Finance-ready attribution ledger

Know Which Campaigns Actually Create Revenue

DriveMetaData is revenue measurement and marketing attribution software that reconciles ad platform claims, first-party events, CRM revenue, SKAN signals, and fraud quality into one finance-ready attribution ledger.

Built for growth, finance, and data teams that need accurate attribution, fraud-adjusted ROAS, and revenue confidence from every campaign.

Server-side truth
SKAN-ready
Fraud-adjusted
BI exports
app.drivemetadata.com / measurement/revenue-truth-ledger
Reconciliation ledger
Platform claims in. Finance-ready revenue out.
$264k
verified
96%
confidence
-18%
overlap
Verified revenue trend
Claims resolved
Meta Ads$132k
18% overlap removed
Google Ads$96k
CRM revenue matched
TikTok$41k
11% invalid traffic
Final decision
$184k
CRM-backed revenue after dedupe, fraud, and refunds.
Dispute detected
Meta + Google claimed the same cohort.
CRM revenue wins the final value.
Verified ROAS
Clean ROAS4.6x
Invalid traffic11%
The measurement problem

Every Platform Claims Credit. Finance Needs One Truth.

Ad dashboards are useful signals, not final revenue. DriveMetaData turns disputed claims into a finance-ready ledger before budget moves.

app.drivemetadata.com / measurement/reconciliation-layer
Meta claims
$132k
Partial claim, not final truth
Google claims
$96k
Partial claim, not final truth
CRM booked
$184k
Partial claim, not final truth
Fraud flagged
-$18k
Partial claim, not final truth
Verified revenue truth layer
Claims are deduped, matched to first-party revenue, adjusted for fraud, and exported as one auditable ROAS view.
Before

Every platform claims the same win

$269k
claimed revenue

Meta, Google, TikTok, CRM, and SKAN reports disagree before finance can trust ROAS.

After

One ledger certifies campaign revenue

$184k
verified revenue

Duplicate claims, fraud, refunds, and late privacy signals resolve into a defensible revenue view.

What changes after reconciliation
Duplicate credit removed before reporting
CRM revenue wins over platform estimates
Fraud and privacy gaps labeled clearly
Attribution reconciliation ledger

Resolve Disputed Campaign Revenue Before It Hits Reports

Every claim becomes an auditable row with the source, identity match, policy rule, fraud decision, and verified revenue value.

Duplicate credit removed before ROAS is published
CRM and commerce revenue treated as the source of truth
SKAN, consent, and modeled gaps shown with confidence
Attribution windowIdentity confidenceChannel precedenceSKAN gapFraud ruleRevenue source
app.drivemetadata.com / measurement/reconciliation-ledger
SourceClaim
Meta Ads
$132k
18% overlap removed
$108k verified
Dedupe applied
Google Ads
$96k
CRM revenue matched
$91k verified
Revenue trusted
TikTok
$41k
11% invalid traffic
$34k verified
Fraud adjusted
SKAN + app
$28k
Modeled privacy gap
$31k verified
Confidence scored
Dedupe
duplicate claims removed
Revenue
CRM value matched
Export
raw decision rows
First-party revenue truth

Connect Conversion Events to the Revenue Finance Trusts

Bring web, app, server-side, CRM, commerce, subscription, and offline signals into one revenue trail.

app.drivemetadata.com / conversions/live-pipeline
Capture
SDKs, APIs, CAPI, S2S
01
Reconcile
Dedupe, identity, SKAN
02
Certify
Fraud, CRM, refunds
03
Activate
BI, alerts, budget moves
04
Verified output
Finance-ready campaign revenue
Every export keeps source, confidence, attribution rule, fraud adjustment, revenue match, and decision timestamp.
Revenue sources reconciled
Web eventsApp eventsServer-sideCRMCommerceSubscriptionsOffline conversions
Delivery
Webhook and postback health by source
Backfill
Replay late events without changing audit logic
Exports
Raw rows for warehouse and BI teams
Platform architecture

Where Measurement Fits in DriveMetaData

Measurement is the intelligence layer between trusted customer data and activation. It turns first-party events, identity, fraud quality, and revenue systems into attribution decisions teams can act on.

