * (baseline_hours_per_po - current_hours_per_po) * labor_rate_cents_per_hour | current_hours_per_po — reading of supply_chain.procurement.hours_per_pobaseline_hours_per_po — baseline of supply_chain.procurement.hours_per_popos_processed_per_period — reading of supply_chain.procurement.purchase_orders_processedlabor_rate_cents_per_hour — assumption |
| Shipping Cost ReductionMeasures direct shipping cost savings from AI-optimized carrier selection and route planning. Shipments in period × reduction in cost per shipment. Only the share of that change you credit to this initiative counts: fuel surcharges, carrier rate changes and the weight and lane mix of shipments move cost per shipment too. | Cash: cost savings | shipments_per_period * (baseline_cost_per_shipment_usd - current_cost_per_shipment_usd) * 100 * attributable_share_decimal | shipments_per_period — reading of supply_chain.shipments.countattributable_share_decimal — your share of the change, a judgment with no defaultcurrent_cost_per_shipment_usd — reading of supply_chain.shipments.cost_per_shipmentbaseline_cost_per_shipment_usd — baseline of supply_chain.shipments.cost_per_shipment |
| Stockout Lost Revenue RecoveryEstimates revenue recovered by reducing stockouts. Fewer out-of-stock situations means fewer lost sales, fewer expedite orders, and better customer fill rates. Only the share of that change you credit to this initiative counts: supplier delays, demand spikes and buying decisions move the stockout rate too. Booked at your contribution margin: the revenue less the costs that grow with it. | Cash: revenue gain | total_revenue_cents * (baseline_stockout_rate_pct - current_stockout_rate_pct) / 100 * attributable_share_decimal * contribution_margin_decimal | total_revenue_centsfinancials_revenue_usd — reading of finance.pnl.revenuecurrent_stockout_rate_pct — reading of supply_chain.inventory.stockout_rateattributable_share_decimal — your share of the change, a judgment with no defaultbaseline_stockout_rate_pct — baseline of supply_chain.inventory.stockout_ratecontribution_margin_decimal — your contribution margin, with no default |
HR
| Formula | Counts as | Arithmetic | Inputs |
|---|---|---|---|
| HR Inquiry Deflection Freed CapacityValues the HR team time freed when the chatbot deflects routine policy, PTO, and benefits questions. Inquiries deflected × average cost for HR to handle one inquiry manually. | Freed capacity | tier1_inquiries_per_period * deflection_rate_decimal * avg_hr_handling_cost_cents | deflection_rate_decimal — reading of hr.inquiries.chatbot_deflection_ratelabor_rate_cents_per_hour — assumptionavg_hr_handling_cost_centstier1_inquiries_per_period — reading of hr.inquiries.tier1_per_monthbaseline_minutes_per_inquiry — baseline of hr.inquiries.minutes_per_inquiry |
| New Hire Ramp-Up Freed CapacityValues the new-hire time put to productive use by shortening the ramp period. Shorter ramp × daily productive output value × number of hires. Only the share of that change you credit to this initiative counts: role mix, managers, hiring quality and training programs move days to productivity too. | Freed capacity | new_hires_onboarded * (baseline_days_to_productivity - current_days_to_productivity) * avg_daily_output_value_cents * attributable_share_decimal | new_hires_onboarded — reading of hr.onboarding.hires_countattributable_share_decimal — your share of the change, a judgment with no defaultavg_daily_output_value_cents — assumption, with no defaultcurrent_days_to_productivity — reading of hr.onboarding.days_to_productivitybaseline_days_to_productivity — baseline of hr.onboarding.days_to_productivity |
| Onboarding HR Freed CapacityValues the HR admin time freed by automating onboarding tasks. New hires × hours saved per hire × blended HR rate. | Freed capacity | new_hires_onboarded * (baseline_hr_hours_per_hire - current_hr_hours_per_hire) * blended_labor_rate_cents_per_hour | new_hires_onboarded — reading of hr.onboarding.hires_countcurrent_hr_hours_per_hire — reading of hr.onboarding.admin_hours_per_hirebaseline_hr_hours_per_hire — baseline of hr.onboarding.admin_hours_per_hireblended_labor_rate_cents_per_hour — assumption |
| Recruiter Screening Freed CapacityValues the recruiter time freed by replacing manual resume review with AI screening. Hires × recruiter hours saved per hire × blended recruiter rate. | Freed capacity | hires_in_period * (baseline_recruiter_hours_per_hire - current_recruiter_hours_per_hire) * labor_rate_cents_per_hour | hires_in_period — reading of hr.hiring.hires_countlabor_rate_cents_per_hour — assumptioncurrent_recruiter_hours_per_hire — reading of hr.hiring.recruiter_hours_per_hirebaseline_recruiter_hours_per_hire — baseline of hr.hiring.recruiter_hours_per_hire |
| Review Cycle Admin Freed CapacityValues the HR and manager admin time freed each review cycle. Headcount reviewed × (admin hours per employee before − after) × labor rate × the review cycles in the period (a year's cycles spread evenly over its months). | Freed capacity | employees_reviewed * (baseline_admin_hours_per_employee - current_admin_hours_per_employee) * labor_rate_cents_per_hour * review_cycles_per_year * months_in_period / 12 | months_in_periodemployees_reviewed — reading of hr.reviews.employees_reviewedreview_cycles_per_year — assumptionlabor_rate_cents_per_hour — assumptioncurrent_admin_hours_per_employee — reading of hr.reviews.admin_hours_per_employeebaseline_admin_hours_per_employee — baseline of hr.reviews.admin_hours_per_employee |
| Vacancy Days Freed CapacityValues the days a role goes uncovered, cut by moving candidates to a first interview sooner. Every day a role is open costs its output, or someone else's time covering it. Hires × days reduced × daily vacancy cost. | Freed capacity | hires_in_period * (baseline_days_to_first_interview - current_days_to_first_interview) * avg_daily_vacancy_cost_cents | hires_in_period — reading of hr.hiring.hires_countavg_daily_vacancy_cost_cents — assumption, with no defaultcurrent_days_to_first_interview — reading of hr.hiring.days_to_first_interviewbaseline_days_to_first_interview — baseline of hr.hiring.days_to_first_interview |
| Voluntary Attrition Cost AvoidanceEstimates the cost avoided by reducing unwanted voluntary attrition. Each prevented departure avoids recruiting, onboarding, and productivity ramp costs. Headcount × reduction in the annualized attrition rate × the period's share of a year gives the departures avoided in the period, × average replacement cost. Only the share of that change you credit to this initiative counts: the job market, pay, managers and the economy move the attrition rate too. | Cash: cost avoidance | total_headcount * (baseline_attrition_rate_pct - current_attrition_rate_pct) / 100 * months_in_period / 12 * avg_replacement_cost_cents * attributable_share_decimal | total_headcount — reading of hr.workforce.headcountmonths_in_periodattributable_share_decimal — your share of the change, a judgment with no defaultavg_replacement_cost_cents — assumptioncurrent_attrition_rate_pct — reading of hr.workforce.voluntary_attrition_ratebaseline_attrition_rate_pct — baseline of hr.workforce.voluntary_attrition_rate |
| Workforce Reporting Freed CapacityValues the HR analyst time freed by replacing manual headcount and workforce data reports with automated analytics. Hours saved per month × months × labor rate. | Freed capacity | (baseline_reporting_hours_per_month - current_reporting_hours_per_month) * months_in_period * labor_rate_cents_per_hour | months_in_periodlabor_rate_cents_per_hour — assumptioncurrent_reporting_hours_per_month — reading of hr.reporting.hours_per_monthbaseline_reporting_hours_per_month — baseline of hr.reporting.hours_per_month |
Operations
| Formula | Counts as | Arithmetic | Inputs |
|---|---|---|---|
| Capacity Freed by an AI AssistantValues the time a workplace AI assistant gives back: people who used it in the period × timed tasks each does with it a period × the minutes it saves on one (the task's minutes without it less its minutes with it, from the account's own time study) × the blended hourly rate. Tasks and minutes have no default, so nothing is booked until both are set. | Freed capacity | monthly_active_users * tasks_per_active_user * (baseline_minutes_per_task - minutes_per_task) / 60 * blended_labor_rate_cents_per_hour | minutes_per_task — reading of ai_assistant.tasks.minutes_per_taskmonthly_active_users — reading of ai_assistant.users.monthly_activetasks_per_active_user — assumption, with no defaultbaseline_minutes_per_task — baseline of ai_assistant.tasks.minutes_per_taskblended_labor_rate_cents_per_hour — assumption |
| Data Entry Freed CapacityValues the staff time freed by automating document or form data extraction. Volume × per-item time saved × labor rate. | Freed capacity | documents_processed * (baseline_minutes_per_doc - current_minutes_per_doc) * labor_rate_cents_per_minute | documents_processed — reading of operations.documents.processed_countcurrent_minutes_per_doc — reading of operations.documents.data_entry_minutes_per_documentbaseline_minutes_per_doc — baseline of operations.documents.data_entry_minutes_per_documentlabor_rate_cents_per_hour — assumptionlabor_rate_cents_per_minute |
| Error Rework Freed CapacityValues the correction and reconciliation work freed when AI keys data with fewer errors than manual entry, at what that work costs. | Freed capacity | documents_processed * (baseline_error_rate_pct - current_error_rate_pct) / 100 * avg_rework_cost_cents | documents_processed — reading of operations.documents.processed_countavg_rework_cost_cents — assumption, with no defaultcurrent_error_rate_pct — reading of operations.processing.error_ratebaseline_error_rate_pct — baseline of operations.processing.error_rate |
| Manual Work Replaced by Automated WorkflowsValues the manual work automated workflows replace: successful runs in the period, summed across workflows, × the minutes a person took to do the task one run now does × the ops hourly rate. Runs come from n8n, Make or Zapier; the minutes come from a time study or your own figure, with Roiva's convention standing in until then. | Freed capacity | successful_runs * manual_minutes_per_run * ops_hourly_rate / 60 | ops_hourly_rate — assumptionsuccessful_runsmanual_minutes_per_run — baseline of automation.runs.manual_minutes_per_run |
| Meeting Freed CapacityValues the time returned to the team by shorter, smaller or skipped meetings: attendee-hours spent in meetings before the assistant, less attendee-hours now, at the blended hourly rate. Both before-values come from the account's own calendar history. Attendee counts are the people invited, which is what a calendar records, on both sides of the comparison. | Freed capacity | (baseline_meeting_hours * baseline_attendees_per_meeting - meeting_hours * attendees_per_meeting) * blended_labor_rate_cents_per_hour | meeting_hours — reading of calendar.meetings.hoursattendees_per_meeting — reading of calendar.meetings.avg_attendeesbaseline_meeting_hours — baseline of calendar.meetings.hoursbaseline_attendees_per_meeting — baseline of calendar.meetings.avg_attendeesblended_labor_rate_cents_per_hour — assumption |
| Reporting Freed CapacityValues the staff hours freed by automating report production. Multiply weekly hours saved by the number of weeks measured and the team's blended hourly rate. | Freed capacity | hours_saved_per_week * weeks_in_period * blended_labor_rate_cents_per_hour | weeks_in_periodhours_saved_per_week — reading of workforce.time.hours_saved_per_weekblended_labor_rate_cents_per_hour — assumption |
Construction engineering
| Formula | Counts as | Arithmetic | Inputs |
|---|---|---|---|
| Cost Overrun Margin ProtectionEstimates gross margin protected by catching and correcting budget overruns earlier in the project lifecycle. Real-time EAC forecasting surfaces cost variance weeks before month-end reviews — enabling corrective action while there is still margin to protect. An overrun is incurred as the work is put in place, so the base is the period's contract revenue earned, not the contract value of every active project, which would count a two-year job again every month: revenue × fall in the overrun rate × the average overrun depth measured before the AI. Overruns made smaller are not counted, so the figure errs low. Only the share of that change you credit to this initiative counts: material and labor prices, subcontractors, weather and scope changes move the cost overrun rate too. | Cash: cost avoidance | financials_revenue_usd * (baseline_overrun_rate_pct - current_overrun_rate_pct) / 100 * avg_overrun_depth_decimal * 100 * attributable_share_decimal | financials_revenue_usd — reading of finance.pnl.revenuecurrent_overrun_rate_pct — reading of construction.projects.cost_overrun_rateavg_overrun_depth_decimalbaseline_overrun_rate_pct — baseline of construction.projects.cost_overrun_rateattributable_share_decimal — your share of the change, a judgment with no defaultbaseline_overrun_depth_pct — baseline of construction.projects.avg_overrun_depth |
| Document Search Freed CapacityValues the field and office staff time freed by replacing manual document hunting with AI-powered search across the Common Data Environment. Hours saved per week × weeks × blended field/office rate. | Freed capacity | (baseline_document_search_hours_per_week - current_document_search_hours_per_week) * weeks_in_period * labor_rate_cents_per_hour | weeks_in_periodlabor_rate_cents_per_hour — assumptioncurrent_document_search_hours_per_week — reading of construction.documents.search_hours_per_weekbaseline_document_search_hours_per_week — baseline of construction.documents.search_hours_per_week |
| Estimate Accuracy Margin ProtectionEstimates gross margin protected by improving bid accuracy. Inaccurate estimates — whether too low (creating margin loss on awarded projects) or too high (losing winnable bids) — destroy value. Tighter estimates using historical actuals protects margin on won work. | Cash: cost avoidance | awarded_project_value_usd * (baseline_estimate_error_pct - current_estimate_error_pct) / 100 * avg_project_margin_decimal * 100 | awarded_project_value_usd — reading of construction.bids.awarded_valueavg_project_margin_decimal — assumption, with no defaultcurrent_estimate_error_pct — reading of construction.estimating.variance_ratebaseline_estimate_error_pct — baseline of construction.estimating.variance_rate |
| Estimating Freed CapacityValues the estimator time freed by automating quantity takeoffs and bid assembly. Each hour saved per bid compounds across the bid volume — allowing the same estimating team to respond to more opportunities without additional headcount. The hours are priced at what they cost. | Freed capacity | bids_submitted_per_period * (baseline_estimating_hours_per_bid - current_estimating_hours_per_bid) * labor_rate_cents_per_hour | bids_submitted_per_period — reading of construction.bids.submitted_countlabor_rate_cents_per_hour — assumptioncurrent_estimating_hours_per_bid — reading of construction.estimating.hours_per_bidbaseline_estimating_hours_per_bid — baseline of construction.estimating.hours_per_bid |
| Job Cost Review Freed CapacityValues the project manager and controller time freed by automating job cost data aggregation and variance reporting. With always-current dashboards, the manual effort of pulling data into spreadsheets for periodic reviews is eliminated. | Freed capacity | active_projects * (baseline_review_hours_per_project_per_cycle - current_review_hours_per_project_per_cycle) * review_cycles_per_period * labor_rate_cents_per_hour | active_projects — reading of construction.projects.active_countreview_cycles_per_period — reading of construction.job_costing.review_cycleslabor_rate_cents_per_hour — assumptioncurrent_review_hours_per_project_per_cycle — reading of construction.job_costing.review_hours_per_projectbaseline_review_hours_per_project_per_cycle — baseline of construction.job_costing.review_hours_per_project |
| Payroll Processing Freed CapacityValues the payroll and office staff time freed by replacing paper timecard collection and manual reconciliation with digital time capture. Hours are compared per timecard processed, so a bigger crew in peak season is not read as slower processing: the fall in weekly hours per timecard × this period's timecards × weeks × blended payroll/admin rate. | Freed capacity | (baseline_payroll_hours_per_timecard - current_payroll_hours_per_timecard) * current_timecards * weeks_in_period * labor_rate_cents_per_hour | weeks_in_periodcurrent_timecards — reading of construction.payroll.timecards_processedbaseline_timecards — baseline of construction.payroll.timecards_processedlabor_rate_cents_per_hour — assumptioncurrent_payroll_hours_per_week — reading of construction.payroll.processing_hours_per_weekbaseline_payroll_hours_per_week — baseline of construction.payroll.processing_hours_per_weekcurrent_payroll_hours_per_timecardbaseline_payroll_hours_per_timecard |
