You invested in Dreamdata to settle the question of which channels drive revenue. This audit tells you whether the answers it's giving you are right - and whether everyone in the business is looking at the same number.
Request a sample auditWhether all channels are actually passing data into Dreamdata - or whether a tracking break is making a channel look like it's producing nothing when it isn't.
Which attribution models are configured, which one is being used for which decisions, and whether they're consistent - or silently giving different answers to the same question.
What each channel actually closes directly versus what it assists - and what that means for channels that look weak on direct attribution but drive the majority of influenced pipeline.
Whether your CAC figures are stable benchmarks or noise - and what's driving month-to-month variance that makes forecasting unreliable.
Which channels are being underinvested in because they're measured by the wrong model - and which are getting credit they haven't earned.
Every finding ordered by commercial impact: tracking breaks first, model divergence second, optimisation opportunities after.
Recent findings from delivered audits, anonymised.
The same LinkedIn revenue, measured by two different views in the same Dreamdata account, differed by 47×. Both were technically correct under their respective attribution models. Neither was being used consistently.
Dreamdata audit, B2B SaaS, 2026One channel was spending £31,000 with zero attributed revenue or influenced pipeline showing in Dreamdata - not because it wasn't working, but because tracking had broken and the spend was invisible to the platform.
Dreamdata audit, B2B SaaS, 2026CAC swung from £1,053 to £3,577 across months in the same calendar year with no identifiable driver in the attribution data - making it unusable as a planning benchmark.
Dreamdata audit, B2B SaaS, 2026Request a sample of the audit - so you can evaluate if it's a good fit for you.
Request a sampleAn independent review of whether your Dreamdata setup is producing reliable, consistent revenue attribution. It checks that all channels are passing data into the platform, that the attribution model being used is appropriate for how you make budget decisions, and that the numbers leadership is relying on are internally consistent - not contradicting themselves depending on which Dreamdata view you open.
Setups drift. Tracking breaks without triggering an alert. Multiple attribution models accumulate without anyone noticing they give different answers. One recent audit found the same LinkedIn revenue reported as two completely different figures - both technically correct under different Dreamdata views, both being used in different parts of the business to make decisions. The setup wasn't wrong. The configuration had never been agreed.
That depends on how you spend. Multi-touch fractional models are accurate for understanding cross-channel contribution. First-touch or last-touch models are accurate for understanding acquisition sources. The problem is that most accounts accumulate multiple models over time, and different teams use different views - so the same channel looks like it's working in one report and failing in another. The audit establishes which model fits your sales cycle and enforces it consistently.
Read-only access to your Dreamdata account plus the connected ad platforms (Google Ads, LinkedIn, Bing if running), your CRM, and your cost data. Nothing is changed - the audit reads, evidences and prioritises.