Layer 01

Data Foundation

Customer profiles, identity resolution, consent-aware collection, server events, CRM revenue, commerce orders, and warehouse-ready pipelines.

CDPIdentity graphFirst-party eventsCRM + commerce
Layer 02

Intelligence Layer

Measurement sits here: attribution policy, duplicate claim resolution, fraud quality, SKAN confidence, analytics, and campaign intelligence.

MeasurementFraud qualityAnalyticsCampaign intelligence
Layer 03

Activation Layer

Verified audiences, journey decisions, retention workflows, suppression lists, and budget recommendations move back into growth channels.

Journey BuilderSegmentationAudience syncBudget moves
Customer truth
Profiles, consent, identity, events, and revenue objects.
Revenue truth
Attribution policy, fraud quality, confidence, and audit rows.
Growth action
Audiences, journeys, budget moves, alerts, and BI reporting.
Enterprise attribution methodology

Show Exactly How Attribution Is Decided

The decision path stays visible from raw claim to certified campaign revenue.

Audit-ready by design

Every decision keeps the source claim, matched identity, attribution rule, fraud reason, revenue object, export version, and timestamp attached.

Window
Logged
Revenue
Logged
Confidence
Logged
01
Capture

Collect platform claims, server events, app and web conversions, CRM updates, refunds, and offline revenue.

source timestamp
02
Match

Resolve users, devices, accounts, and anonymous paths with consent-aware first-party confidence.

identity score
03
Decide

Apply windows, channel precedence, campaign hierarchy, SKAN rules, and CRM revenue priority.

policy version
04
Certify

Remove bots, invalid clicks, duplicate claims, refunds, and suspicious conversions before ROAS is published.

audit row
Trusted campaign credit review

See how much of your reported ROAS is defensible.

Bring your ad platform claims, CRM revenue, fraud signals, and current reporting stack into a focused measurement audit.

Attribution decision example

See One Attribution Decision From Claim to Certified Revenue

A buyer should not have to trust a black-box ROAS number. Show the disputed claim, the decision rule, and the final revenue row.

Revenue Decision Lab
Attribution Verdict Studio
No black-box ROAS
Every claim is explainable
Decision cockpit
Conflict to verdict
Finance packet
Ready for export
Claim conflict · Claim duel

Two channels. One customer cohort. One revenue owner.

Meta Ads
$132k
claimed
VS
Google Ads
$96k
claimed
Meta and Google both claim the same high-value cohort.
Decision checks · Trust gates · Evidence gates
Identity
Known buyer matched
Window
7-day click policy
CRM
Booked revenue wins
Fraud
Invalid traffic removed
Policy
Precedence applied
CRM revenue + qualified touch decides final credit.
app.drivemetadata.com / measurement/attribution-decision
Certified revenue row · Final verdict
One certified answer
92%
confidence
SourceClaimFinal
Meta Ads
18% overlap with Google cohort
$132k
$108k
Google Ads
CRM revenue and last qualified touch win
$96k
$91k
TikTok
11% invalid traffic excluded
$41k
$34k
CRM booked
Finance source of truth
$184k booked
$184k
Finance-ready output · Finance packet
$30k
Rejected
$184k
Certified
Ready
Export
7-day click
Invalid traffic excluded
Finance row certified
Who can act on it
Same verdict, different decisions.
CMO
Defend where revenue came from before approving budget shifts.
Board-ready ROAS, confidence, rejected claims, and spend moves.
Performance marketing
Stop optimizing from inflated channel dashboards.
Clean campaign rankings by verified revenue and fraud-adjusted ROAS.
Data team
Inspect how every attribution decision was produced.
Raw rows, policy versions, audit logs, and warehouse export schemas.
Recommendation-to-decision

Turn Reconciled Measurement Into Budget Moves

Recommendations stay tied to the measured evidence: overlap, confidence, clean revenue, and expected impact.