| Recordable Incident Cost AvoidanceEstimates the cost avoided by reducing recordable safety incidents. Each recordable incident carries direct costs (medical, OSHA reporting, investigation) and indirect costs (project disruption, crew morale, insurance premium impact, and potential contract disqualification). Only the share of that change you credit to this initiative counts: site mix, crews, weather and other safety programs move the recordable incident rate too. | Cash: cost avoidance | total_labor_hours_worked * (baseline_recordable_incident_rate - current_recordable_incident_rate) / 200000 * avg_recordable_incident_cost_cents * attributable_share_decimal | total_labor_hours_worked — reading of construction.labor.hours_workedattributable_share_decimal — your share of the change, a judgment with no defaultcurrent_recordable_incident_rate — reading of construction.safety.recordable_incident_ratebaseline_recordable_incident_rate — baseline of construction.safety.recordable_incident_rateavg_recordable_incident_cost_cents — assumption, with no default |
| RFI Delay Freed CapacityValues faster RFI resolution as freed capacity, at the daily cost of a delay. Unanswered RFIs block field crews and idle subcontractors — each day of RFI delay has a direct impact on project schedule and labor cost. Faster routing and automated precedent surfacing compresses response time. | Freed capacity | rfis_per_period * (baseline_rfi_response_days - current_rfi_response_days) * avg_daily_rfi_delay_cost_cents | rfis_per_period — reading of construction.rfis.submitted_countcurrent_rfi_response_days — reading of construction.rfis.response_daysbaseline_rfi_response_days — baseline of construction.rfis.response_daysavg_daily_rfi_delay_cost_cents — assumption, with no default |
| Safety Administration Freed CapacityValues the safety officer and superintendent time freed by digitizing safety observation reporting, JHA distribution, and inspection checklists. Hours are compared per field labor hour worked, so more crews on site are not read as slower reporting: the fall in weekly safety admin hours per field hour × this period's field hours × weeks × blended safety staff rate. | Freed capacity | (baseline_safety_hours_per_field_hour - current_safety_hours_per_field_hour) * current_field_hours * weeks_in_period * labor_rate_cents_per_hour | weeks_in_periodcurrent_field_hours — reading of construction.labor.hours_workedbaseline_field_hours — baseline of construction.labor.hours_workedlabor_rate_cents_per_hour — assumptioncurrent_safety_hours_per_field_hourbaseline_safety_hours_per_field_hourcurrent_safety_observation_hours_per_week — reading of construction.safety.observation_hours_per_weekbaseline_safety_observation_hours_per_week — baseline of construction.safety.observation_hours_per_week |
| Timecard Error Correction Freed CapacityValues the payroll admin time freed once paper timecard mistakes are eliminated: each error takes a manual correction. Total timecards × error reduction × the minutes to correct one error, at their loaded hourly rate. Payroll reprocessing fees a correction triggers are cash and are not counted here. | Freed capacity | timecards_processed_per_period * (baseline_timecard_error_rate_pct - current_timecard_error_rate_pct) / 100 * avg_correction_cost_cents | avg_correction_cost_centslabor_rate_cents_per_hour — assumptiontimecards_processed_per_period — reading of construction.payroll.timecards_processedbaseline_minutes_per_correction — baseline of construction.payroll.minutes_per_timecard_correctioncurrent_timecard_error_rate_pct — reading of construction.payroll.timecard_error_ratebaseline_timecard_error_rate_pct — baseline of construction.payroll.timecard_error_rate |
Education
| Formula | Counts as | Arithmetic | Inputs |
|---|---|---|---|
| Admissions Staff Freed CapacityValues the admissions team time freed by automating application review, document verification, and routine communications. Applications processed × staff minutes saved per application × staff rate. The minutes are hands-on review time, not the days an application waits for a decision. | Freed capacity | applications_processed * (baseline_review_minutes_per_application - current_review_minutes_per_application) * labor_rate_cents_per_minute | applications_processed — reading of operations.applications.processed_countlabor_rate_cents_per_hour — assumptionlabor_rate_cents_per_minutecurrent_review_minutes_per_application — reading of education.admissions.review_minutes_per_applicationbaseline_review_minutes_per_application — baseline of education.admissions.review_minutes_per_application |
| Advising & Outreach Freed CapacityValues the advising and student-success staff time freed by automating routine at-risk identification and initial outreach, freeing staff for high-touch interventions. Hours are compared per enrolled student, so a larger intake is not read as slower outreach: the fall in weekly outreach hours per student × the students enrolled now × weeks × staff rate. | Freed capacity | (baseline_outreach_hours_per_student - current_outreach_hours_per_student) * current_students * weeks_in_period * labor_rate_cents_per_hour | weeks_in_periodcurrent_students — reading of education.enrollment.student_countbaseline_students — baseline of education.enrollment.student_countlabor_rate_cents_per_hour — assumptioncurrent_outreach_hours_per_week — reading of education.outreach.hours_per_weekbaseline_outreach_hours_per_week — baseline of education.outreach.hours_per_weekcurrent_outreach_hours_per_studentbaseline_outreach_hours_per_student |
| DFW Rate Reduction — Revenue ProtectionEstimates tuition revenue protected by reducing the rate of students who fail, withdraw, or earn a D. A D, F or W does not always end a student's enrollment, so only the departures it adds count: enrolled students × DFW improvement × the extra share of DFW students who leave × the period's share of annual revenue per student. Only the share of that change you credit to this initiative counts: instructors, grading policy, course design and how prepared each cohort is move the DFW rate too. Booked at your contribution margin: the revenue less the costs that grow with it. | Cash: revenue gain | enrolled_students * (baseline_dfw_rate_pct - current_dfw_rate_pct) / 100 * dfw_attrition_rate_decimal * avg_annual_revenue_per_student_cents * months_in_period / 12 * attributable_share_decimal * contribution_margin_decimal | months_in_periodenrolled_students — reading of education.enrollment.student_countcurrent_dfw_rate_pct — reading of education.courses.dfw_ratebaseline_dfw_rate_pct — baseline of education.courses.dfw_rateattributable_share_decimal — your share of the change, a judgment with no defaultdfw_attrition_rate_decimal — assumption, with no defaultcontribution_margin_decimal — your contribution margin, with no defaultavg_annual_revenue_per_student_cents — assumption, with no default |
| Faculty Administrative Freed CapacityValues the faculty hours freed from course administration — syllabus drafting, assessment creation, objective grading, and content formatting. Faculty × hours saved per week × weeks × faculty hourly cost. | Freed capacity | faculty_count * (baseline_admin_hours_per_week - current_admin_hours_per_week) * weeks_in_period * labor_rate_cents_per_hour | faculty_count — reading of education.faculty.countweeks_in_periodlabor_rate_cents_per_hour — assumptioncurrent_admin_hours_per_week — reading of education.faculty.admin_hours_per_weekbaseline_admin_hours_per_week — baseline of education.faculty.admin_hours_per_week |
| Improved Yield RevenueEstimates incremental tuition revenue from improving enrollment yield through faster decisions and personalized outreach. Yield is enrolled ÷ admitted, so the gain applies to students admitted, not to every applicant: admitted × yield improvement × the period's share of avg annual revenue per enrolled student — a month's tuition from the added enrollees. Only the share of that change you credit to this initiative counts: financial aid offers, competing institutions, demographics and the economy move the yield rate too. Booked at your contribution margin: the revenue less the costs that grow with it. | Cash: revenue gain | admitted_in_period * (current_yield_rate_pct - baseline_yield_rate_pct) / 100 * avg_annual_revenue_per_enrolled_student_cents * months_in_period / 12 * attributable_share_decimal * contribution_margin_decimal | months_in_periodadmitted_in_period — reading of education.admissions.admitted_countcurrent_yield_rate_pct — reading of education.admissions.yield_ratebaseline_yield_rate_pct — baseline of education.admissions.yield_rateattributable_share_decimal — your share of the change, a judgment with no defaultcontribution_margin_decimal — your contribution margin, with no defaultavg_annual_revenue_per_enrolled_student_cents — assumption, with no default |
| Retained Student Revenue ValueEstimates revenue protected by improving retention. Each percentage point of retention improvement keeps more students enrolled and paying tuition. Retention gain × enrolled students × the period's share of annual revenue per student: a month's tuition from the students kept. Only the share of that change you credit to this initiative counts: students' finances and aid, how prepared each cohort is, advising staff and the job market move the retention rate too. Booked at your contribution margin: the revenue less the costs that grow with it. | Cash: revenue gain | (current_retention_rate_pct - baseline_retention_rate_pct) / 100 * students_enrolled * avg_annual_revenue_per_student_cents * months_in_period / 12 * attributable_share_decimal * contribution_margin_decimal | months_in_periodstudents_enrolled — reading of education.enrollment.student_countattributable_share_decimal — your share of the change, a judgment with no defaultcurrent_retention_rate_pct — reading of education.enrollment.retention_ratebaseline_retention_rate_pct — baseline of education.enrollment.retention_ratecontribution_margin_decimal — your contribution margin, with no defaultavg_annual_revenue_per_student_cents — assumption, with no default |
| Tutoring Cost ReductionEstimates savings from replacing a portion of labor-intensive human tutoring hours with AI tutoring. Reduction in tutoring cost per student in the period × enrolled students. | Cash: cost savings | (baseline_tutoring_cost_per_student_usd - current_tutoring_cost_per_student_usd) * enrolled_students * 100 | enrolled_students — reading of education.enrollment.student_countcurrent_tutoring_cost_per_student_usd — reading of education.tutoring.cost_per_studentbaseline_tutoring_cost_per_student_usd — baseline of education.tutoring.cost_per_student |
Energy utilities
| Formula | Counts as | Arithmetic | Inputs |
|---|---|---|---|
| CapEx Deferral ValueEstimates the financial value of deferring asset replacement by extending healthy asset life through condition-based management. Deferred spend × the period's share of the annual cost of capital: what it costs to finance that spend for the period it stays deferred, booked month by month for as long as the deferral holds. | Cash: cost savings | deferred_capex_usd * annual_cost_of_capital_decimal * months_in_period / 12 * 100 | months_in_perioddeferred_capex_usd — reading of energy.assets.deferred_capexannual_cost_of_capital_decimal — assumption |
| External Counsel Cost ReductionEstimates external legal spend saved when AI-assisted drafting reduces reliance on outside counsel for regulatory filings and rate case support. Outside counsel hours saved per filing × filings in the period × counsel's billing rate. | Cash: cost savings | (baseline_external_counsel_hours - current_external_counsel_hours) * rate_cases_per_period * external_counsel_rate_cents_per_hour | rate_cases_per_period — reading of energy.rate_cases.countcurrent_external_counsel_hours — reading of energy.rate_cases.external_counsel_hoursbaseline_external_counsel_hours — baseline of energy.rate_cases.external_counsel_hoursexternal_counsel_rate_cents_per_hour — assumption, with no default |
| Forecasting & Planning Freed CapacityValues the planning and trading team time freed when AI replaces manual load forecasting and scenario modeling. Hours saved per cycle × cycles per period × blended staff rate. | Freed capacity | (baseline_forecast_hours_per_cycle - current_forecast_hours_per_cycle) * forecast_cycles_per_period * labor_rate_cents_per_hour | labor_rate_cents_per_hour — assumptionforecast_cycles_per_period — reading of operations.forecasting.cyclescurrent_forecast_hours_per_cycle — reading of operations.forecasting.hours_per_cyclebaseline_forecast_hours_per_cycle — baseline of operations.forecasting.hours_per_cycle |
| Outage Notification Freed CapacityValues the operations and communications team time freed by automating customer outage status updates and ETR notifications, replacing manual call trees and email blasts. Staff hours saved per event × the events in the period (a year's events spread evenly over its months) × blended rate. | Freed capacity | (baseline_notification_hours_per_event - current_notification_hours_per_event) * storm_events_per_year * months_in_period / 12 * labor_rate_cents_per_hour | months_in_periodstorm_events_per_year — reading of energy.storms.events_per_yearlabor_rate_cents_per_hour — assumptioncurrent_notification_hours_per_event — reading of energy.storms.notification_hours_per_eventbaseline_notification_hours_per_event — baseline of energy.storms.notification_hours_per_event |
| Rate Case Internal Freed CapacityValues the internal legal, regulatory, and engineering hours freed by AI-assisted drafting of rate case filings and regulatory responses. Hours saved per filing × filings per period × blended rate. | Freed capacity | (baseline_rate_case_prep_hours - current_rate_case_prep_hours) * rate_cases_per_period * labor_rate_cents_per_hour | rate_cases_per_period — reading of energy.rate_cases.countlabor_rate_cents_per_hour — assumptioncurrent_rate_case_prep_hours — reading of energy.rate_cases.prep_hoursbaseline_rate_case_prep_hours — baseline of energy.rate_cases.prep_hours |
| Reserve Requirement Reduction SavingsEstimates capacity cost savings from reducing required reserves due to tighter AI load forecasts. More accurate forecasts mean less buffer capacity must be procured or held idle. Reserves are compared per MW of peak demand, so load growth is not read as a worse forecast: the fall in reserve per MW of peak × this period's peak × capacity cost per MW per period. | Cash: cost savings | (baseline_reserve_per_peak_mw - current_reserve_per_peak_mw) * current_peak_demand_mw * capacity_cost_cents_per_mw_per_period | current_peak_demand_mw — reading of energy.grid.peak_demand_mwbaseline_peak_demand_mw — baseline of energy.grid.peak_demand_mwcurrent_reserve_capacity_mw — reading of energy.grid.reserve_capacity_mwcurrent_reserve_per_peak_mwbaseline_reserve_capacity_mw — baseline of energy.grid.reserve_capacity_mwbaseline_reserve_per_peak_mwcapacity_cost_cents_per_mw_per_period — assumption, with no default |
| Storm Call Center Freed CapacityValues the call-center time freed when proactive ETR communications deflect inbound customer calls during storm events. Calls deflected per event × the events in the period (a year's events spread evenly over its months) × cost per call. Only the share of that change you credit to this initiative counts: storm severity, how many customers lose power and how long restoration takes move calls per storm too. | Freed capacity | (baseline_storm_calls_per_event - current_storm_calls_per_event) * storm_events_per_year * months_in_period / 12 * avg_cost_per_call_cents * attributable_share_decimal | months_in_periodstorm_events_per_year — reading of energy.storms.events_per_yearavg_cost_per_call_centsbaseline_minutes_per_call — baseline of support.calls.avg_handle_timelabor_rate_cents_per_hour — assumptionattributable_share_decimal — your share of the change, a judgment with no defaultcurrent_storm_calls_per_event — reading of energy.storms.call_volume_indexbaseline_storm_calls_per_event — baseline of energy.storms.call_volume_index |
