Comparable cohorts
Use the same economic model
Calculate agentic, non-agentic, and unknown source cohorts with identical revenue, discount, refund, and cost rules.
Channel contribution economics
Measure Shopify AI Channel Profitability Beyond GMV
A growing agentic sales cohort can still reduce contribution after discounts, refunds, COGS, and channel-specific costs. Compare every source through the same evidence and coverage model.
Current product boundaryThe Shopify monitor operates in read-only OBSERVE during the private pilot. It does not process payments or hold funds.
THE OPERATING MODEL
Revenue answers how much sold. Contribution asks what remained.
Channel profitability should not start with agentic orders alone. All analyzed Shopify orders count because agentic, non-agentic, and unknown cohorts need a consistent comparison baseline for discounts, refunds, returns, product mix, and cost coverage.
AgentGateFee keeps observed Shopify values separate from merchant-supplied assumptions and estimates. A profitability result without sufficient cost coverage remains explicitly incomplete instead of appearing precise.
WHAT THE RECORD MUST SHOW
A fair channel comparison needs aligned inputs.
Coverage
Expose missing costs
Show which orders and line items have observed COGS or merchant-defined costs so incomplete coverage cannot masquerade as profit.
Exposure
Include post-purchase change
Reconcile refunds and return evidence over time rather than freezing profitability at the original checkout value.
EVIDENCE FLOW
From order value to channel contribution
- 01Define source cohorts and confidence threshold
- 02Normalize gross and net merchandise values
- 03Apply discounts and economic refunds
- 04Join COGS and merchant-defined costs
- 05Calculate coverage and contribution
- 06Compare cohorts without hiding UNKNOWN
CLEAR PRODUCT BOUNDARY
Profitability is an analytical result, not a savings guarantee.
AgentGateFee reports the inputs, assumptions, coverage, and source confidence behind each comparison. It does not claim guaranteed savings, recovered revenue, or causal channel lift from observational data.
COMMON QUESTIONS
Understand the evidence before changing the workflow.
Why count non-agentic orders?
They form the comparison baseline. Without them, a merchant cannot tell whether margin, discount, product mix, or return behavior differs by source.
What if product costs are missing?
Cost coverage is reported explicitly, and the result remains incomplete or estimated rather than being presented as fully observed contribution.
Does higher GMV mean a more profitable AI channel?
Not necessarily. Discounts, refunds, product mix, COGS, and channel-specific costs can materially change contribution.
START WITH EVIDENCE