Use case
Know which acquisition journeys lead to real orders.
DATALYR helps ecommerce teams bring marketing visits and supported commerce records into one investigation. Compare how channels attract paying customers, follow the purchase journey, and separate first-order return from repeat revenue before deciding where to invest the next advertising dollar.
Compare plans and usage
Channel dashboards tell different versions of the sale.
Several advertising platforms may claim the same order under different attribution windows. Meanwhile, the commerce system records the actual payment and later refund. The useful question is not which dashboard has the largest number, but how the evidence changes when the same business definition is applied across acquisition journeys.
In DATALYR
Ecommerce Attribution
One business definition
Define a paid order, a new customer and the revenue measure before comparing channels. These definitions keep campaign discussions grounded in a consistent business outcome even when platform-reported conversions differ.
Purchase-path investigation
Inspect landing activity, checkout progress and the completed purchase together. A funnel problem and an identity gap can both reduce attributed results, but they require different changes from the team.
Repeat-value context
Evaluate later purchases separately from first-order economics. Customers from a mature acquisition cohort have had more time to return, so use comparable observation periods before interpreting differences in their value.
The workflow
From setup to a useful answer.
Connect the commerce source
Choose the supported payment or storefront integration that owns the sale. Document any checkout system that mirrors orders elsewhere so the same transaction does not enter your business comparison twice.
Verify the buying journey
Follow a tagged visit through the real customer path. Check purchase identity, amount and currency, then reconcile a refund so the measurement includes more than an optimistic first conversion count.
Compare the same outcome
Use a consistent attribution model and window for channel analysis. Keep platform-reported results alongside, rather than summed into, the business view; annotate currency, timing and definition differences before changing budgets.
Verify the result
Know what good looks like.
- Paid orders reconcile with the connected source over matching dates.
- First-time customer counts exclude returning buyers.
- Channel comparisons use the same attribution window and revenue definition.
Go deeper
Use the guide for context, then follow the implementation reference for your setup.
A few practical questions.
Should I add the conversions reported by every ad platform?
No. Their attribution can overlap, and their reporting definitions may differ. Compare each platform’s view with a consistently defined business outcome instead of treating the sum as unique orders.
Is revenue return the same as profit?
No. A revenue figure does not automatically include product cost, fulfillment, fees or overhead. State the margin assumptions used in a budget decision and distinguish them from the observed payment records.
Explore related workflows.
- Shopify Attribution ↗
Connect Shopify orders, checkout activity and refunds to campaign visits with DATALYR. Verify purchase identity and reconcile transaction-based revenue.
- Checkout Champ Attribution ↗
Connect Checkout Champ purchases to acquisition context with DATALYR. Verify checkout identity, revenue records and overlap with connected Shopify stores.
- Multi-touch attribution software ↗
Compare first-touch, last-touch, linear and time-decay attribution in DATALYR, using an explicit lookback window and recorded customer touchpoints.