| Unplanned Outage Cost AvoidanceEstimates the cost avoided by preventing unplanned critical-asset failures. Each avoided outage eliminates emergency restoration labor, contractor spend, regulatory penalties, and customer compensation. Outages are compared per critical asset in service, so a larger fleet of assets is not read as more failures: the fall in outages per asset a year × the assets in service now, spread evenly over the year's months, × average all-in cost per event. | Cash: cost avoidance | (baseline_outages_per_asset_per_year - current_outages_per_asset_per_year) * current_critical_assets * months_in_period / 12 * avg_outage_event_cost_cents | months_in_periodcurrent_critical_assets — reading of energy.assets.critical_countbaseline_critical_assets — baseline of energy.assets.critical_countavg_outage_event_cost_cents — assumption, with no defaultcurrent_outages_per_asset_per_yearbaseline_outages_per_asset_per_yearcurrent_unplanned_outage_events_per_year — reading of energy.assets.unplanned_outages_per_yearbaseline_unplanned_outage_events_per_year — baseline of energy.assets.unplanned_outages_per_year |
Financial services
| Formula | Counts as | Arithmetic | Inputs |
|---|---|---|---|
| Advisor Capacity Revenue ValueEstimates revenue from enabling advisors to serve more accounts at the same service quality. AI reduces prep time per client interaction, expanding effective capacity. Additional accounts per advisor × advisor count × the period's share of average annual revenue per account. Booked at your contribution margin: the revenue less the costs that grow with it. | Cash: revenue gain | (current_accounts_per_advisor - baseline_accounts_per_advisor) * advisor_count * avg_annual_revenue_per_account_cents * months_in_period / 12 * contribution_margin_decimal | advisor_count — reading of financial_services.advisors.countmonths_in_periodcontribution_margin_decimal — your contribution margin, with no defaultcurrent_accounts_per_advisor — reading of financial_services.advisors.accounts_per_advisorbaseline_accounts_per_advisor — baseline of financial_services.advisors.accounts_per_advisoravg_annual_revenue_per_account_cents — assumption, with no default |
| Data Error Remediation Freed CapacityValues the staff time freed by eliminating manual data errors that require correction, resubmission, regulatory inquiries, or internal investigation. The error rate is a share of line items, so it applies to the line items filed: report cycles in the period (a year's cycles spread evenly over its months) × line items in a cycle's reports × reduction in error rate × average cost to correct an erroneous line item. | Freed capacity | report_cycles_per_year * months_in_period / 12 * line_items_per_cycle * (baseline_error_rate_pct - current_error_rate_pct) / 100 * avg_error_remediation_cost_cents | months_in_periodline_items_per_cycle — reading of financial_services.regulatory_reporting.line_items_per_cyclecurrent_error_rate_pct — reading of financial_services.regulatory_reporting.error_ratereport_cycles_per_year — assumptionbaseline_error_rate_pct — baseline of financial_services.regulatory_reporting.error_rateavg_error_remediation_cost_cents — assumption, with no default |
| False Positive Investigation Freed CapacityValues the fraud operations team time freed by reducing false positive alerts. Fewer false positives means fewer unnecessary investigations, fewer blocked legitimate customers, and lower operational burden. | Freed capacity | transactions_flagged * (baseline_false_positive_rate_pct - current_false_positive_rate_pct) / 100 * avg_investigation_cost_cents | transactions_flagged — reading of financial_services.fraud.flagged_countlabor_rate_cents_per_hour — assumptionavg_investigation_cost_centscurrent_false_positive_rate_pct — reading of financial_services.fraud.false_positive_ratebaseline_false_positive_rate_pct — baseline of financial_services.fraud.false_positive_ratebaseline_minutes_per_investigation — baseline of financial_services.fraud.minutes_per_investigation |
| Faster Onboarding ValueEstimates what onboarding customers faster is worth. An account approved in hours rather than days earns the same revenue over its life, starting sooner: the days of revenue gained at the start are given back when the account closes, so what they are worth is the cost of capital on their margin over the years between. Accounts onboarded × days saved × average daily revenue per account × expected account life in years × annual cost of capital. A straight-line estimate that runs high for accounts that stay many years, and is never worth more than the days of revenue themselves. Valued at your contribution margin, because an account that starts sooner brings the costs of serving it forward too: only the margin arrives early. | Cash: cost savings | accounts_onboarded * (baseline_onboarding_days - current_onboarding_days) * avg_daily_revenue_per_account_cents * avg_account_life_years * annual_cost_of_capital_decimal * contribution_margin_decimal | accounts_onboarded — reading of financial_services.onboarding.accounts_countavg_account_life_years — assumption, with no defaultcurrent_onboarding_days — reading of financial_services.onboarding.days_to_activebaseline_onboarding_days — baseline of financial_services.onboarding.days_to_activecontribution_margin_decimal — your contribution margin, with no defaultannual_cost_of_capital_decimal — assumptionavg_daily_revenue_per_account_cents — assumption, with no default |
| Fraud Loss ReductionEstimates the direct reduction in fraud losses from improved detection accuracy. Better ML models catch more actual fraud before payment clears. Total transaction volume × reduction in fraud loss rate (in basis points). Only the share of that change you credit to this initiative counts: fraud trends, merchant and channel mix, card network rules and the economy move the fraud loss rate too. | Cash: cost avoidance | total_transaction_volume_usd * (baseline_fraud_loss_rate_bps - current_fraud_loss_rate_bps) / 10000 * 100 * attributable_share_decimal | attributable_share_decimal — your share of the change, a judgment with no defaultcurrent_fraud_loss_rate_bps — reading of financial_services.fraud.loss_rate_bpsbaseline_fraud_loss_rate_bps — baseline of financial_services.fraud.loss_rate_bpstotal_transaction_volume_usd — reading of financial_services.transactions.volume |
| Incremental Revenue from Expanded ApprovalsEstimates the revenue earned in the period from approving creditworthy applicants that rules-based systems would decline. A loan earns its revenue over its term, not the day it is approved, and these are the marginal applicants, so what one is worth is its lifetime revenue less its expected credit loss, spread over its term: applications × reduction in manual review rate × the share of those the AI approves that the old process declined, measured on a back-test of past applications × (lifetime revenue × contribution margin − expected loss) × months in the period ÷ loan term in months. The revenue is taken at your contribution margin, what is left after funding and servicing the loan, before the expected loss comes off. Once the added loans are a steady book, that is what they earn each month; while the book is still building it errs low. | Cash: revenue gain | applications_processed * (baseline_manual_review_rate_pct - current_manual_review_rate_pct) / 100 * incremental_approval_rate_decimal * (avg_loan_revenue_cents * contribution_margin_decimal - avg_expected_loss_cents) * months_in_period / avg_loan_term_months | months_in_periodavg_loan_term_months — assumption, with no defaultapplications_processed — reading of operations.applications.processed_countavg_loan_revenue_cents — assumption, with no defaultavg_expected_loss_cents — assumption, with no defaultcontribution_margin_decimal — your contribution margin, with no defaultoverturned_decline_rate_pct — baseline of financial_services.underwriting.overturned_decline_ratecurrent_manual_review_rate_pct — reading of financial_services.underwriting.manual_review_ratebaseline_manual_review_rate_pct — baseline of financial_services.underwriting.manual_review_rateincremental_approval_rate_decimal |
| KYC Review Freed CapacityValues the compliance team time freed by automating document review, sanctions screening, and identity verification. Accounts onboarded × hours saved per account × blended compliance analyst rate. | Freed capacity | accounts_onboarded * (baseline_review_hours_per_account - current_review_hours_per_account) * labor_rate_cents_per_hour | accounts_onboarded — reading of financial_services.onboarding.accounts_countlabor_rate_cents_per_hour — assumptioncurrent_review_hours_per_account — reading of financial_services.kyc.review_hours_per_accountbaseline_review_hours_per_account — baseline of financial_services.kyc.review_hours_per_account |
| Proactive Outreach Revenue ValueEstimates the fee revenue from assets gathered by advisors reaching out at higher-value moments. More proactive outreach means catching life events (inheritances, rollovers, rebalancing needs) that drive inflows and referrals. Organic growth counts net new assets, inflows less outflows, so market moves are left out: AUM × rise in the annualized organic growth rate × months ÷ 12 gives the added assets; the value is the advisory fee they earn in the period, not the assets themselves. Only the share of that rise you credit to this initiative counts: markets, referrals, hiring and pricing move organic growth too. Booked at your contribution margin: the revenue less the costs that grow with it. | Cash: revenue gain | advisor_count * avg_aum_per_advisor_usd * (current_organic_growth_rate_pct - baseline_organic_growth_rate_pct) / 100 * advisory_fee_rate_decimal * months_in_period / 12 * 100 * attributable_share_decimal * contribution_margin_decimal | advisor_count — reading of financial_services.advisors.countmonths_in_periodavg_aum_per_advisor_usd — baseline of financial_services.advisors.aum_per_advisoradvisory_fee_rate_decimal — assumption, with no defaultattributable_share_decimal — your share of the change, a judgment with no defaultcontribution_margin_decimal — your contribution margin, with no defaultcurrent_organic_growth_rate_pct — reading of financial_services.advisors.organic_growth_ratebaseline_organic_growth_rate_pct — baseline of financial_services.advisors.organic_growth_rate |
| Report Preparation Freed CapacityValues the compliance and finance team time freed by automating regulatory data aggregation and submission workflows. Hours saved per reporting cycle × the reporting cycles in the period (a year's cycles spread evenly over its months) × blended compliance rate. | Freed capacity | (baseline_report_prep_hours - current_report_prep_hours) * report_cycles_per_year * months_in_period / 12 * labor_rate_cents_per_hour | months_in_periodreport_cycles_per_year — assumptioncurrent_report_prep_hours — reading of financial_services.regulatory_reporting.prep_hours_per_reportlabor_rate_cents_per_hour — assumptionbaseline_report_prep_hours — baseline of financial_services.regulatory_reporting.prep_hours_per_report |
| Underwriting Freed CapacityValues the underwriter time freed when AI handles automated approvals, reducing the volume of applications requiring manual review. Applications processed × underwriter hours saved per application × analyst rate. The hours are hands-on review time averaged over every application, an automatically decided one counting as none — not the elapsed time to a decision, which is mostly waiting. | Freed capacity | applications_processed * (baseline_review_hours_per_application - current_review_hours_per_application) * labor_rate_cents_per_hour | applications_processed — reading of operations.applications.processed_countlabor_rate_cents_per_hour — assumptioncurrent_review_hours_per_application — reading of financial_services.underwriting.review_hours_per_applicationbaseline_review_hours_per_application — baseline of financial_services.underwriting.review_hours_per_application |
Government public sector
| Formula | Counts as | Arithmetic | Inputs |
|---|---|---|---|
| Call Deflection Freed CapacityValues the contact-center time freed by deflecting routine 311 and constituent service requests into AI self-service. Calls are compared per service request, so a storm week or a tax deadline is not read as a loss nor a quiet month as a gain: the fall in calls per request × this month's requests × months × cost per call. | Freed capacity | (baseline_calls_per_request - current_calls_per_request) * current_requests_per_month * months_in_period * avg_cost_per_call_cents | months_in_periodavg_cost_per_call_cents — assumption, with no defaultcurrent_calls_per_month — reading of government.constituent_services.calls_per_monthbaseline_calls_per_month — baseline of government.constituent_services.calls_per_monthcurrent_calls_per_requestbaseline_calls_per_requestcurrent_requests_per_month — reading of government.constituent_services.requests_per_monthbaseline_requests_per_month — baseline of government.constituent_services.requests_per_month |
| Case Processing Freed CapacityValues the caseworker time freed when AI handles document extraction, eligibility rules checking, and initial routing — freeing staff for complex cases and applicant support. Cases × caseworker hours saved per case × blended rate. The hours are hands-on work, not the days a case waits for a decision. | Freed capacity | cases_processed_per_period * (baseline_processing_hours_per_case - current_processing_hours_per_case) * labor_rate_cents_per_hour | labor_rate_cents_per_hour — assumptioncases_processed_per_period — reading of government.cases.processed_countcurrent_processing_hours_per_case — reading of government.cases.staff_hours_per_casebaseline_processing_hours_per_case — baseline of government.cases.staff_hours_per_case |
| Permit Processing Freed CapacityValues the staff time freed when AI automates permit intake, document review, and inspection scheduling — reducing manual processing hours per permit. Permits × staff hours saved per permit × blended rate. The hours are hands-on work, not the days a permit waits for a decision. | Freed capacity | permits_processed_per_period * (baseline_processing_hours_per_permit - current_processing_hours_per_permit) * labor_rate_cents_per_hour | labor_rate_cents_per_hour — assumptionpermits_processed_per_period — reading of government.permits.processed_countcurrent_processing_hours_per_permit — reading of government.permits.staff_hours_per_permitbaseline_processing_hours_per_permit — baseline of government.permits.staff_hours_per_permit |
| Staff Redeployment Freed CapacityEstimates the value of constituent-services staff capacity freed by self-service deflection, redeployable to complex cases and high-touch constituent support that requires human judgment. Calls are compared per service request, so a busy month's extra calls are not set against the initiative. The same deflected contacts are what call deflection freed capacity prices at their full cost, staff time included, so an initiative gets one of the two. | Freed capacity | (baseline_calls_per_request - current_calls_per_request) * current_requests_per_month * months_in_period * avg_handle_time_minutes * labor_rate_cents_per_minute | months_in_periodavg_handle_time_minutes — reading of support.calls.avg_handle_timecurrent_calls_per_month — reading of government.constituent_services.calls_per_monthbaseline_calls_per_month — baseline of government.constituent_services.calls_per_monthcurrent_calls_per_requestlabor_rate_cents_per_hour — assumptionbaseline_calls_per_requestcurrent_requests_per_month — reading of government.constituent_services.requests_per_monthbaseline_requests_per_month — baseline of government.constituent_services.requests_per_monthlabor_rate_cents_per_minute |
Healthcare life sciences
| Formula | Counts as | Arithmetic | Inputs |
|---|---|---|---|
| Added Visit RevenueRevenue on the visits clinicians actually added once documentation took less of their day: the rise in encounters per clinician per day × clinicians × working days × average visit revenue. Reclaimed time is revenue only where it shows up as visits, so this counts the visits rather than assuming a share of the time became them. Booked at your contribution margin: the revenue less the costs that grow with it. Clinician documentation freed capacity on the same template values the reclaimed time itself, at the clinician's hourly cost, so an initiative gets one of the two. | Cash: revenue gain | clinician_count * (current_encounters_per_clinician_per_day - baseline_encounters_per_clinician_per_day) * working_days_in_period * avg_visit_revenue_cents * contribution_margin_decimal | clinician_count — reading of healthcare.documentation.clinician_countworking_days_in_periodavg_visit_revenue_cents — assumption, with no defaultcontribution_margin_decimal — your contribution margin, with no defaultcurrent_encounters_per_clinician_per_day — reading of healthcare.documentation.encounters_per_clinician_per_daybaseline_encounters_per_clinician_per_day — baseline of healthcare.documentation.encounters_per_clinician_per_day |
