FOR REVOPS // MARKETING OPS // ATTRIBUTION

Your forecast is fiction.

Vendor scripts manufacture identity events your analytics ingests as real. The same vendors take credit for the pipeline they helped fabricate. Channel mix, CAC, and forecast read off corrupted data.

Audit attribution Track 2 Methodology

Attribution Audit

Q3 demand-gen

▸ Vendor-credited pipeline

$2.4M

attributed to intent vendors

▸ Pooled-signal contribution

~30% derived from your own CRM data

re-laundered through the shared graph

▸ At-risk campaign spend

8 – 22%

of demand-gen budget · Track 2 methodology

How we calculate this

01 // THE CASCADE

Five steps from your data to your wrong forecast.

The methodology calls this Campaign Effectiveness Degradation. Each step is observable. The cumulative effect is a forecast built on signal you can't trust.

Vendor takes

Your data flows out via browser-side identity resolution and OAuth-scoped CRM pulls. Quantified by Track 1.

Vendor pools

Your data joins your competitors' data in the shared intent graph. Account signal becomes cohort signal.

Vendor sells back

You receive “intent data” partially derived from your own CRM, repackaged with a UI and a brand.

Marketing acts on it

Your team runs campaigns, builds lists, allocates budget against accounts the vendor flagged as buyer-intent.

Attribution closes the loop

The vendor takes credit for the pipeline its signal helped manufacture. Board deck reports CAC built on circular data.

02 // ATTRIBUTION CIRCULARITY

The vendor crediting itself helped fabricate the signal.

When your attribution model credits a vendor with pipeline influence and that vendor's signal is partially derived from data they extracted from your own CRM, your attribution is circular.

The ROI you're reporting to your board includes your own data being sold back to you as a “signal.”

BLACKOUT://ATTRIBUTION/CIRCULARITYLOOP

[t-90d] Vendor pulls account from your CRM

[t-30d] Vendor signals “buyer intent” for that account

[t-7d] Marketing budget allocated to that account

[t-0] Account books a meeting

Vendor takes credit for “intent influence”

CAC calculation rolls in vendor cost as a campaign expense attributed to the vendor's “influence” on a pipeline they helped manufacture from your own data.

Vendor in the attribution loop · self-reportedCircular

03 // SIGNAL DECAY

The graph degrades as more companies join.

Signal-to-noise drops with each new contributor. The intent data you bought in 2023 with 500 contributors was higher quality than the same data in 2026 with 2,000 contributors. Your contract price didn't go down.

2023

~500

contributors · High

Original cohort. Strong signal-to-noise ratio.

2024

~1,200

contributors · Degrading

Cohort 2.4× larger. Signal dilution begins.

2026

~2,400

contributors · Compromised

Same contract. Materially lower per-contributor signal.

The vendor's customer base grew. The shared pool grew. Your signal-to-noise dropped proportionally. Your contract price stayed the same or went up.

04 // CUT THE LOOP

Block the contribution. Recover the signal.

You don't need to remove the vendor. You need to remove your contribution to the graph. Same dashboards, same campaigns, clean attribution restored.

Attribution Fidelity

Eliminate vendor-manufactured identity events. Channel mix reflects actual demand, not graph echo.

Forecast Accuracy

Pipeline coverage built on cleaned signal. Quarter-end actuals align with model predictions.

Per-Vendor Visibility

See which vendors are in your attribution loop, what data they extracted, and what fraction of their credit is circular.

Clean data in. Clean revenue out.

▸ No tag rip-and-replace · No attribution model rebuild · Per-vendor reversible

Stop reporting fiction to the board.

Run a scan. See which vendors are in your attribution loop and how much of your demand-gen spend is at risk.

Audit your attribution or read about the product →

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