United States Revenue Leak Map
Reconcile the margin story before diligence or the next stage of scale.
Revenue Leak Map is a fixed-fee diagnostic that validates supplied costs, reconciles to Shopify totals, and ranks defensible margin leaks for physical-product DTC brands.
Who it is for
US Shopify DTC brands with real COGS and a decision to support.
The documented qualification band is $2M-$30M in annual revenue, with a $3M-$15M sweet spot. These bands describe the current operating profile. The brand must sell physical products through Shopify and be able to provide actual COGS or a usable SKU crosswalk.
Choose your intent
What decision does the margin model need to support?
Raise or sale path
Diagnostic first, then a qualified Margin Fix Sprint.
The diagnostic documents the supplied data, actual COGS handling, Shopify reconciliation variance, CM definitions, ranked supported leaks, and caveats. If the findings point to fixes that are currently deliverable and your team has a named executor, I scope a defined Margin Fix Sprint. The Sprint is not assumed before the diagnostic.
Independent scaling path
Margin Command begins with the same baseline reconciliation.
Margin Command is a standing specialist-consultancy service for qualified operators. Its onboarding includes the diagnostic and reconciliation, followed by scoped fixes, concierge reruns, and a management read. It does not assume your existing contribution-margin model is already trusted.
Illustrative US sample
Inspect the structure before sharing your data.
Reconciliation, CM bridge, ranked leaks, methodology, and caveats.
What the reviewed copy contains
- Shopify-total reconciliation with explicit variance
- Per-SKU CM1 and CM1, CM2, and CM3 by marketing channel when supported
- Ranked discount, below-cost SKU, return, shipping, and payment-fee leaks
- Method notes, data gaps, and caveats
It uses illustrative synthetic data and does not represent a client result. Until the public artifact handoff is approved, I share the reviewed copy during the readiness call.
Reconciliation method
Make the margin bridge explainable from Shopify totals down.
I map supplied SKUs to actual COGS, normalize supported order and refund fields, account for supplied payment fees in CM2, and reconcile the modeled totals to Shopify. Any remaining variance is shown with an explanation or caveat.
Triple Whale, BeProfit, and Lifetimely can be useful data sources. The value here is validating actual COGS, testing the reconciliation, and producing a decision-grade contribution-margin view rather than dismissing the tools you already use.
| CM1 | Net revenue less actual product cost, where the supplied data supports the calculation. |
|---|---|
| CM2 | CM1 less supported variable fulfillment and payment costs. |
| CM3 | CM2 less supplied marketing spend at the supported channel level. Per-SKU CM3 is not included. |
Data requirements
Useful output depends on traceable inputs.
- Shopify order, line-item, discount, refund, and return exports
- Actual COGS by SKU or a usable SKU-to-cost crosswalk
- Supported shipping, fulfillment, and payment-fee data
- Marketing spend by channel for the analysis period
- Settlement data for any separately scoped marketplace coverage
Deliverables
A reconciled management view with a clear decision trail.
- Data and SKU crosswalk notes
- Shopify reconciliation and documented variance
- Supported CM views by SKU and marketing channel
- Ranked leak register with evidence and caveats
- Management read and qualified next-step recommendation
Current limitations
Clear boundaries keep the diagnostic defensible.
This is not a live dashboard or an automated monitoring system. It does not include automated alerts, scenario modeling, LTV or cohort economics, automated CAC or MER, automated landed-cost allocation, subscription or churn analysis, mature peer benchmarks, or per-SKU CM3. Cross-industry delivery is not offered today.
How pricing works
Compare the three ways to engage.
Start with the diagnostic. A Sprint or Margin Command is considered only when the findings support it and your team has a named executor.
Swipe or scroll to compare all three offers.
| What to compare | Revenue Leak Map DiagnosticDiligence readiness | Margin Fix SprintDefined implementation | Margin CommandStanding margin cadence |
|---|---|---|---|
| Commercial model | Contact for pricingFixed fee | Contact for pricingFixed scope | Contact for pricingScoped cadence |
| Entry condition | First step after data readiness | Supported diagnostic findings and a named client-side executor | Supported diagnostic findings and a named client-side executor |
| Best for | Diligence readiness for a raise or sale | Implementing a defined set of supported fixes | Recurring, human-led margin review |
| Target delivery window | Days after data readiness | 3 to 4 weeks after scope confirmation | 4 to 8 weeks core; cadence confirmed separately |
| Reconciled margin view | Included with documented variance and caveats | Starts from the diagnostic baseline | Starts from the diagnostic baseline |
| Ranked leak register | Included with supporting evidence | Used to select the defined fixes | Used to set priorities and cadence |
| Implementation scope | Not included | Defined highest-priority fixes supported by the diagnostic | Scoped, currently deliverable fixes |
| Marketplace coverage | Separate scope; settlement data required | Add-on after review | Scoped after review |
| Ongoing reruns and management reporting | Not included | Not included | Included at the agreed concierge cadence |
Delivery windows are planning targets that begin after the required data passes readiness review. Final scope, timing, cleanup, adjacent-channel coverage, and pricing are confirmed before engagement.
Data and security
Agree the minimum-data boundary before transfer.
The readiness review confirms the required exports, transfer method, access, retention, and deletion approach. Client data is not requested before those controls and the analysis scope are clear.
Revenue Leak Map replaces the former Profit Truth Audit.
Readiness call
Bring the decision, data footprint, and current margin question.
I will confirm fit, identify the minimum useful data set, and recommend the diligence or operator path. If the inputs cannot support a defensible diagnostic, I will say so.