| AR Day Reduction Cash ValueEstimates the working capital benefit of collecting cash faster. Each day of AR reduction frees cash that was previously tied up in receivables. Monthly revenue × days reduced × annual cost of capital ÷ 365. Only the share of that change you credit to this initiative counts: payer mix, payer policy and processing times, and billing staff levels move days in AR too. | Cash: cost savings | average_monthly_revenue_cents * (baseline_days_in_ar - current_days_in_ar) * annual_cost_of_capital_decimal / 365 * attributable_share_decimal | current_days_in_ar — reading of healthcare.revenue_cycle.days_in_arbaseline_days_in_ar — baseline of healthcare.revenue_cycle.days_in_arfinancials_revenue_usd — reading of finance.pnl.revenueattributable_share_decimal — your share of the change, a judgment with no defaultaverage_monthly_revenue_centsannual_cost_of_capital_decimal — assumption |
| Audit Preparation Freed CapacityValues the compliance team time freed by maintaining always-ready documentation rather than scrambling to prepare evidence for audits. Hours saved per cycle × the audit cycles in the period (a year's cycles spread evenly over its months) × blended compliance rate. | Freed capacity | (baseline_audit_prep_hours - current_audit_prep_hours) * audit_cycles_per_year * months_in_period / 12 * labor_rate_cents_per_hour | months_in_periodaudit_cycles_per_year — assumptioncurrent_audit_prep_hours — reading of healthcare.audits.prep_hours_per_cyclebaseline_audit_prep_hours — baseline of healthcare.audits.prep_hours_per_cyclelabor_rate_cents_per_hour — assumption |
| Clinician Documentation Freed CapacityValues the clinical time freed when ambient AI eliminates after-hours charting. Fewer documentation hours means reduced burnout and time that can be redeployed to patients. Locum or overtime spend that falls because of it is cash, recorded as capacity realized as cash with the document that shows it. Clinicians × weekly hours saved × weeks × hourly clinical rate. Added visit revenue on the same template values the same saved documentation time as extra visits, so an initiative gets one of the two. | Freed capacity | clinician_count * (baseline_after_hours_charting_hours - current_after_hours_charting_hours) * weeks_in_period * labor_rate_cents_per_hour | clinician_count — reading of healthcare.documentation.clinician_countweeks_in_periodlabor_rate_cents_per_hour — assumptioncurrent_after_hours_charting_hours — reading of healthcare.documentation.after_hours_charting_hours_per_weekbaseline_after_hours_charting_hours — baseline of healthcare.documentation.after_hours_charting_hours_per_week |
| Coder Productivity ValueEstimates revenue from increased coder throughput. AI-suggested codes allow coders to process more charts per day, reducing backlogs and accelerating the chart-to-claim timeline. Coders × incremental charts per day × avg revenue per chart × working days. Booked at your contribution margin: the revenue less the costs that grow with it. | Cash: revenue gain | coder_count * (current_charts_per_coder_per_day - baseline_charts_per_coder_per_day) * working_days_in_period * avg_revenue_per_chart_cents * contribution_margin_decimal | coder_count — reading of healthcare.coding.coder_countworking_days_in_periodavg_revenue_per_chart_cents — assumption, with no defaultcontribution_margin_decimal — your contribution margin, with no defaultcurrent_charts_per_coder_per_day — reading of healthcare.coding.charts_per_coder_per_daybaseline_charts_per_coder_per_day — baseline of healthcare.coding.charts_per_coder_per_day |
| Coding Error Revenue RecoveryEstimates revenue recovered by reducing coding errors that cause denials, downcoding, or missed HCC captures. Fewer coding errors means more accurate, complete reimbursement. The work was delivered and paid for either way, so the price recovered is all margin, and it books in full. | Cash: revenue gain | charts_coded_per_period * (baseline_coding_error_rate_pct - current_coding_error_rate_pct) / 100 * avg_revenue_impact_per_coding_error_cents | charts_coded_per_period — reading of healthcare.coding.charts_codedcurrent_coding_error_rate_pct — reading of healthcare.coding.error_ratebaseline_coding_error_rate_pct — baseline of healthcare.coding.error_rateavg_revenue_impact_per_coding_error_cents — assumption, with no default |
| Denial Recovery RevenueEstimates incremental revenue recovered by improving denial appeal success rates. Faster routing and automated appeals documentation means more denied claims are overturned before the appeal deadline. The work was delivered and paid for either way, so the price recovered is all margin, and it books in full. | Cash: revenue gain | claims_denied_per_period * avg_claim_value_cents * (current_appeal_success_rate_decimal - baseline_appeal_success_rate_decimal) | avg_claim_value_cents — assumption, with no defaultclaims_denied_per_period — reading of healthcare.claims.denied_countcurrent_appeal_success_rate_decimal — reading of healthcare.denials.appeal_success_ratebaseline_appeal_success_rate_decimal — baseline of healthcare.denials.appeal_success_rate |
| No-Show Reduction RevenueEstimates revenue recovered by reducing appointment no-shows. Each prevented no-show allows the slot to be filled by a waiting patient or avoided as wasted clinical capacity. Only the share of that change you credit to this initiative counts: weather, flu season, patient mix, transport and scheduling policy move the no-show rate too. Booked at your contribution margin: the revenue less the costs that grow with it. | Cash: revenue gain | scheduled_appointments_per_period * (baseline_no_show_rate_pct - current_no_show_rate_pct) / 100 * avg_visit_revenue_cents * attributable_share_decimal * contribution_margin_decimal | avg_visit_revenue_cents — assumption, with no defaultcurrent_no_show_rate_pct — reading of healthcare.appointments.no_show_ratebaseline_no_show_rate_pct — baseline of healthcare.appointments.no_show_rateattributable_share_decimal — your share of the change, a judgment with no defaultcontribution_margin_decimal — your contribution margin, with no defaultscheduled_appointments_per_period — reading of healthcare.appointments.scheduled_count |
| Outreach Program Freed CapacityValues the care team and administrative time freed by automating appointment reminders, care gap calls, and chronic disease management touchpoints. Hours are compared per appointment scheduled, so a fuller schedule is not read as slower outreach: the fall in weekly outreach hours per appointment × this period's appointments × weeks × staff rate. | Freed capacity | (baseline_outreach_hours_per_appointment - current_outreach_hours_per_appointment) * current_appointments * weeks_in_period * labor_rate_cents_per_hour | weeks_in_periodcurrent_appointments — reading of healthcare.appointments.scheduled_countbaseline_appointments — baseline of healthcare.appointments.scheduled_countlabor_rate_cents_per_hour — assumptioncurrent_outreach_hours_per_week — reading of healthcare.outreach.hours_per_weekbaseline_outreach_hours_per_week — baseline of healthcare.outreach.hours_per_weekcurrent_outreach_hours_per_appointmentbaseline_outreach_hours_per_appointment |
| Value-Based Contract Performance ValueEstimates revenue protected or earned by improving quality measure performance in value-based care contracts. Better HEDIS/MIPS scores translate directly to shared savings, quality bonuses, and avoided penalties. A performance year's revenue at risk × closure-rate improvement × payout sensitivity, × the period's share of the year. Only the share of that change you credit to this initiative counts: patients, providers, payer programs and how gaps are measured move the care-gap closure rate too. Booked at your contribution margin: the revenue less the costs that grow with it. | Cash: revenue gain | value_based_contract_revenue_at_risk_usd * (current_care_gap_closure_rate_pct - baseline_care_gap_closure_rate_pct) / 100 * quality_score_to_payout_multiplier_decimal * months_in_period / 12 * 100 * attributable_share_decimal * contribution_margin_decimal | months_in_periodattributable_share_decimal — your share of the change, a judgment with no defaultcontribution_margin_decimal — your contribution margin, with no defaultcurrent_care_gap_closure_rate_pct — reading of healthcare.quality.care_gap_closure_ratebaseline_care_gap_closure_rate_pct — baseline of healthcare.quality.care_gap_closure_ratevalue_based_contract_revenue_at_risk_usd — reading of healthcare.quality.value_based_revenue_at_riskquality_score_to_payout_multiplier_decimal — assumption, with no default |
Hospitality
| Formula | Counts as | Arithmetic | Inputs |
|---|---|---|---|
| Ancillary Spend UpliftEstimates incremental ancillary revenue (F&B, spa, upgrades, experiences) from personalized pre-arrival and in-stay offers. Guests × incremental ancillary spend per guest. Only the share of that change you credit to this initiative counts: guest mix, seasonality, prices and what the property offers move spend per guest too. Booked at your contribution margin: the revenue less the costs that grow with it. | Cash: revenue gain | guests_in_period * (current_ancillary_spend_per_guest_usd - baseline_ancillary_spend_per_guest_usd) * 100 * attributable_share_decimal * contribution_margin_decimal | guests_in_period — reading of hospitality.guests.countattributable_share_decimal — your share of the change, a judgment with no defaultcontribution_margin_decimal — your contribution margin, with no defaultcurrent_ancillary_spend_per_guest_usd — reading of hospitality.guests.ancillary_spend_per_guestbaseline_ancillary_spend_per_guest_usd — baseline of hospitality.guests.ancillary_spend_per_guest |
| F&B Prep Freed CapacityValues the kitchen and prep time freed when accurate forecasts eliminate over-preparation shifts and reduce last-minute scrambles. Prep hours are compared per cover served, so a full house is not read as over-preparation nor an empty one as a gain: the fall in prep hours per cover × this period's covers × labor rate. | Freed capacity | (baseline_prep_hours_per_cover - current_prep_hours_per_cover) * current_covers * labor_rate_cents_per_hour | current_covers — reading of hospitality.food_beverage.covers_servedbaseline_covers — baseline of hospitality.food_beverage.covers_servedlabor_rate_cents_per_hour — assumptioncurrent_prep_hours_per_coverbaseline_prep_hours_per_covercurrent_prep_hours_per_period — reading of hospitality.food_beverage.prep_hoursbaseline_prep_hours_per_period — baseline of hospitality.food_beverage.prep_hours |
| Food Waste Cost AvoidanceEstimates food cost recovered by reducing waste and spoilage through AI-driven demand forecasting and par-level optimization. F&B cost of goods × waste rate reduction. Only the share of that change you credit to this initiative counts: demand swings, menu changes, events and supplier quality move the waste share too. | Cash: cost savings | fb_cost_of_goods_usd * (baseline_waste_pct - current_waste_pct) / 100 * 100 * attributable_share_decimal | current_waste_pct — reading of hospitality.food_beverage.waste_sharebaseline_waste_pct — baseline of hospitality.food_beverage.waste_sharefb_cost_of_goods_usd — reading of hospitality.food_beverage.cost_of_goodsattributable_share_decimal — your share of the change, a judgment with no default |
| Housekeeping Freed CapacityValues the housekeeping time freed by reducing minutes per room turn through AI-optimized scheduling and task dispatch. Rooms turned × minutes saved per turn × labor rate per minute. | Freed capacity | rooms_turned_per_period * (baseline_minutes_per_room_turn - current_minutes_per_room_turn) * labor_rate_cents_per_minute | rooms_turned_per_period — reading of hospitality.housekeeping.rooms_turnedlabor_rate_cents_per_hour — assumptionlabor_rate_cents_per_minutecurrent_minutes_per_room_turn — reading of hospitality.housekeeping.minutes_per_room_turnbaseline_minutes_per_room_turn — baseline of hospitality.housekeeping.minutes_per_room_turn |
| OTA Commission SavingsEstimates commissions saved by shifting bookings from OTAs to direct channels through AI-powered conversational booking and personalized offers. Total room revenue × channel shift × OTA commission rate. Only the share of that change you credit to this initiative counts: loyalty programs, OTA ranking and promotions, and guest mix move the direct booking share too. | Cash: cost savings | total_room_revenue_usd * (current_direct_booking_share_pct - baseline_direct_booking_share_pct) / 100 * avg_ota_commission_rate_decimal * 100 * attributable_share_decimal | total_room_revenue_usd — reading of hospitality.rooms.revenueattributable_share_decimal — your share of the change, a judgment with no defaultavg_ota_commission_rate_decimal — assumptioncurrent_direct_booking_share_pct — reading of hospitality.bookings.direct_sharebaseline_direct_booking_share_pct — baseline of hospitality.bookings.direct_share |
| Revenue Manager Freed CapacityValues the revenue management staff time freed when AI automates daily rate decisions, comp set monitoring, and reporting — freeing managers for strategy and multi-property oversight. | Freed capacity | (baseline_rate_management_hours_per_week - current_rate_management_hours_per_week) * weeks_in_period * labor_rate_cents_per_hour | weeks_in_periodlabor_rate_cents_per_hour — assumptioncurrent_rate_management_hours_per_week — reading of hospitality.revenue_management.hours_per_weekbaseline_rate_management_hours_per_week — baseline of hospitality.revenue_management.hours_per_week |
| RevPAR Uplift RevenueEstimates incremental room revenue from AI-driven rate optimization. Improvement in revenue per available room × total available room nights in the period. Only the share of that change you credit to this initiative counts: seasonality, local events, market demand and new supply move RevPAR too. Booked at your contribution margin: the revenue less the costs that grow with it. | Cash: revenue gain | (current_revpar_usd - baseline_revpar_usd) * available_room_nights_per_period * 100 * attributable_share_decimal * contribution_margin_decimal | current_revpar_usd — reading of hospitality.rooms.revparbaseline_revpar_usd — baseline of hospitality.rooms.revparattributable_share_decimal — your share of the change, a judgment with no defaultcontribution_margin_decimal — your contribution margin, with no defaultavailable_room_nights_per_period — reading of hospitality.rooms.available_room_nights |
Insurance
| Formula | Counts as | Arithmetic | Inputs |
|---|---|---|---|
| Adjudicator Freed CapacityValues the adjuster time freed on auto-adjudicated routine claims — time that can be redeployed to complex, high-severity claims. Claims × adjuster hours saved per claim × hourly rate. The hours are hands-on adjuster work averaged over every claim, a straight-through claim counting as none, so the drop already reflects the share auto-adjudicated; it is not the days from first notice of loss to close. Adjuster time is part of loss adjustment expense, which LAE reduction on the same template already counts, so an initiative gets one of the two. | Freed capacity | claims_processed_per_period * (baseline_adjuster_hours_per_claim - current_adjuster_hours_per_claim) * labor_rate_cents_per_hour | labor_rate_cents_per_hour — assumptionclaims_processed_per_period — reading of insurance.claims.processed_countcurrent_adjuster_hours_per_claim — reading of insurance.claims.adjuster_hours_per_claimbaseline_adjuster_hours_per_claim — baseline of insurance.claims.adjuster_hours_per_claim |
| Call Deflection Freed CapacityValues the call-center time freed by deflecting routine policy-servicing requests (endorsements, certificates, billing questions) into AI-assisted self-service. Calls are compared per servicing request, so a renewal-season surge is not read as a loss nor a quiet month as a gain: the fall in calls per request × this month's requests × months × cost per call. | Freed capacity | (baseline_calls_per_request - current_calls_per_request) * current_requests_per_month * months_in_period * avg_cost_per_call_cents | months_in_periodavg_cost_per_call_cents — assumption, with no defaultcurrent_calls_per_month — reading of insurance.policy_servicing.calls_per_monthbaseline_calls_per_month — baseline of insurance.policy_servicing.calls_per_monthcurrent_calls_per_requestbaseline_calls_per_requestcurrent_requests_per_month — reading of insurance.policy_servicing.requests_per_monthbaseline_requests_per_month — baseline of insurance.policy_servicing.requests_per_month |