Duplicate claim detected with the rejected source named
Verified ROAS scored before spend is moved
Budget moves tied to verified revenue impact
app.drivemetadata.com / ai-measurement/decision-map
AI recommendation
Shift 14% toward campaigns with verified CRM revenue and clean traffic.
01
Reject overlap

Meta and Google claim the same CRM cohort.

Keep the revenue-backed touch.
02
Score signal

SKAN arrives late and consent coverage is partial.

Publish modeled revenue with confidence.
03
Move budget

Clean campaigns beat claimed ROAS after fraud.

Shift spend only after verified revenue wins.
Verified ROAS
certified
Google81%
Meta74%
TikTok62%
Email58%
Trusted signal quality

Protect Revenue Attribution From Privacy Gaps and Fraud Noise

Signal quality travels with every report, export, alert, and budget recommendation.

Modeled privacy gaps are labeled, not hidden
Fraud exclusions happen before campaign ROI is certified
Revenue completeness is visible before spend shifts
app.drivemetadata.com / signal-quality/privacy-fraud
96%
Privacy gaps

SKAN, consent, and modeled revenue are labeled with confidence.

-11%
Fraud quality

Invalid clicks, bots, and suspicious installs are removed before ROAS.

4.6x
Revenue trust

CRM, commerce, refunds, and offline sales travel with the final row.

Exportable quality context

Each row carries the privacy model, fraud decision, revenue match, and confidence score.

Enterprise infrastructure

Measurement Infrastructure Built for Enterprise Review

Data teams can inspect delivery, replay events, audit decisions, and export the raw rows behind every campaign metric.

Layer 1
SDKs + APIs
claim intake
Layer 2
Event stream
delivery health
Layer 3
Identity graph
match confidence
Layer 4
Attribution ledger
policy decisions
Layer 5
Fraud quality
signal cleanup
Layer 6
Revenue exports
BI rows
RBAC, SSO, audit logs
Raw event exports
Warehouse-ready schemas
Backfill and replay
Retention controls
Data processing and privacy law support
Integrations

Connect the Systems That Argue About Revenue

Group ad platforms, MMPs, revenue systems, warehouses, and BI tools into the same attribution decision model.

Search ad platforms, MMPs, revenue systems, and destinations
Ad platforms
MetaGoogle AdsTikTokApple Search Ads
MMP + app
AppsFlyerAdjustKochavaBranch
Revenue systems
ShopifySalesforceHubSpotStripe
Warehouse + BI
SnowflakeBigQueryClickHouseLooker
Enterprise-grade foundation

Enterprise-Grade Foundation for Revenue Measurement

Give marketing, finance, and data teams enough governance detail to trust the attribution ledger before it changes budget decisions.

Controls buyers expect
SSO and role-based access
Audit logs for attribution decisions
Raw event and decision exports
Warehouse-ready schemas
Backfill and replay workflows
Data processing, privacy, and retention controls
Implementation path

Source mapping, SDK/API/server-side events, CRM and warehouse validation, attribution policy setup, then finance-ready reporting.

app.drivemetadata.com / measurement/export-schema
Sample export schema
Raw decision rows for BI and warehouse teams

Each row keeps the evidence behind the final number, so teams can audit, replay, and explain every campaign metric.

event_id
customer_key
campaign_id
source_claim
final_credit
confidence_score
fraud_flag
revenue_object_id
Freshness
event timestamp
Policy
versioned rule
Governance
audit-ready row
Revenue decisions

Give Growth and Finance the Same Campaign Truth

Replace ROAS debates with one auditable view of campaign quality, conversion truth, and revenue confidence.

+39%
Higher Verified ROAS
-42%
Duplicate claim exposure
2.8x
Faster revenue reconciliation
+31%
First-party match rate
Measurement FAQ

Measurement Questions Enterprise Buyers Ask

Answer the questions growth, finance, and data teams raise before trusting a new attribution source of truth.

Build Finance-Ready Campaign Attribution.

Bring platform claims, first-party conversions, CRM revenue, SKAN signals, and fraud quality into one attribution ledger your marketing, finance, and data teams can defend.