Assessment · 6 November 2026
Can the data be trusted? · Vantage Software
Core data capabilities in place but significant work is needed. Targeted remediation required before AI adoption (Band C).
What can the data actually run?
The finding that lands
Counts computed from the data1,582 customers exist as two different companies across HubSpot and QuickBooks.
€1,969,990 of revenue can't be reconciled
The same account is recorded under different names, IDs or addresses in each system. Each one is clean in isolation, so every single-system audit passes them.
Broken for AI: Any AI keyed on customer identity (pricing, churn, lifetime value, next-best-action) trains on a contradiction and ships confident, wrong answers.
3,224 of 21,600 customers in HubSpot have no matching record in QuickBooks.
€1,969,990 of revenue sits in a cross-system blind spot
Forecasting and revenue AI can only see half the picture, so projections are built on a partial book.
734 internal employees from Gusto are sitting in the HubSpot contact list.
734 records mislabelled as customers
Segmentation, scoring and outreach AI treat employees as prospects, polluting every model trained on the list.
Use Case Readiness
Whether the data can run each standard AI use case, and the gap to close.
0 of 6 relevant use cases ready · tuned to B2B SaaS
The Opportunity · AI Value Creation
What this data could actually run to create value
Specific value-creation plays for this company's data, including moves beyond the standard use cases. Grounded and put through an adversarial check: every fact is cited and verifiable; the play is a hypothesis, not a promise; no figure is invented. Open any play for a full, downloadable proposal.
Reconcile HubSpot↔QuickBooks customer identity to expose true churn and expansion revenue
Because CRM (HubSpot) and billing (QuickBooks) records for the same customer are not linked, the company cannot reliably see who is a paying customer vs. an open pipeline, which hides real net revenue retention. A deterministic + fuzzy entity-resolution pass keyed on domain/legal name will link the 1,582 duplicated accounts and surface silent churn and un-billed expansions.
Based on - verifiable in the assessment
- 1,582 customers exist as two different companies across HubSpot and QuickBooks · crossSystem HubSpot ↔ QuickBooks context
- HubSpot ↔ QuickBooks match rate is 46% · crossSystem HubSpot ↔ QuickBooks matchRate
- Connectivity pillar scores 46 · gaps Connectivity 46
Gap: 46% match rate means the majority of customer records cannot be auto-linked; needs an entity-resolution build and a canonical customer_id shared across HubSpot and QuickBooks.
Billing-signal churn-risk agent from QuickBooks payment behaviour
Once customer identity is resolved, invoice/payment behaviour in QuickBooks (late payments, downgrades, shrinking invoice amounts, disputed lines) is a leading churn signal the CRM does not see. An agent that scores accounts on billing-behaviour drift and routes at-risk accounts to CSMs will predict churn earlier than usage or sentiment alone.
Based on - verifiable in the assessment
- QuickBooks is an in-scope system · systems
- HubSpot is an in-scope system · systems
- 1,582 customers exist across HubSpot and QuickBooks as unlinked entities · crossSystem HubSpot ↔ QuickBooks context
Gap: Depends on the HubSpot↔QuickBooks entity resolution above; also Consistency 58 and Lineage 52 mean invoice fields need standardising before behavioural drift can be measured reliably.
Separate employees from customers (Gusto↔HubSpot) to clean the revenue funnel
The 56% overlap between Gusto (employees) and HubSpot (customers) indicates employees, internal test accounts, or personal contacts are polluting the CRM as if they were prospects/customers. De-duplicating employees out of the funnel will correct pipeline, conversion, and per-rep metrics that current reporting distorts.
Based on - verifiable in the assessment
- Gusto ↔ HubSpot match rate is 56%, employee vs customer separation (HRIS to CRM) · crossSystem Gusto ↔ HubSpot
- Accuracy pillar scores 64 · gaps Accuracy 64
- Gusto is an in-scope system · systems
Gap: 56% match rate and Accuracy 64 mean the employee-vs-customer boundary must be reconciled with a Gusto-sourced employee identity list before CRM records can be safely flagged.
Data-governance uplift to unlock decision-grade reporting across all 6 pillars
With nothing deployable today and Governance already the strongest pillar (66), a targeted data-owner/definition-and-lineage program on the small 3-system stack (HubSpot, QuickBooks, Gusto) is the fastest way to move multiple 'needs work' use cases to deployable - a prerequisite that gates every other play.
Based on - verifiable in the assessment
- Overall score 58, band C · score / band
- 0 of 6 use cases are deployable now; 6 need work · leverability deployableNow 0 / needsWork 6
- Governance pillar scores 66 (the highest gap score) · gaps Governance 66
- Lineage pillar scores 52 · gaps Lineage 52
- Annual cost 724,755 · annualCost
Gap: Lineage 52 and Consistency 58 require documented field-level definitions and ownership before pillar scores lift; the work itself can start immediately on the existing stack.