| Claims Fraud Leakage RecoveryEstimates paid-loss reduction from detecting and denying or settling fraudulent claims earlier. Improvement in claims leakage rate × incurred losses. Only the share of that change you credit to this initiative counts: claim mix, catastrophe surges, SIU staffing and fraud trends in the market move the leakage rate too. | Cash: cost avoidance | incurred_losses_usd * (baseline_leakage_pct - current_leakage_pct) / 100 * 100 * attributable_share_decimal | current_leakage_pct — reading of insurance.claims.leakage_rateincurred_losses_usd — reading of insurance.claims.incurred_lossesbaseline_leakage_pct — baseline of insurance.claims.leakage_rateattributable_share_decimal — your share of the change, a judgment with no default |
| Loss Adjustment Expense (LAE) ReductionEstimates savings from reducing the loss adjustment expense ratio through automated triage and straight-through adjudication. Lower LAE means less adjuster time per claim without increasing error rates. Incurred losses × LAE ratio improvement. Only the share of that change you credit to this initiative counts: claim mix and severity, catastrophe events, litigation costs and staffing move the LAE ratio too. Adjudicator freed capacity on the same template prices the adjuster time inside LAE, so an initiative gets one of the two. | Cash: cost savings | incurred_losses_usd * (baseline_lae_ratio_pct - current_lae_ratio_pct) / 100 * 100 * attributable_share_decimal | incurred_losses_usd — reading of insurance.claims.incurred_lossescurrent_lae_ratio_pct — reading of insurance.claims.lae_ratiobaseline_lae_ratio_pct — baseline of insurance.claims.lae_ratioattributable_share_decimal — your share of the change, a judgment with no default |
| Loss Ratio Improvement ValueEstimates the underwriting profit from a lower loss ratio on the book the AI selects risks for: each point the ratio falls on the same earned premium is losses not paid. Earned premium × loss ratio reduction. Only the share of that change you credit to this initiative counts: weather and catastrophe losses, rate changes, reserve development and the book's mix move the loss ratio too. | Cash: cost avoidance | earned_premium_usd * (baseline_loss_ratio_pct - current_loss_ratio_pct) / 100 * 100 * attributable_share_decimal | earned_premium_usd — reading of insurance.underwriting.earned_premiumcurrent_loss_ratio_pct — reading of insurance.underwriting.loss_ratiobaseline_loss_ratio_pct — baseline of insurance.underwriting.loss_ratioattributable_share_decimal — your share of the change, a judgment with no default |
| Service Agent Redeployment Freed CapacityEstimates the value of service agent capacity freed by self-service deflection, redeployed to retention, cross-sell, or complex high-value servicing instead of routine transactions. Calls are compared per servicing request, so a busy month's extra calls are not set against the initiative. The same deflected calls are what call deflection freed capacity prices at their full cost, agent time included, so an initiative gets one of the two. | Freed capacity | (baseline_calls_per_request - current_calls_per_request) * current_requests_per_month * months_in_period * avg_handle_time_minutes * labor_rate_cents_per_minute | months_in_periodavg_handle_time_minutes — reading of support.calls.avg_handle_timecurrent_calls_per_month — reading of insurance.policy_servicing.calls_per_monthbaseline_calls_per_month — baseline of insurance.policy_servicing.calls_per_monthcurrent_calls_per_requestlabor_rate_cents_per_hour — assumptionbaseline_calls_per_requestcurrent_requests_per_month — reading of insurance.policy_servicing.requests_per_monthbaseline_requests_per_month — baseline of insurance.policy_servicing.requests_per_monthlabor_rate_cents_per_minute |
| SIU Investigation Freed CapacityEstimates the value of SIU investigator time redirected from false-positive referrals to high-quality fraud leads. False-positive referrals are compared per claim processed, so a catastrophe month's surge in claims is not read as worse triage: the fall in false positives per claim × this period's claims × the investigator hours one investigation takes, at their loaded hourly rate. Vendor costs an investigation runs up are cash and are not counted here. | Freed capacity | (baseline_false_positives_per_claim - current_false_positives_per_claim) * current_claims_processed * avg_investigation_cost_cents | current_claims_processed — reading of insurance.claims.processed_countbaseline_claims_processed — baseline of insurance.claims.processed_countlabor_rate_cents_per_hour — assumptionavg_investigation_cost_centsbaseline_hours_per_investigation — baseline of insurance.fraud.hours_per_siu_investigationcurrent_false_positives_per_claimbaseline_false_positives_per_claimcurrent_false_positive_referrals_per_month — reading of insurance.fraud.false_positive_siu_referralsbaseline_false_positive_referrals_per_month — baseline of insurance.fraud.false_positive_siu_referrals |
| Underwriter Freed CapacityValues the underwriter time freed when AI pre-screens submissions and surfaces data, compressing manual review from days to hours on standard risks. Submissions × hours saved per submission × underwriter rate. | Freed capacity | submissions_per_period * (baseline_quote_hours - current_quote_hours) * labor_rate_cents_per_hour | current_quote_hours — reading of insurance.underwriting.quote_hoursbaseline_quote_hours — baseline of insurance.underwriting.quote_hourssubmissions_per_period — reading of insurance.underwriting.submission_countlabor_rate_cents_per_hour — assumption |
Manufacturing
| Formula | Counts as | Arithmetic | Inputs |
|---|---|---|---|
| Condition-Based Maintenance Cost ReductionEstimates maintenance spend saved by servicing equipment based on actual condition rather than fixed time schedules. Condition-based maintenance eliminates unnecessary preventive services and reduces catastrophic failure repair costs. Annual maintenance spend × reduction × the period's share of the year. The production lost while failed equipment is down is not maintenance spend; unplanned downtime cost avoidance on the same template counts it, so the two do not overlap. | Cash: cost savings | total_maintenance_spend_usd * maintenance_cost_reduction_pct / 100 * months_in_period / 12 * 100 | months_in_periodtotal_maintenance_spend_usd — reading of manufacturing.maintenance.annual_spendmaintenance_cost_reduction_pct — reading of manufacturing.maintenance.cost_reduction_rate |
| Cross-System Data Reconciliation Freed CapacityValues the time freed by eliminating manual data reconciliation between MES, SCADA, CMMS, and ERP systems. A unified digital thread means no more manual exports, spreadsheet reconciliations, and data-quality firefighting. The hours are priced at what they cost. | Freed capacity | (baseline_reconciliation_hours_per_week - current_reconciliation_hours_per_week) * weeks_in_period * labor_rate_cents_per_hour | weeks_in_periodlabor_rate_cents_per_hour — assumptioncurrent_reconciliation_hours_per_week — reading of manufacturing.data.reconciliation_hours_per_weekbaseline_reconciliation_hours_per_week — baseline of manufacturing.data.reconciliation_hours_per_week |
| Defect Escape & Warranty Cost ReductionEstimates the warranty liability and field service cost avoided by catching more defects before they escape to the customer. Fewer escaped defects means fewer warranty claims, field repairs, and recall risks. | Cash: cost avoidance | units_produced_per_period * (baseline_defect_escape_rate_pct - current_defect_escape_rate_pct) / 100 * avg_warranty_cost_per_escaped_defect_cents | units_produced_per_period — reading of manufacturing.production.units_producedcurrent_defect_escape_rate_pct — reading of manufacturing.quality.defect_escape_ratebaseline_defect_escape_rate_pct — baseline of manufacturing.quality.defect_escape_rateavg_warranty_cost_per_escaped_defect_cents — assumption, with no default |
| Material Shortage Stoppage Cost AvoidanceEstimates production cost avoided by eliminating material shortage-driven line stoppages. Real-time visibility into inventory and supply status allows planners to act before a shortage becomes a stoppage. Stoppages are compared per unit produced, so a heavier schedule is not read as worse planning: the fall in stoppages per unit × this period's units × cost per stoppage. | Cash: cost avoidance | (baseline_stoppages_per_unit - current_stoppages_per_unit) * current_units_produced * avg_stoppage_cost_cents | current_units_produced — reading of manufacturing.production.units_producedavg_stoppage_cost_cents — assumption, with no defaultbaseline_units_produced — baseline of manufacturing.production.units_producedcurrent_stoppages_per_unitbaseline_stoppages_per_unitcurrent_shortage_incidents_per_month — reading of manufacturing.materials.shortage_incidents_per_monthbaseline_shortage_incidents_per_month — baseline of manufacturing.materials.shortage_incidents_per_month |
| OEE Data Collection Freed CapacityValues the operations team time freed by replacing manual OEE data collection, loss categorization, and report building with automated real-time dashboards. The hours are priced at what they cost. | Freed capacity | (baseline_oee_reporting_hours_per_week - current_oee_reporting_hours_per_week) * weeks_in_period * labor_rate_cents_per_hour | weeks_in_periodlabor_rate_cents_per_hour — assumptioncurrent_oee_reporting_hours_per_week — reading of manufacturing.equipment.oee_reporting_hours_per_weekbaseline_oee_reporting_hours_per_week — baseline of manufacturing.equipment.oee_reporting_hours_per_week |
| OEE Improvement Production ValueTranslates OEE percentage point improvements directly into production throughput value. Each point of OEE improvement means more good units produced from the same assets. OEE gain × theoretical capacity × revenue per unit. Only the share of that change you credit to this initiative counts: demand and scheduling, maintenance, product mix and staffing move OEE too. Booked at your contribution margin: the revenue less the costs that grow with it. | Cash: revenue gain | (current_oee_pct - baseline_oee_pct) / 100 * theoretical_capacity_units_per_period * avg_revenue_per_unit_cents * attributable_share_decimal * contribution_margin_decimal | current_oee_pct — reading of manufacturing.equipment.oeebaseline_oee_pct — baseline of manufacturing.equipment.oeeattributable_share_decimal — your share of the change, a judgment with no defaultavg_revenue_per_unit_cents — assumption, with no defaultcontribution_margin_decimal — your contribution margin, with no defaulttheoretical_capacity_units_per_period — reading of manufacturing.production.theoretical_capacity_units |
| On-Time Delivery Improvement Revenue ValueEstimates revenue protected or unlocked by improving on-time delivery rates. Late deliveries create customer penalties, order cancellations, and lost future business. OTD improvement × revenue at risk. Only the share of that change you credit to this initiative counts: suppliers, carriers, demand swings and capacity move the on-time delivery rate too. Booked at your contribution margin: the revenue less the costs that grow with it. | Cash: revenue gain | (current_otd_rate_pct - baseline_otd_rate_pct) / 100 * revenue_at_risk_from_late_delivery_usd * 100 * attributable_share_decimal * contribution_margin_decimal | current_otd_rate_pct — reading of supply_chain.deliveries.on_time_ratebaseline_otd_rate_pct — baseline of supply_chain.deliveries.on_time_rateattributable_share_decimal — your share of the change, a judgment with no defaultcontribution_margin_decimal — your contribution margin, with no defaultrevenue_at_risk_from_late_delivery_usd — reading of supply_chain.deliveries.revenue_at_risk |
| Production Scheduling Freed CapacityValues the planning and operations team time freed by replacing manual schedule building with AI optimization. Hours saved per week × weeks × planner rate. | Freed capacity | (baseline_scheduling_hours_per_week - current_scheduling_hours_per_week) * weeks_in_period * labor_rate_cents_per_hour | weeks_in_periodlabor_rate_cents_per_hour — assumptioncurrent_scheduling_hours_per_week — reading of manufacturing.scheduling.hours_per_weekbaseline_scheduling_hours_per_week — baseline of manufacturing.scheduling.hours_per_week |
| Scrap & Rework Cost ReductionMeasures the direct reduction in material scrap and rework labor costs from catching defects earlier in the production process. Real-time detection prevents defective work-in-progress from accumulating before the defect is caught. Scrap cost is compared per unit produced, so a heavy production month is not read as a loss nor a short one as a saving: the fall in scrap cost per unit × this period's units. | Cash: cost savings | (baseline_scrap_cost_per_unit_usd - current_scrap_cost_per_unit_usd) * current_units_produced * 100 | current_units_produced — reading of manufacturing.production.units_producedbaseline_units_produced — baseline of manufacturing.production.units_producedcurrent_scrap_cost_per_unit_usdbaseline_scrap_cost_per_unit_usdcurrent_scrap_cost_per_month_usd — reading of manufacturing.quality.scrap_cost_per_monthbaseline_scrap_cost_per_month_usd — baseline of manufacturing.quality.scrap_cost_per_month |
| Unplanned Downtime Cost AvoidanceEstimates the production value recovered by preventing unplanned equipment failures. Each avoided downtime hour represents lost throughput, production crew idled, and expedite costs to catch up. Downtime is compared per scheduled production hour, so an extra shift is not read as worse reliability nor a short month as a saving: the fall in downtime per scheduled hour × this month's scheduled hours × months × hourly production loss value. Repairing the failed equipment is maintenance spend, which condition-based maintenance cost reduction on the same template counts, so it is left out of the hourly cost. | Cash: cost avoidance | (baseline_downtime_per_scheduled_hour - current_downtime_per_scheduled_hour) * current_scheduled_hours_per_month * months_in_period * hourly_production_loss_cents | months_in_periodhourly_production_loss_cents — assumption, with no defaultcurrent_downtime_hours_per_month — reading of manufacturing.equipment.unplanned_downtime_hours_per_monthbaseline_downtime_hours_per_month — baseline of manufacturing.equipment.unplanned_downtime_hours_per_monthcurrent_scheduled_hours_per_month — reading of manufacturing.equipment.scheduled_hours_per_monthbaseline_scheduled_hours_per_month — baseline of manufacturing.equipment.scheduled_hours_per_monthcurrent_downtime_per_scheduled_hourbaseline_downtime_per_scheduled_hour |
Media entertainment
| Formula | Counts as | Arithmetic | Inputs |
|---|---|---|---|
| Churn Reduction RevenueEstimates subscription revenue protected by reducing monthly churn through better recommendations and engagement. More relevant content keeps subscribers from canceling. Subscribers × churn reduction × months × ARPU. Only the share of that change you credit to this initiative counts: the content slate, price changes, seasonality and competing services move the churn rate too. Booked at your contribution margin: the revenue less the costs that grow with it. | Cash: revenue gain | subscriber_count * (baseline_monthly_churn_rate_pct - current_monthly_churn_rate_pct) / 100 * months_in_period * avg_revenue_per_subscriber_per_month_cents * attributable_share_decimal * contribution_margin_decimal | months_in_periodsubscriber_count — reading of billing.subscriptions.active_countattributable_share_decimal — your share of the change, a judgment with no defaultcontribution_margin_decimal — your contribution margin, with no defaultbilling_subscriptions_mrr_usd — reading of billing.subscriptions.mrrcurrent_monthly_churn_rate_pct — reading of billing.subscriptions.churn_ratebaseline_monthly_churn_rate_pct — baseline of billing.subscriptions.churn_rateavg_revenue_per_subscriber_per_month_cents |
