Product
See how credit changes across the journey.
Compare first-touch, last-touch, linear and time-decay attribution in DATALYR, using an explicit lookback window and recorded customer touchpoints. Make the model part of the decision, rather than a hidden reporting setting.
Compare plans and usage
One order can support more than one interpretation.
A prospect might discover you through an ad, return through search and finally buy after an email. Changing the allocation rule changes which touchpoint receives credit; it does not change the payment. DATALYR gives teams a way to compare supported attribution models while retaining the revenue and journey context needed to interpret the difference.
In DATALYR
Multi-touch attribution software
Compare allocation rules
Use first-touch and last-touch alongside linear or time-decay views. A model comparison is most useful when the business question and eligible journey stay explicit.
Choose the lookback window
Keep the attribution window consistent when comparing campaigns or periods. A changed window can move reported credit even when the underlying purchases stay the same.
Inspect the supporting journey
Move from an aggregate result to recorded customer activity. Look for missing acquisition context before treating an unattributed payment as evidence that marketing had no role.
The workflow
From setup to a useful answer.
Set a revenue definition
Decide which transactions and refund treatment belong in the analysis. Keep the same revenue scope across models so the comparison isolates the attribution rule.
Compare a fixed period
Choose one date range, window and channel breakdown. Review the allocation changes between models instead of combining incompatible totals into a single score.
Turn the difference into a question
Investigate channels that introduce buyers but rarely close, or vice versa. Use the findings to plan creative, journey and experiment follow-up rather than declaring causal lift.
Verify the result
Know what good looks like.
- The period, currency and revenue basis match across models.
- The attribution window is recorded with the report.
- A sample of attributed orders has understandable supporting touchpoints.
Go deeper
Use the guide for context, then follow the implementation reference for your setup.
A few practical questions.
Does attribution prove incrementality?
No. Attribution allocates observed revenue across recorded interactions. Incrementality asks what would have happened without the marketing, which requires an appropriate experiment or causal method.
Will every touchpoint be visible?
Only activity captured and connected by the implementation can inform the observed journey. Missing consent, identity or channel data can leave gaps that a model cannot repair.
Explore related workflows.
- Customer journey analytics ↗
Explore customer sessions, events and conversions in DATALYR to understand the recorded path from acquisition to payment.
- Ad analytics software ↗
Review ad spend, campaign performance and attributed revenue in DATALYR. Keep platform-reported conversions distinct from first-party outcomes.
- Subscription revenue attribution ↗
Connect supported subscription revenue sources to DATALYR and distinguish first payments, renewals and refunds in your acquisition analysis.