Platform ROAS doesn't match Shopify. Reconcile every dollar.
AI agents that learn your unit economics. True incrementality reconciled to the P&L. Forecasts that sharpen across every replenishment cycle.
$0K
$0K
$0K
$330K of attributed revenue is overlap. Parker reconciles this against Shopify monthly. Numbers illustrative; exact gap depends on channel mix.
Revenue up. Margins down. Scaling on broken data.
Platforms inflate their own numbers and dashboards repeat them. These four failure modes are why scaling DTC budgets faster than they should rarely produces sustainable margin.
Scaling kills your margins
Spend doubles, ROAS drops 40 percent. Without marginal-return curves, every scaling decision is a coin flip and every retreat costs another week.
Platform ROAS doesn't match Shopify
Meta says four hundred thousand. Google says two-fifty. Shopify shows five hundred total. Someone is double-counting.
Seasonal patterns caught too late
By the time the Q4 ramp shows up in the dashboard, the competition has already won the auction. Last year's spreadsheet doesn't capture this year's curve.
CAC rising, LTV unknown
Acquisition cost climbs every quarter. Without per-channel LTV, the team can't tell which customers are profitable across cohorts.
Reconcile every dollar. Compound the truth.
Parker debiases attribution against Shopify. Dana unifies the data layer. Felix forecasts revenue against the calendar. Sam tests scaling moves before money commits.
Parker
Attribution
Reconciles platform-reported ROAS against Shopify orders. Surfaces non-incremental spend that platforms claim but Shopify didn't see. Architecture target: identify and reallocate the spend that doesn't make it to the P&L.
Debiased ROAS, reconciled to Shopify.
Dana
Unified Data
Builds the unified data layer across Shopify, Meta, Google, TikTok, GA4, Klaviyo. Reconciles spend, revenue, and conversions every night. Architecture target: one source of truth that the rest of the workforce reads from.
One source of truth, Shopify-aligned.
Felix
Forecasting
Forecasts revenue, AOV, and ROAS by channel. Learns the Q4 ramp, the post-Black-Friday slump, and your specific replenishment cycles. Architecture target: forecasts climb from 78 percent in month one to 91 percent by month nine.
Forecasts that learn your seasonality.
Sam
Scenario Testing
Models budget shifts and scaling moves before money commits. Hard CAC and margin caps as constraints. Architecture target: scaling decisions arrive with confidence intervals, not gut calls.
Scale before you commit.
The other three agents fill out the workforce. See all seven →.
Reported ROAS in. True incremental ROAS out.
Stage 1
Meta-reported ROAS
Stage 2
Platform-debiased
Stage 3
Incremental
Stage 4
True ROAS
Numbers illustrative of the reconciliation steps Parker performs. Exact deltas depend on channel mix, holdout design, and Shopify order data.
Concrete deltas. Architecture targets for DTC.
Four metrics targeted by the 14-day pilot structure. Exact numbers depend on channel mix, AOV, and current attribution stack.
Architecture target: Parker reconciles platform-claimed revenue against Shopify orders every night. Exact gap depends on channel mix and overlap.
Architecture target across the 9-month pilot structure. What compound learning is built to deliver.
Architecture target: Sam runs the scaling scenario in seconds. The output is a confidence interval, not a meeting.
Architecture target: OAuth-based connection to Shopify, Meta, Google, TikTok. First insights inside 48 hours.
Questions DTC teams ask
How does Cresva help DTC brands scale profitably?
The workforce learns your unit economics and reconciles attribution against Shopify. Test budget scenarios before money commits, get debiased ROAS, and forecast revenue against your specific calendar.
Does Cresva integrate with Shopify?
Yes. Connects in five minutes via OAuth. Pulls order data, revenue, AOV, and customer cohorts. Combined with Meta, Google, TikTok for a unified DTC view.
What makes Cresva different from Triple Whale or Northbeam?
Those tools focus on attribution alone. Cresva is a workforce: attribution plus forecasting plus scenario testing plus institutional memory plus reporting. Seven specialized agents share one memory.
Can it find true incremental ROAS?
Parker reconciles platform-claimed revenue against Shopify and against holdouts. Architecture target: surface the share of platform-reported spend that didn't make it to the P&L.
How fast can we start?
Five minutes via OAuth: Shopify, Meta, Google, TikTok. First insights inside 48 hours. Full compounding effect rolls in across the 14-day pilot structure.
DTC not the right fit?
Ready when you are
See the agents on your Shopify data.
Pilot connects Shopify, Meta, Google, TikTok in five minutes. First reconciled view inside 48 hours.
Looking for a deep dive? See Parker debiases →, Felix forecasts → or AI commerce surface →.