| Engagement-Driven Advertising Revenue UpliftEstimates incremental advertising revenue from increased watch time through better recommendations. More engaged users watch more ad-supported content and command higher CPMs. Only the share of that change you credit to this initiative counts: new releases, seasonality, live sports and competing services move watch time per user too. Booked at your contribution margin: the revenue less the costs that grow with it. | Cash: revenue gain | avg_active_users * (current_watch_time_hours_per_user - baseline_watch_time_hours_per_user) * ad_revenue_per_hour_watched_cents * attributable_share_decimal * contribution_margin_decimal | avg_active_users — reading of media.audience.avg_active_usersattributable_share_decimal — your share of the change, a judgment with no defaultcontribution_margin_decimal — your contribution margin, with no defaultad_revenue_per_hour_watched_cents — assumption, with no defaultcurrent_watch_time_hours_per_user — reading of media.audience.monthly_watch_hours_per_userbaseline_watch_time_hours_per_user — baseline of media.audience.monthly_watch_hours_per_user |
| Localization Cost ReductionEstimates savings from AI-assisted dubbing, subtitling, and localization quality review, reducing vendor cost per episode per language while unlocking additional markets. | Cash: cost savings | episodes_localised_per_period * (baseline_localisation_cost_per_episode_usd - current_localisation_cost_per_episode_usd) * 100 | episodes_localised_per_period — reading of media.localization.episodes_localizedcurrent_localisation_cost_per_episode_usd — reading of media.localization.cost_per_episodebaseline_localisation_cost_per_episode_usd — baseline of media.localization.cost_per_episode |
| Post-Production Cost SavingsEstimates post-production cost saved by compressing timelines through AI-assisted editing, VFX clean-up, and transcription. Episodes × days saved per episode × daily crew and facility cost. | Cash: cost savings | episodes_per_period * (baseline_post_days_per_episode - current_post_days_per_episode) * avg_daily_post_cost_cents | episodes_per_period — reading of media.post_production.episode_countavg_daily_post_cost_cents — assumption, with no defaultcurrent_post_days_per_episode — reading of media.post_production.days_per_episodebaseline_post_days_per_episode — baseline of media.post_production.days_per_episode |
| Rights Administration Freed CapacityValues the rights, legal, and business-affairs team time freed by automating metadata enrichment, rights ingestion, and catalog reconciliation. The hours are priced at what they cost. | Freed capacity | (baseline_rights_admin_hours_per_week - current_rights_admin_hours_per_week) * weeks_in_period * labor_rate_cents_per_hour | weeks_in_periodlabor_rate_cents_per_hour — assumptioncurrent_rights_admin_hours_per_week — reading of media.rights.admin_hours_per_weekbaseline_rights_admin_hours_per_week — baseline of media.rights.admin_hours_per_week |
| Royalty Dispute Freed CapacityValues the legal and finance time freed by reducing royalty statement disputes through AI-verified accurate rights data and automated calculations. Fewer disputes mean less legal and finance time spent on corrections. | Freed capacity | royalty_statements_per_period * (baseline_dispute_rate_pct - current_dispute_rate_pct) / 100 * avg_dispute_resolution_cost_cents | current_dispute_rate_pct — reading of media.royalties.dispute_ratebaseline_dispute_rate_pct — baseline of media.royalties.dispute_rateroyalty_statements_per_period — reading of media.royalties.statement_countavg_dispute_resolution_cost_cents — assumption, with no default |
Professional services
| Formula | Counts as | Arithmetic | Inputs |
|---|---|---|---|
| Bench Time Freed CapacityValues the practitioner time put back to use by reducing unassigned bench time. Practitioners on bench are paid in full with no revenue offset, and payroll is the same once they are assigned, so each percentage point of bench reduction turns idle paid time into deployable capacity. Only the share of that change you credit to this initiative counts: client demand, the pipeline and hiring move bench time too. A bench hour filled with billed work is still paid for; billable utilization revenue uplift on the same template counts that hour as revenue, so an initiative gets one of the two. | Freed capacity | practitioner_count * available_hours_per_practitioner_per_period * (baseline_bench_time_pct - current_bench_time_pct) / 100 * fully_loaded_cost_cents_per_hour * attributable_share_decimal | practitioner_count — reading of professional_services.practitioners.billable_countcurrent_bench_time_pct — reading of professional_services.utilization.bench_sharebaseline_bench_time_pct — baseline of professional_services.utilization.bench_shareattributable_share_decimal — your share of the change, a judgment with no defaultfully_loaded_cost_cents_per_hour — assumptionavailable_hours_per_practitioner_per_period — reading of professional_services.practitioners.available_hours |
| Billable Utilization Revenue UpliftEstimates incremental revenue from improving billable utilization rates across the practitioner base. Each percentage point of utilization improvement converts previously undeployed capacity into billed hours. Total practitioners × available hours × utilization improvement × blended bill rate. Only the share of that change you credit to this initiative counts: client demand, the pipeline, staffing levels and holidays move billable utilization too. Booked at your contribution margin: the revenue less the costs that grow with it. Hours moved off the bench into billed work are what bench time freed capacity on the same template values as freed capacity, so an initiative gets one of the two. | Cash: revenue gain | practitioner_count * available_hours_per_practitioner_per_period * (current_billable_utilization_rate_pct - baseline_billable_utilization_rate_pct) / 100 * avg_bill_rate_cents_per_hour * attributable_share_decimal * contribution_margin_decimal | practitioner_count — reading of professional_services.practitioners.billable_countattributable_share_decimal — your share of the change, a judgment with no defaultcontribution_margin_decimal — your contribution margin, with no defaultavg_bill_rate_cents_per_hour — assumptioncurrent_billable_utilization_rate_pct — reading of professional_services.utilization.billable_ratebaseline_billable_utilization_rate_pct — baseline of professional_services.utilization.billable_rateavailable_hours_per_practitioner_per_period — reading of professional_services.practitioners.available_hours |
| Budget Overrun Cost AvoidanceEstimates the margin value recovered by catching at-risk projects earlier and avoiding budget overruns. Each percentage point reduction in overrun rate represents real margin that would otherwise be absorbed as write-offs or discounts. An overrun is incurred as the work is done, so the base is the period's revenue, not the contract value of every active project, which would count a long engagement again each month it ran: revenue × fall in the overrun rate × the average overrun depth measured before the AI. Overruns made smaller are not counted, so the figure errs low. Only the share of that change you credit to this initiative counts: scope changes, clients, staffing and how projects were priced move the budget overrun rate too. | Cash: cost avoidance | financials_revenue_usd * (baseline_overrun_rate_pct - current_overrun_rate_pct) / 100 * avg_overrun_depth_decimal * 100 * attributable_share_decimal | financials_revenue_usd — reading of finance.pnl.revenuecurrent_overrun_rate_pct — reading of professional_services.projects.budget_overrun_rateavg_overrun_depth_decimalbaseline_overrun_rate_pct — baseline of professional_services.projects.budget_overrun_rateattributable_share_decimal — your share of the change, a judgment with no defaultbaseline_overrun_depth_pct — baseline of professional_services.projects.avg_overrun_depth |
| Early Intervention — PM Escalation Freed CapacityEstimates the project management time saved when AI flags at-risk engagements weeks earlier, allowing lightweight interventions instead of costly late-stage crisis management. Earlier detection means fewer all-hands escalation events and less senior time spent on recovery. | Freed capacity | at_risk_projects_per_period * (baseline_pm_recovery_hours - current_pm_recovery_hours) * labor_rate_cents_per_hour | current_pm_recovery_hours — reading of professional_services.projects.recovery_hours_per_projectlabor_rate_cents_per_hour — assumptionbaseline_pm_recovery_hours — baseline of professional_services.projects.recovery_hours_per_projectat_risk_projects_per_period — reading of professional_services.projects.at_risk_identified_count |
| Knowledge Search Freed CapacityValues the practitioner time freed by replacing manual search across shared drives, email, and intranets with AI-powered knowledge retrieval. Hours saved per task × tasks per period × blended practitioner rate. | Freed capacity | practitioners_using_system * knowledge_search_tasks_per_practitioner_per_period * (baseline_search_minutes - current_search_minutes) / 60 * labor_rate_cents_per_hour | current_search_minutes — reading of professional_services.knowledge.search_minutesbaseline_search_minutes — baseline of professional_services.knowledge.search_minuteslabor_rate_cents_per_hour — assumptionpractitioners_using_system — reading of professional_services.knowledge.active_usersknowledge_search_tasks_per_practitioner_per_period — reading of professional_services.knowledge.searches_per_practitioner |
| Proposal Assembly Freed CapacityEstimates the practitioner time saved on proposal and SOW development by surfacing relevant prior proposals, case studies, and rate cards instantly. Fewer hours per proposal × proposals per period × blended staff rate. | Freed capacity | proposals_per_period * (baseline_proposal_prep_hours - current_proposal_prep_hours) * labor_rate_cents_per_hour | proposals_per_period — reading of professional_services.proposals.submitted_countlabor_rate_cents_per_hour — assumptioncurrent_proposal_prep_hours — reading of professional_services.proposals.prep_hoursbaseline_proposal_prep_hours — baseline of professional_services.proposals.prep_hours |
| Realization Rate Improvement RevenueEstimates revenue from improving the realization rate — the fraction of worked hours actually billed and collected. Better time capture and tighter write-off controls convert more worked hours into invoiced and collected revenue. Total worked hours × realization improvement × blended bill rate. Only the share of that change you credit to this initiative counts: client pushback, pricing, write-off policy and matter mix move the realization rate too. The work was delivered and paid for either way, so the price recovered is all margin, and it books in full. | Cash: revenue gain | total_practitioner_hours_worked * (current_realization_rate_pct - baseline_realization_rate_pct) / 100 * avg_bill_rate_cents_per_hour * attributable_share_decimal | attributable_share_decimal — your share of the change, a judgment with no defaultavg_bill_rate_cents_per_hour — assumptioncurrent_realization_rate_pct — reading of professional_services.billing.realization_ratebaseline_realization_rate_pct — baseline of professional_services.billing.realization_ratetotal_practitioner_hours_worked — reading of professional_services.practitioners.billable_hours_worked |
| Recovered Billable Hours RevenueEstimates revenue from billable hours previously lost to under-capture. AI time suggestions recover hours that practitioners forgot to log or intentionally wrote off due to friction in the timekeeping system. Practitioners × weekly hours recovered × weeks × bill rate. The work was delivered and paid for either way, so the price recovered is all margin, and it books in full. | Cash: revenue gain | practitioner_count * recovered_billable_hours_per_practitioner_per_week * weeks_in_period * avg_bill_rate_cents_per_hour | weeks_in_periodpractitioner_count — reading of professional_services.practitioners.billable_countavg_bill_rate_cents_per_hour — assumptionrecovered_billable_hours_per_practitioner_per_week — reading of professional_services.billing.unbilled_hours_per_practitioner_weekly |
| Report Assembly Freed CapacityValues the project manager and analyst time freed by automating the data gathering, formatting, and delivery of client status reports. Hours saved per report × reports per period × blended PM rate. PM capacity expansion on the same template values these same hours at the billing rate of the client work they are redeployed to, so an initiative gets one of the two. | Freed capacity | reports_per_period * (baseline_hours_per_report - current_hours_per_report) * labor_rate_cents_per_hour | reports_per_period — reading of professional_services.reporting.client_reports_deliveredcurrent_hours_per_report — reading of professional_services.reporting.hours_per_client_reportbaseline_hours_per_report — baseline of professional_services.reporting.hours_per_client_reportlabor_rate_cents_per_hour — assumption |
Real estate property
| Formula | Counts as | Arithmetic | Inputs |
|---|---|---|---|
| Capital Allocation Improvement ValueEstimates the return value of making better capital allocation decisions with real-time portfolio intelligence. Portfolio-wide scenario modeling enables earlier identification of underperforming assets and more optimal CapEx sequencing — improving overall portfolio yield. AUM × the change in annual portfolio yield from before the initiative to now × the period's share of the year. Only the share of that change you credit to this initiative counts: rents, interest rates, cap rates and the property market move a portfolio's yield too. | Cash: cost savings | total_aum_usd * (current_annual_yield_pct - baseline_annual_yield_pct) / 100 * months_in_period / 12 * 100 * attributable_share_decimal | total_aum_usd — reading of real_estate.portfolio.aummonths_in_periodcurrent_annual_yield_pct — reading of real_estate.portfolio.annual_yieldbaseline_annual_yield_pct — baseline of real_estate.portfolio.annual_yieldattributable_share_decimal — your share of the change, a judgment with no default |
| Emergency Repair Cost AvoidanceEstimates the repair cost avoided by catching equipment failures early. Emergency repairs from deferred or undetected building system failures cost 2–5× more than planned interventions — and create tenant satisfaction impacts that drive turnover. Emergency repairs are compared per unit in the portfolio, so an acquisition is not read as more failures: the fall in emergency repairs per unit × the units in the portfolio now × months × cost per repair. | Cash: cost avoidance | (baseline_emergency_repairs_per_unit - current_emergency_repairs_per_unit) * current_units * months_in_period * avg_emergency_repair_cost_cents | current_units — reading of real_estate.portfolio.unit_countbaseline_units — baseline of real_estate.portfolio.unit_countmonths_in_periodavg_emergency_repair_cost_cents — assumption, with no defaultcurrent_emergency_repairs_per_unitbaseline_emergency_repairs_per_unitcurrent_emergency_incidents_per_month — reading of real_estate.maintenance.emergency_repairs_per_monthbaseline_emergency_incidents_per_month — baseline of real_estate.maintenance.emergency_repairs_per_month |
| Financial Reporting Freed CapacityValues the accounting and asset management staff time freed by replacing manual spreadsheet assembly with automated property-level financial reporting. Hours saved per property per month × property count × months × blended accounting rate. | Freed capacity | property_count * (baseline_reporting_hours_per_property - current_reporting_hours_per_property) * months_in_period * labor_rate_cents_per_hour | property_count — reading of real_estate.portfolio.property_countmonths_in_periodlabor_rate_cents_per_hour — assumptioncurrent_reporting_hours_per_property — reading of real_estate.reporting.hours_per_property_monthlybaseline_reporting_hours_per_property — baseline of real_estate.reporting.hours_per_property_monthly |
| Investor Reporting Cycle Compression Freed CapacityValues the time freed by delivering investor reports faster. Faster reporting shortens the investor decision cycle for capital calls and distributions, and reduces management time on ad-hoc data requests from investors who are waiting on delayed reports. The reporting cycles in the period (a year's cycles spread evenly over its months) × lag days saved × cost per lag day. | Freed capacity | investor_report_cycles_per_year * months_in_period / 12 * (baseline_delivery_lag_days - current_delivery_lag_days) * daily_investor_inquiry_cost_cents | months_in_periodcurrent_delivery_lag_days — reading of operations.reporting.delivery_lag_daysbaseline_delivery_lag_days — baseline of operations.reporting.delivery_lag_daysinvestor_report_cycles_per_year — assumptiondaily_investor_inquiry_cost_cents — assumption, with no default |
