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Synthetic case study. This scenario uses a fictional B2B SaaS company and entirely synthetic data. It reflects common GTM systems and Marketing Operations challenges. No proprietary company information or source data is used.
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Scenario: Aperture Systems — fictional scaling B2B SaaS company
Focus: Marketing Operations · Revenue Systems · CRM Governance · AI Enablement
Platforms: Salesforce · HubSpot · Gong · Clay
Aperture Systems wants to use AI-assisted enrichment and outbound automation to increase pipeline coverage without proportionally increasing headcount.
The technology is already available. Salesforce serves as the CRM. HubSpot supports marketing automation. Gong captures sales activity. Clay can enrich records and support AI-assisted research and personalization.
The apparent next step is simple:
Identify targets → enrich them → personalize outreach → automate execution.
But before scaling that workflow, I would ask a different question:
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Can the GTM operating system reliably determine who should enter an automated motion in the first place?
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To test that question, I created and audited a synthetic GTM environment containing 200 accounts and 3,000 contacts.
The audit showed that the biggest obstacle was not missing technology. It was operational ambiguity.
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3,000
Contacts evaluated
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31
Accounts with inconsistent Salesforce and HubSpot lifecycle states
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497
Contacts with active sales activity that could conflict with automation
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200
Accounts evaluated
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291
Contacts affected by critical customer or opportunity-state conflicts
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280
Marketing contacts with suppression or deliverability conflicts
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294
Records with inactive account or contact ownership
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904
Contacts without usable job titles
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265
Records with engagement classifications that did not reconcile cleanly to supporting dates
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The risk wasn't dirty data. It was unreliable decision logic.
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