What marketing roles does Cresva support?

Cresva supports growth teams, marketing executives (CMOs, VPs, directors), agencies, and brand teams with AI-powered intelligence that learns from each team's specific decisions and KPIs.

What types of ecommerce businesses does Cresva work with?

Cresva works with DTC brands, multi-channel retailers, subscription brands, high-SKU catalog businesses, and brands selling on both marketplaces and their own store. The AI learns business-model-specific patterns like seasonal trends, churn curves, SKU profitability, and channel dynamics.

How accurate are Cresva's forecasts?

Cresva's forecasts learn from each brand's specific seasonality, channel mix, and decision history. Architecture target: forecasts sharpen across the pilot's onboarding period and continue to improve with each cycle. Exact accuracy depends on data history and category.

Can agencies use Cresva for multiple clients?

Yes, agencies can manage 10+ clients in one dashboard. Each client gets smarter individually, and the system learns patterns across your entire portfolio for cross-client insights.

Can Cresva show which reported revenue is real?

Parker (Performance Agent) reconciles platform-reported conversions against revenue your store actually recorded, and shows the gap per channel. Separating incremental from organic demand is a different question, and answering it needs a holdout test you run deliberately.

How fast can a DTC brand get started with Cresva?

Connect Shopify, Meta, Google, and TikTok in about 5 minutes. First insights within 48 hours. Forecast accuracy improves across the first quarter as the model accumulates history for your brand.

Your catalogue, open to AI shoppers.

See how

DTC brands

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.

Book a demo Meet the agents →

Reconciliation runs monthly

$0K

Total claimed$0K

Live

$0K

Shopify actual revenue

$830K total claimed by platforms$500K real (Shopify)

$500K

$330K

$330K of attributed revenue is overlap. Parker reconciles this against Shopify monthly. Numbers illustrative; exact gap depends on channel mix.

Where dashboards break for DTC

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.

The four agents

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.

Attribution Analyst

Parker

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.

Analytics Engineer

Dana

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 agents reads from.

One source of truth, Shopify-aligned.

Performance Forecaster

Felix

Forecasts revenue, AOV, and ROAS by channel. Learns the Q4 ramp, the post-Black-Friday slump, and your specific replenishment cycles. Every prediction is scored against actuals once its period closes, and the miss trains the next forecast.

Forecasts that learn your seasonality.

Media Planner

Sam

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 agents fill out the agents. See all of them →.

How Parker reconciles

Reported ROAS in. True incremental ROAS out.

Stage 1

4.2x

Meta-reported ROAS

Stage 2

2.4x

Platform-debiased

platform self-claim removed−43%

Stage 3

2.1x

Incremental

holdout calibration−13%

Stage 4

1.8x

True ROAS

reconciled to Shopify−14%

Numbers illustrative of the reconciliation steps Parker performs. Exact deltas depend on channel mix, holdout design, and Shopify order data.

Targeted by the 7-day pilot

Concrete deltas. Architecture targets for DTC.

Four metrics targeted by the 7-day pilot structure. Exact numbers depend on channel mix, AOV, and current attribution stack.

Platform ROAS reconciliation

Self-attribution→P&L-reconciled

Architecture target: Parker reconciles platform-claimed revenue against Shopify orders every night. Exact gap depends on channel mix and overlap.

Forecast accuracy

Predictions not written down→Every prediction scored against actuals

Each forecast is recorded before the period it covers, then compared with what actually happened. A miss is analyzed for cause and fed back into the model.

Decision speed

3-5 days→30 min

Architecture target: Sam runs the scaling scenario in seconds. The output is a confidence interval, not a meeting.

Setup time

Weeks→5 minutes

Architecture target: OAuth-based connection to Shopify, Meta, Google, TikTok. First insights inside 48 hours.

Common questions

Questions DTC teams ask

How does Cresva help DTC brands scale profitably?

The agents learn your unit economics and reconcile 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?

Cresva is agents: attribution plus forecasting plus scenario testing plus institutional memory plus reporting. Specialized agents share one memory. Sourced comparisons against both, including where they win, are at [/compare](/compare).

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 7-day pilot structure.