| Leasing Staff Freed CapacityValues the leasing staff time freed when AI handles initial lead response, qualification screening, and tour scheduling. Fewer manual touchpoints per lease allows the same team to manage higher lead volume or handle more units. The hours are priced at what they cost. | Freed capacity | units_leased_per_period * (baseline_staff_hours_per_lease - current_staff_hours_per_lease) * labor_rate_cents_per_hour | units_leased_per_period — reading of real_estate.leasing.units_leasedlabor_rate_cents_per_hour — assumptioncurrent_staff_hours_per_lease — reading of real_estate.leasing.staff_hours_per_leasebaseline_staff_hours_per_lease — baseline of real_estate.leasing.staff_hours_per_lease |
| Maintenance Request Self-Serve Deflection Freed CapacityEstimates staff time saved when tenants self-serve common maintenance requests and status checks through the tenant app rather than calling the management office. Fewer inbound calls and emails means less property management staff time per unit. | Freed capacity | units_in_portfolio * (baseline_staff_contacts_per_unit_per_month - current_staff_contacts_per_unit_per_month) * months_in_period * avg_contact_handling_minutes / 60 * labor_rate_cents_per_hour | months_in_periodunits_in_portfolio — reading of real_estate.portfolio.unit_countlabor_rate_cents_per_hour — assumptionavg_contact_handling_minutes — reading of real_estate.tenants.minutes_per_contactcurrent_staff_contacts_per_unit_per_month — reading of real_estate.tenants.contacts_per_unit_monthlybaseline_staff_contacts_per_unit_per_month — baseline of real_estate.tenants.contacts_per_unit_monthly |
| Portfolio Reporting Freed CapacityValues the asset management and finance team time freed by replacing manual cross-property data aggregation with automated portfolio dashboards. Hours saved per property per month × portfolio size × months × blended analyst rate. | Freed capacity | properties_in_portfolio * (baseline_reporting_hours_per_property_per_month - current_reporting_hours_per_property_per_month) * months_in_period * labor_rate_cents_per_hour | months_in_periodproperties_in_portfolio — reading of real_estate.portfolio.property_countlabor_rate_cents_per_hour — assumptioncurrent_reporting_hours_per_property_per_month — reading of real_estate.reporting.hours_per_property_monthlybaseline_reporting_hours_per_property_per_month — baseline of real_estate.reporting.hours_per_property_monthly |
| Tenant Turnover Cost AvoidanceEstimates the cost avoided by improving tenant renewal rates. Each prevented turnover avoids vacancy loss, re-leasing commissions, unit make-ready costs, and the time-to-lease gap. Improved renewal rate × units up for renewal × total turnover cost per unit. Only the share of that change you credit to this initiative counts: market rents, rent increases, tenants relocating and building condition move the renewal rate too. | Cash: cost avoidance | units_up_for_renewal * (current_renewal_rate_pct - baseline_renewal_rate_pct) / 100 * avg_turnover_cost_cents * attributable_share_decimal | units_up_for_renewal — reading of real_estate.leasing.units_up_for_renewalavg_turnover_cost_cents — assumption, with no defaultcurrent_renewal_rate_pct — reading of real_estate.leasing.annual_renewal_ratebaseline_renewal_rate_pct — baseline of real_estate.leasing.annual_renewal_rateattributable_share_decimal — your share of the change, a judgment with no default |
| Vacancy Day Reduction RevenueEstimates incremental revenue from compressing time-to-lease. Every day a unit sits vacant is lost rent. Faster lead response and self-tour scheduling convert more leads before they lease elsewhere — directly reducing average vacancy days per unit. Only the share of that change you credit to this initiative counts: the rental market, seasonality, concessions and unit condition move days to lease too. Booked at your contribution margin: the revenue less the costs that grow with it. | Cash: revenue gain | units_leased_per_period * (baseline_time_to_lease_days - current_time_to_lease_days) * avg_daily_rent_cents * attributable_share_decimal * contribution_margin_decimal | avg_daily_rent_cents — assumption, with no defaultunits_leased_per_period — reading of real_estate.leasing.units_leasedattributable_share_decimal — your share of the change, a judgment with no defaultcurrent_time_to_lease_days — reading of real_estate.leasing.days_to_leasebaseline_time_to_lease_days — baseline of real_estate.leasing.days_to_leasecontribution_margin_decimal — your contribution margin, with no default |
| Work Order Administration Freed CapacityValues the property management staff time freed by automating work order creation, vendor dispatch, and status tracking. Fewer manual coordination touchpoints per work order × volume × blended property management rate. | Freed capacity | work_orders_per_period * (baseline_admin_minutes_per_work_order - current_admin_minutes_per_work_order) / 60 * labor_rate_cents_per_hour | work_orders_per_period — reading of real_estate.work_orders.created_countlabor_rate_cents_per_hour — assumptioncurrent_admin_minutes_per_work_order — reading of real_estate.work_orders.admin_minutes_per_orderbaseline_admin_minutes_per_work_order — baseline of real_estate.work_orders.admin_minutes_per_order |
Retail ecommerce
| Formula | Counts as | Arithmetic | Inputs |
|---|---|---|---|
| Average Order Value Uplift RevenueEstimates incremental revenue from increasing average order value through AI-driven product recommendations (cross-sell, upsell, complementary items). Orders in period × AOV increase. Only the share of that change you credit to this initiative counts: inflation, pricing, promotions and product mix move average order value too. Booked at your contribution margin: the revenue less the costs that grow with it. | Cash: revenue gain | orders_per_period * (current_avg_order_value_usd - baseline_avg_order_value_usd) * 100 * attributable_share_decimal * contribution_margin_decimal | orders_per_period — reading of retail.orders.countattributable_share_decimal — your share of the change, a judgment with no defaultcontribution_margin_decimal — your contribution margin, with no defaultcurrent_avg_order_value_usd — reading of retail.orders.avg_valuebaseline_avg_order_value_usd — baseline of retail.orders.avg_value |
| Conversion Rate Uplift RevenueEstimates incremental revenue from improving on-site conversion through personalized experiences. More relevant product presentation means more sessions convert to purchases. Sessions × conversion rate improvement × average order value. Only the share of that change you credit to this initiative counts: traffic mix, promotions, seasonality and pricing move the conversion rate too. Booked at your contribution margin: the revenue less the costs that grow with it. | Cash: revenue gain | sessions_per_period * (current_conversion_rate_pct - baseline_conversion_rate_pct) / 100 * avg_order_value_usd * 100 * attributable_share_decimal * contribution_margin_decimal | avg_order_value_usd — reading of retail.orders.avg_valuesessions_per_period — reading of retail.web.session_countattributable_share_decimal — your share of the change, a judgment with no defaultcontribution_margin_decimal — your contribution margin, with no defaultcurrent_conversion_rate_pct — reading of retail.web.conversion_ratebaseline_conversion_rate_pct — baseline of retail.web.conversion_rate |
| Customer Retention Revenue ValueEstimates the revenue impact of improving customer retention. Each percentage point of retention improvement keeps more customers buying; the value is the period's share of a retained customer's annual revenue, one month of it for a month. Only the share of that change you credit to this initiative counts: prices, competitors, loyalty programs and the economy move the retention rate too. Booked at your contribution margin: the revenue less the costs that grow with it. Repeat purchase revenue uplift on the same template counts the second purchases inside this, so an initiative gets one of the two. | Cash: revenue gain | customer_base * (current_retention_rate_pct - baseline_retention_rate_pct) / 100 * avg_annual_revenue_per_customer_cents * months_in_period / 12 * attributable_share_decimal * contribution_margin_decimal | customer_base — reading of retail.customers.active_countmonths_in_periodattributable_share_decimal — your share of the change, a judgment with no defaultcurrent_retention_rate_pct — reading of retail.customers.retention_ratebaseline_retention_rate_pct — baseline of retail.customers.retention_ratecontribution_margin_decimal — your contribution margin, with no defaultavg_annual_revenue_per_customer_cents — assumption, with no default |
| Gross Margin Improvement ValueEstimates the revenue value of gross margin percentage improvement from AI-optimized pricing and markdown timing. Demand-responsive pricing captures more value at full price and limits margin sacrifice on clearance. Only the share of that change you credit to this initiative counts: cost of goods, freight, product mix and promotions move gross margin too. | Cash: cost savings | total_revenue_cents * (current_gross_margin_pct - baseline_gross_margin_pct) / 100 * attributable_share_decimal | total_revenue_centsfinancials_revenue_usd — reading of finance.pnl.revenuecurrent_gross_margin_pct — reading of retail.pricing.gross_marginbaseline_gross_margin_pct — baseline of retail.pricing.gross_marginattributable_share_decimal — your share of the change, a judgment with no default |
| Inventory Accuracy & Shrink ReductionEstimates the shrink loss avoided by keeping inventory records accurate. Accurate records catch phantom inventory, receiving errors and mis-shipments before they become write-offs. Shrink is measured from your stock counts as a share of sales, so its fall is applied to the period's sales: revenue × (shrink rate before − shrink rate now). Only the share of that change you credit to this initiative counts: theft, staffing, store mix and loss-prevention programs move shrink too. | Cash: cost avoidance | financials_revenue_usd * (baseline_shrink_rate_pct - current_shrink_rate_pct) / 100 * 100 * attributable_share_decimal | financials_revenue_usd — reading of finance.pnl.revenuecurrent_shrink_rate_pct — reading of retail.inventory.shrink_ratebaseline_shrink_rate_pct — baseline of retail.inventory.shrink_rateattributable_share_decimal — your share of the change, a judgment with no default |
| Markdown & Overstock Cost ReductionEstimates gross margin saved by marking down less of what sells. Better demand forecasts mean less overbuying, fewer full-clearance markdowns, and a higher share of sales made at full price. The markdown rate is a share of what sold, so it is applied to the period's sales, not to the stock on hand: revenue × fall in the markdown rate × the average markdown depth measured before the AI. Revenue is already net of markdowns, and markdowns made shallower are not counted, so the figure errs low. Only the share of that change you credit to this initiative counts: weather, trends, competitors' promotions and buying decisions move the markdown rate too. | Cash: cost savings | financials_revenue_usd * (baseline_markdown_rate_pct - current_markdown_rate_pct) / 100 * avg_markdown_depth_decimal * 100 * attributable_share_decimal | financials_revenue_usd — reading of finance.pnl.revenuecurrent_markdown_rate_pct — reading of retail.pricing.markdown_rateattributable_share_decimal — your share of the change, a judgment with no defaultavg_markdown_depth_decimalbaseline_markdown_rate_pct — baseline of retail.pricing.markdown_ratebaseline_markdown_depth_pct — baseline of retail.pricing.avg_markdown_depth |
| Omnichannel Stockout Revenue RecoveryEstimates revenue recovered by eliminating stockouts across all channels. Unified inventory visibility allows ship-from-store and accurate ATP promises — converting what was previously a lost sale into a fulfilled order. Only the share of that change you credit to this initiative counts: supplier delays, demand spikes and buying decisions move the stockout rate too. Booked at your contribution margin: the revenue less the costs that grow with it. | Cash: revenue gain | total_revenue_cents * (baseline_stockout_rate_pct - current_stockout_rate_pct) / 100 * attributable_share_decimal * contribution_margin_decimal | total_revenue_centsfinancials_revenue_usd — reading of finance.pnl.revenuecurrent_stockout_rate_pct — reading of supply_chain.inventory.stockout_rateattributable_share_decimal — your share of the change, a judgment with no defaultbaseline_stockout_rate_pct — baseline of supply_chain.inventory.stockout_ratecontribution_margin_decimal — your contribution margin, with no default |
| Repeat Purchase Revenue UpliftEstimates incremental revenue from improving the repeat purchase rate through CLV-optimized lifecycle marketing. More customers returning for a second purchase directly increases revenue without additional acquisition spend. The repeat rate is over twelve months, so the added second purchases are a year's, spread evenly over its months. Only the share of that change you credit to this initiative counts: promotions, seasonality, loyalty programs and the economy move the repeat purchase rate too. Booked at your contribution margin: the revenue less the costs that grow with it. A customer who makes that second purchase is a retained customer, whose revenue customer retention revenue value on the same template already counts, so an initiative gets one of the two. | Cash: revenue gain | customer_base * (current_repeat_purchase_rate_pct - baseline_repeat_purchase_rate_pct) / 100 * months_in_period / 12 * avg_second_purchase_value_cents * attributable_share_decimal * contribution_margin_decimal | customer_base — reading of retail.customers.active_countmonths_in_periodattributable_share_decimal — your share of the change, a judgment with no defaultcontribution_margin_decimal — your contribution margin, with no defaultavg_second_purchase_value_cents — assumption, with no defaultcurrent_repeat_purchase_rate_pct — reading of retail.customers.repeat_purchase_ratebaseline_repeat_purchase_rate_pct — baseline of retail.customers.repeat_purchase_rate |
| Sell-Through Improvement ValueEstimates the revenue recovered by selling more seasonal stock at its target price instead of clearing it at a markdown. Of the markdown-eligible stock that sold or cleared in the period, the share that sold at target rose; each of those sales kept the markdown it would otherwise have given up. Retail value sold or cleared × sell-through gain × the average markdown depth measured before the AI. Only the share of that change you credit to this initiative counts: weather, trends, promotions and buying decisions move the sell-through rate too. Gross margin improvement on the same template counts this too, so an initiative gets one of the two. | Cash: cost savings | markdown_eligible_sold_value_usd * (current_sell_through_rate_pct - baseline_sell_through_rate_pct) / 100 * avg_markdown_depth_decimal * 100 * attributable_share_decimal | attributable_share_decimal — your share of the change, a judgment with no defaultavg_markdown_depth_decimalbaseline_markdown_depth_pct — baseline of retail.pricing.avg_markdown_depthcurrent_sell_through_rate_pct — reading of retail.inventory.sell_through_ratebaseline_sell_through_rate_pct — baseline of retail.inventory.sell_through_ratemarkdown_eligible_sold_value_usd — reading of retail.inventory.markdown_eligible_sold_value |
| Stockout Lost Revenue RecoveryEstimates revenue recovered by reducing in-season stockouts. Each percentage point of stockout reduction means fewer lost sales from customers who found the product unavailable. Only the share of that change you credit to this initiative counts: supplier delays, demand spikes and buying decisions move the stockout rate too. Booked at your contribution margin: the revenue less the costs that grow with it. | Cash: revenue gain | total_revenue_cents * (baseline_stockout_rate_pct - current_stockout_rate_pct) / 100 * attributable_share_decimal * contribution_margin_decimal | total_revenue_centsfinancials_revenue_usd — reading of finance.pnl.revenuecurrent_stockout_rate_pct — reading of supply_chain.inventory.stockout_rateattributable_share_decimal — your share of the change, a judgment with no defaultbaseline_stockout_rate_pct — baseline of supply_chain.inventory.stockout_ratecontribution_margin_decimal — your contribution margin, with no default |
Telecom
| Formula | Counts as | Arithmetic | Inputs |
|---|---|---|---|