Exploratory · needs validation before it's shown to a client
Usage-and-billing-based dynamic pricing / packaging engine
Cross-referencing what customers actually pay (QuickBooks) against segment/firmographic data in HubSpot will reveal systematic under-pricing pockets and packaging mismatches, enabling segment-based price realignment at renewal.
Based on - verifiable in the assessment
- QuickBooks (billing) and HubSpot (CRM) are in-scope systems · systems
- Completeness pillar scores 60 · gaps Completeness 60
- Consistency pillar scores 58 · gaps Consistency 58
Gap: Blocked: no product-usage/telemetry system appears in the profile (only HubSpot, QuickBooks, Gusto), so value-based pricing lacks a usage signal; Completeness 60 and Consistency 58 also undermine cohort definitions.
Are you ready for the tools you're evaluating?
0 of 8 ready on today's dataName the AI tools on your shortlist and we score whether your data can actually feed each one. Independent by design: we sell none of them. Tools marked External data depend on inputs we do not read (call recordings, web or intent events), so we score the structured prerequisites and say so.
| Tool | Verdict | Readiness | Assessability | Fix first |
|---|---|---|---|---|
HubSpot AI Predictive Lead Scoring and Breeze CRM / Predictive Lead Scoring | Conditional | 62 | Fully assessable | Close the flagged data gaps first |
Celonis Process Mining and Execution Management | Conditional | 62 | Fully assessable | Close the flagged data gaps first |
Clari Revenue Intelligence | Conditional | 46 | Partial | Close the flagged data gaps first |
Gainsight Customer Success / Health Scoring | Conditional | 46 | Partial | Close the flagged data gaps first |
MadKudu Predictive Lead Scoring | Conditional | 46 | Partial | Close the flagged data gaps first |
Sift Digital Trust and Fraud Prevention | Conditional | 46 | Partial | Close the flagged data gaps first |
Gong Conversation Intelligence / Revenue Intelligence | Not ready | 34 | External data | Connect Core input is recorded and transcribed sales calls, meetings and emails, captured through telephony/video/email integrations, not from any CRM/ERP/HRIS export DataTruth reads |
6sense ABM / Predictive Intent | Not ready | 34 | External data | Connect Core signal is third-party intent and anonymous web behaviour from the 6sense Signalverse, plus a website tracking tag, none of which appears in a CRM/ERP export |
In a live assessment these verdicts are measured from the company's own systems, across 30+ tools including the ones on your list.
What it costs to wait
Conservative estimate, built line by line from observed dataCapital at risk, this year (estimate)
€522-957K
The assessment is €20,000. The single largest line alone is worth €433,398. This is insurance, not a report.
How we get to this number
Revenue leakage from unreconciled systems
€433,398
Observed: €1,969,990 of revenue across 3,224 accounts has no match between HubSpot and QuickBooks
Rate: x 22% leakage (collection slippage, attribution loss, double-billing), the low end of the 20-35% range
AI initiative at risk of stalling
€181,440
Observed: Truth Score 58 (Band C), 42 points below AI-ready
Rate: x 1.8% of revenue, scaled by the readiness gap (most stalled GenAI fails first on data quality)
Wasted spend on duplicate / contaminated records
€50,637
Observed: ~8% of 21,600 contacts are duplicates or internal records
Rate: x €7.40 cost-per-contact x 4 touch cycles a year
Manual reconciliation & rework
€38,880
Observed: 36-point accuracy gap across 120,000 records
Rate: x ~€0.90 per record of analyst time stitching systems by hand
Governance & compliance exposure
€20,400
Observed: 34-point governance gap: ownership, lineage and audit trail
Rate: data-governance & audit-trail readiness for AI regulation, capped conservatively at €60K
Every rate is deliberately conservative and yours to change. Dial them to your own numbers and the total moves with you. The point is the observed finding, not the decimal.
The findings above are observed facts in the data. The euro figures are modelled estimates from those facts. On a real engagement every number is computed from your actual records, with the working shown. Demo figures are illustrative.
The evidence behind the verdict
Six-pillar breakdown
Click a pillar to open the engineHow it compares
Positioned on the B2B SaaS reference model - a calibrated cross-sector baseline. Live peer benchmarking activates as real assessments accumulate.
Top 35%
+6 vs reference median
Pillars vs sector median
Continuous monitoring
Every snapshot retained since onboarding · trend, not just point-in-time
▲ 21 pts since Jan - ~€71K annual data-risk reduced
37 → 58
What moved over the period
Value attributed: a 21-point improvement reduced estimated annual data-risk exposure by ~€71K. This is what continuous monitoring proves - and what justifies the recurring engagement.
The roadmap: what to fix first
Backfill product-usage events into the CRM - 31% of accounts have no usage signal, blinding any churn model.
Board-ready deliverable
Download a sample IC board pack built from this assessment.
Buying this company? See the M&A diligence verdict
Proceed, fix, or walk, with the time-to-AI-ready and remediation cost, the buyer's view of Vantage Software.