Reporting that connects ad spend to profitable growth.
This public sample shows how ATTN Labs can turn Shopify revenue and Meta Ads performance into a practical e-commerce growth view. The figures below are fictional and are included only to demonstrate report structure, terminology, and decision-making context.
Synthetic executive snapshot
Example 30-day view for a fictional wellness e-commerce brand. These numbers are not client results, forecasts, or benchmarks.
Net sales after discounts and refunds.
Spend mapped to acquisition and retargeting.
First-time purchasers identified in Shopify.
Average order value from store-side order data.
Channel performance
| Channel | Spend | Revenue view | Readout |
|---|---|---|---|
| Meta prospecting | $78,400 | $238,900 | Efficient new-customer volume; creative fatigue beginning in two ad sets. |
| Meta retargeting | $18,700 | $91,600 | Strong short-window conversion, but overlap with email should be watched. |
| Influencer whitelisting | $14,900 | $52,300 | Higher CTR than core ads; needs landing-page test for subscription offer. |
| Email / SMS context | n/a | $103,200 | Included as store-side comparison, not paid media spend. |
What changed
Example takeaway: growth came primarily from new-customer acquisition, while retention channels supported blended revenue and repeat purchases.
Six-week synthetic revenue pattern
W1
$58k
W2
$66k
W3
$61k
W4
$79k
W5
$88k
W6
$96k
Example decisions this report supports
Separate prospecting from retargeting
Watch refund-adjusted revenue
Compare Meta ROAS with blended MER
Spot new-customer CAC drift
Connect offer tests to AOV
The goal is not to make every metric look perfect. It is to make the next growth decision obvious enough to act on.
Attribution, explained plainly
Platform reporting and store reporting answer different questions. ATTN Labs reporting is designed to keep both views visible without pretending one number tells the whole story.
Start with Shopify as the revenue source of truth
Orders, refunds, discounts, customer type, and product revenue come from the commerce platform so the business view ties back to actual store activity.
Use Meta Ads for media performance signals
Spend, creative, campaign structure, click behavior, and platform-attributed conversions help diagnose what the ad system is optimizing toward.
Compare views before making budget calls
Meta ROAS, blended MER, new-customer CAC, and contribution-aware revenue are reviewed together to reduce overreaction to any single attribution model.
Data handling summary
This sample uses invented data. For real reporting work, access and handling should be scoped to the operational need.
Minimum necessary access
Connect only the accounts and fields needed for reporting, optimization, and agreed business questions.
No public client data
Public examples should use synthetic, anonymized, or permissioned material only. This page uses synthetic figures throughout.
Clear metric definitions
Revenue, ROAS, MER, CAC, AOV, refunds, and attribution windows should be defined before stakeholders compare results.
Review before sharing
Reports intended for external audiences should be checked for accidental identifiers, screenshots, names, or account details.
About store-specific reporting
Store-specific reporting is prepared for merchants working with ATTN Labs through our reporting workflow. This page shows an illustrative example only.