| Care Call Deflection Freed CapacityValues the agent time freed by deflecting routine billing, account, and technical inquiries into self-service. Calls are compared per care contact across every channel, so an outage or a billing run that floods care is not read as a loss: the fall in calls per contact × this month's contacts × months × average handle time × agent rate. Care cost per subscriber on the same template already falls by the agent time deflection saves, so an initiative gets one of the two. | Freed capacity | (baseline_calls_per_contact - current_calls_per_contact) * current_contacts_per_month * months_in_period * avg_handle_time_minutes * labor_rate_cents_per_minute | months_in_periodavg_handle_time_minutes — reading of support.calls.avg_handle_timecurrent_calls_per_month — reading of telecom.customer_care.calls_per_monthbaseline_calls_per_month — baseline of telecom.customer_care.calls_per_monthcurrent_calls_per_contactlabor_rate_cents_per_hour — assumptionbaseline_calls_per_contactcurrent_contacts_per_month — reading of telecom.customer_care.contacts_per_monthbaseline_contacts_per_month — baseline of telecom.customer_care.contacts_per_monthlabor_rate_cents_per_minute |
| Care Cost Per Subscriber ReductionEstimates the total care cost saved by reducing per-subscriber care cost through deflection bots and AI agent assist. Subscriber base × cost reduction per sub × measurement periods. Only the share of that change you credit to this initiative counts: outages, billing changes, device launches and subscriber mix move care cost per subscriber too. | Cash: cost savings | subscriber_count * (baseline_care_cost_per_sub_usd - current_care_cost_per_sub_usd) * months_in_period * 100 * attributable_share_decimal | months_in_periodsubscriber_count — reading of billing.subscriptions.active_countattributable_share_decimal — your share of the change, a judgment with no defaultcurrent_care_cost_per_sub_usd — reading of telecom.customer_care.cost_per_subscriberbaseline_care_cost_per_sub_usd — baseline of telecom.customer_care.cost_per_subscriber |
| Churn Prevention RevenueEstimates ARPU revenue protected by reducing monthly churn through predictive save plays. Subscribers retained × months × ARPU. Only the share of that change you credit to this initiative counts: competitors' offers, pricing, device cycles and network quality move the churn rate too. Booked at your contribution margin: the revenue less the costs that grow with it. | Cash: revenue gain | subscriber_count * (baseline_monthly_churn_rate_pct - current_monthly_churn_rate_pct) / 100 * months_in_period * avg_arpu_cents_per_month * attributable_share_decimal * contribution_margin_decimal | months_in_periodsubscriber_count — reading of billing.subscriptions.active_countavg_arpu_cents_per_monthattributable_share_decimal — your share of the change, a judgment with no defaultcontribution_margin_decimal — your contribution margin, with no defaultbilling_subscriptions_mrr_usd — reading of billing.subscriptions.mrrcurrent_monthly_churn_rate_pct — reading of billing.subscriptions.churn_ratebaseline_monthly_churn_rate_pct — baseline of billing.subscriptions.churn_rate |
| MTTR Reduction Cost AvoidanceEstimates the operational cost avoided by resolving network incidents faster. Shorter MTTR means fewer NOC engineer hours per incident, lower overtime, and reduced customer SLA credit exposure. Incidents × MTTR reduction × cost per hour of incident. | Cash: cost avoidance | incidents_per_period * (baseline_mttr_minutes - current_mttr_minutes) / 60 * cost_per_incident_hour_cents | current_mttr_minutes — reading of telecom.network.mttr_minutesincidents_per_period — reading of telecom.network.incident_countbaseline_mttr_minutes — baseline of telecom.network.mttr_minutescost_per_incident_hour_cents — assumption, with no default |
| Save Play Cost EfficiencyEstimates the net value of targeted save offers versus blanket discounting — fewer wasted retention offers on low-risk subscribers reduces discount cost while protecting revenue on high-risk, high-ARPU targets. | Cash: cost savings | (baseline_save_offer_cost_per_retained_subscriber_usd - current_save_offer_cost_per_retained_subscriber_usd) * retained_subscribers_per_period * 100 | retained_subscribers_per_period — reading of telecom.retention.retained_subscriberscurrent_save_offer_cost_per_retained_subscriber_usd — reading of telecom.retention.cost_per_retained_subscriberbaseline_save_offer_cost_per_retained_subscriber_usd — baseline of telecom.retention.cost_per_retained_subscriber |
| Truck Roll Reduction Freed CapacityValues the truck rolls freed when predictive network maintenance and remote diagnostics eliminate unnecessary ones, at what a dispatch costs (mostly technician time, so it counts as freed capacity until a bill shows the spend fell). Truck rolls are compared per subscriber, so a growing base is not read as more dispatches: the fall in truck rolls per subscriber × this month's subscribers × months × cost per dispatch. | Freed capacity | (baseline_truck_rolls_per_subscriber - current_truck_rolls_per_subscriber) * current_subscribers * months_in_period * avg_truck_roll_cost_cents | months_in_periodcurrent_subscribers — reading of billing.subscriptions.active_countbaseline_subscribers — baseline of billing.subscriptions.active_countavg_truck_roll_cost_cents — assumption, with no defaultcurrent_truck_rolls_per_month — reading of telecom.field_service.truck_rolls_per_monthbaseline_truck_rolls_per_month — baseline of telecom.field_service.truck_rolls_per_monthcurrent_truck_rolls_per_subscriberbaseline_truck_rolls_per_subscriber |
Transportation logistics
| Formula | Counts as | Arithmetic | Inputs |
|---|---|---|---|
| Breakdown Cost AvoidanceEstimates cost avoided by preventing unplanned roadside breakdowns. Each avoided event eliminates towing, roadside labor, missed-load penalties, and expedite recovery costs. Breakdowns are compared per vehicle in service, so a larger fleet is not read as more breakdowns: the fall in roadside events per vehicle a year × the vehicles in service now, spread evenly over the year's months, × average all-in breakdown cost. | Cash: cost avoidance | (baseline_roadside_events_per_vehicle_per_year - current_roadside_events_per_vehicle_per_year) * current_vehicles * months_in_period / 12 * avg_breakdown_cost_cents | current_vehicles — reading of logistics.fleet.vehicle_countmonths_in_periodbaseline_vehicles — baseline of logistics.fleet.vehicle_countavg_breakdown_cost_cents — assumption, with no defaultcurrent_roadside_events_per_year — reading of logistics.fleet.roadside_events_per_yearbaseline_roadside_events_per_year — baseline of logistics.fleet.roadside_events_per_yearcurrent_roadside_events_per_vehicle_per_yearbaseline_roadside_events_per_vehicle_per_year |
| Driver Productivity ValueEstimates incremental revenue or cost capacity unlocked by improving stops per driver hour. More stops per hour means the same drivers can handle more volume without adding headcount. Added stops per driver hour × the fleet's driver hours in the period × revenue per stop. Only the share of that change you credit to this initiative counts: route density, order volume, weather and traffic move stops per driver hour too. Booked at your contribution margin: the revenue less the costs that grow with it. | Cash: revenue gain | (current_stops_per_driver_hour - baseline_stops_per_driver_hour) * operating_hours_per_period * revenue_per_stop_cents * attributable_share_decimal * contribution_margin_decimal | revenue_per_stop_cents — assumption, with no defaultattributable_share_decimal — your share of the change, a judgment with no defaultoperating_hours_per_period — reading of logistics.drivers.operating_hourscontribution_margin_decimal — your contribution margin, with no defaultcurrent_stops_per_driver_hour — reading of logistics.drivers.stops_per_hourbaseline_stops_per_driver_hour — baseline of logistics.drivers.stops_per_hour |
| Fuel & Mile Reduction SavingsEstimates cost saved by reducing fleet miles through AI route optimization. Fewer miles directly lower fuel, tire, and per-mile maintenance spend. Miles are compared per stop completed, so a peak-season month with more deliveries is not read as worse routing nor a slow one as a saving: the fall in miles per stop × this period's stops × blended cost per mile. | Cash: cost savings | (baseline_miles_per_stop - current_miles_per_stop) * current_stops * avg_cost_cents_per_mile | current_stops — reading of logistics.fleet.stop_countbaseline_stops — baseline of logistics.fleet.stop_countcurrent_fleet_miles — reading of logistics.fleet.milesbaseline_fleet_miles — baseline of logistics.fleet.milescurrent_miles_per_stopavg_cost_cents_per_mile — assumption, with no defaultbaseline_miles_per_stop |
| Insurance Premium ReductionEstimates the commercial auto and cargo premium saved once a renewal has lowered it. The premium is compared per vehicle in service, so a fleet that grew is not read as a dearer policy nor one that shrank as a saving: the annual premium per vehicle in force before the safety program, less the annual premium per vehicle in force now, × the vehicles in service now × the period's share of the year. Until a renewal changes the premium per vehicle nothing books; after it, the saving books month by month while the lower premium is paid. Only the share of that change you credit to this initiative counts: the insurance market's rates, miles driven, coverage limits and deductibles, and a change of carrier move the premium too. Preventable accident cost avoidance on the same template leaves the premium out of the cost of an accident, so the two do not overlap. | Cash: cost savings | (baseline_premium_per_vehicle_usd - current_premium_per_vehicle_usd) * current_vehicles * months_in_period / 12 * 100 * attributable_share_decimal | current_vehicles — reading of logistics.fleet.vehicle_countmonths_in_periodbaseline_vehicles — baseline of logistics.fleet.vehicle_countattributable_share_decimal — your share of the change, a judgment with no defaultcurrent_annual_premium_usd — reading of logistics.fleet.annual_insurance_premiumbaseline_annual_premium_usd — baseline of logistics.fleet.annual_insurance_premiumcurrent_premium_per_vehicle_usdbaseline_premium_per_vehicle_usd |
| Maintenance Labor Freed CapacityValues the shop and maintenance team time freed when condition-based scheduling replaces reactive and over-scheduled preventive services, reducing unnecessary labor hours. Hours are compared per vehicle in service, so a larger fleet is not read as less efficient maintenance: the fall in monthly technician hours per vehicle × the vehicles in service now × months × technician rate. | Freed capacity | (baseline_maintenance_hours_per_vehicle - current_maintenance_hours_per_vehicle) * current_vehicles * months_in_period * labor_rate_cents_per_hour | current_vehicles — reading of logistics.fleet.vehicle_countmonths_in_periodbaseline_vehicles — baseline of logistics.fleet.vehicle_countlabor_rate_cents_per_hour — assumptioncurrent_maintenance_hours_per_month — reading of logistics.fleet.maintenance_hours_per_monthbaseline_maintenance_hours_per_month — baseline of logistics.fleet.maintenance_hours_per_monthcurrent_maintenance_hours_per_vehiclebaseline_maintenance_hours_per_vehicle |
| On-Time Delivery Revenue ProtectionEstimates revenue protected or penalties avoided by improving on-time delivery rates. Late deliveries cause customer chargebacks, contract penalties, and order cancellations. OTD improvement × revenue at risk. Only the share of that change you credit to this initiative counts: weather, traffic, order volume and staffing move the on-time delivery rate too. Booked at your contribution margin: the revenue less the costs that grow with it. | Cash: revenue gain | (current_on_time_delivery_pct - baseline_on_time_delivery_pct) / 100 * revenue_at_risk_from_late_delivery_usd * 100 * attributable_share_decimal * contribution_margin_decimal | attributable_share_decimal — your share of the change, a judgment with no defaultcontribution_margin_decimal — your contribution margin, with no defaultcurrent_on_time_delivery_pct — reading of logistics.deliveries.on_time_ratebaseline_on_time_delivery_pct — baseline of logistics.deliveries.on_time_raterevenue_at_risk_from_late_delivery_usd — reading of supply_chain.deliveries.revenue_at_risk |
| Preventable Accident Cost AvoidanceEstimates the cost the fleet itself avoids by reducing preventable accident frequency. Each avoided accident eliminates vehicle repair and cargo loss insurance does not cover, deductibles or self-insured retention on injury and damage claims, legal costs, and downtime. What the insurer pays is not the fleet's cost, and the premium it charges is counted by insurance premium reduction on the same template, so neither is included here. Accidents avoided × average cost per accident. Only the share of that change you credit to this initiative counts: weather, traffic, driver mix and other safety programs move the preventable accident rate too. | Cash: cost avoidance | miles_driven_per_period / 1000000 * (baseline_accidents_per_million_miles - current_accidents_per_million_miles) * avg_preventable_accident_cost_cents * attributable_share_decimal | miles_driven_per_period — reading of logistics.fleet.milesattributable_share_decimal — your share of the change, a judgment with no defaultavg_preventable_accident_cost_cents — assumption, with no defaultcurrent_accidents_per_million_miles — reading of logistics.safety.preventable_accidents_per_million_milesbaseline_accidents_per_million_miles — baseline of logistics.safety.preventable_accidents_per_million_miles |
| WISMO Contact Freed CapacityValues the customer service time freed by reducing 'Where Is My Order' contacts through accurate real-time ETAs and proactive delivery status notifications. Contacts are compared per shipment, so peak season's extra orders are not read as a loss nor a slow month as a gain: the fall in WISMO contacts per shipment × this period's shipments × cost per contact. | Freed capacity | (baseline_wismo_contacts_per_shipment - current_wismo_contacts_per_shipment) * current_shipments * avg_cost_per_contact_cents | current_shipments — reading of supply_chain.shipments.countbaseline_shipments — baseline of supply_chain.shipments.countlabor_rate_cents_per_hour — assumptionavg_cost_per_contact_centsbaseline_minutes_per_contact — baseline of logistics.customer_service.minutes_per_wismo_contactcurrent_wismo_contacts_per_month — reading of logistics.customer_service.wismo_contacts_per_monthbaseline_wismo_contacts_per_month — baseline of logistics.customer_service.wismo_contacts_per_monthcurrent_wismo_contacts_per_shipmentbaseline_wismo_contacts_per_shipment |
Value a sync books, not a formula
One entry per initiative per month, from the platform's own sync. Each resolved record the link's deflection rule matches is worth the price in effect on the date it was created, once the recontact window has passed without the customer coming back about it.
| Formula | Counts as | Arithmetic | Inputs |
|---|---|---|---|
| Zendesk · Deflected ticketsThe agent time freed when an AI agent resolves a ticket without a person. | Freed capacity | deflected_tickets * pricing |
deflected_tickets — tickets the help desk resolved (status solved or closed) that the link's deflection rule matches; by default, tagged ai-deflectedpricing.zendesk.cost_per_deflected_ticket — assumptionpricing.deflection.recontact_window_days — assumption |
| Freshdesk · Deflected ticketsThe agent time freed when an AI agent resolves a ticket without a person. | Freed capacity | deflected_tickets * pricing |
deflected_tickets — tickets the help desk resolved (status resolved or closed) that the link's deflection rule matches; by default, tagged ai-deflectedpricing.freshdesk.cost_per_deflected_ticket — assumptionpricing.deflection.recontact_window_days — assumption |
| Intercom · Deflected conversationsThe agent time freed when an AI agent resolves a conversation without a person. | Freed capacity | deflected_conversations * pricing |
deflected_conversations — conversations the help desk resolved (state closed) that the link's deflection rule matches; by default, tagged ai-deflectedpricing.intercom.cost_per_deflected_conversation — assumptionpricing.deflection.recontact_window_days — assumption |
| Salesforce · Deflected conversationsThe agent time freed when an AI agent resolves a conversation without a person. | Freed capacity | deflected_conversations * pricing |
deflected_conversations — conversations (sessions that ended with an AI agent alone or with a person, not abandoned) that the link's deflection rule matches; handled by the AI agent the link names, and nothing until it names onepricing.salesforce.cost_per_deflected_conversation — assumptionpricing.deflection.recontact_window_days — assumption |