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TDM Revenue Systems

One-page system summary · CRM, CDP, Data Warehouse · Internal MarTech memo

This page summarizes a longer internal memo evaluating the current MarTech stack through the lens of scalability, cost efficiency, and data reliability. The focus was on clarifying system roles and identifying structural gaps that would limit reporting and decision-making as volume increases.

The problem

• No Customer Data Platform (CDP) to unify events and identities
• No central data warehouse for long-term storage and modeling
• Overlapping tools creating duplicated spend
• Measurement and attribution exposed to compliance and deliverability changes

Without addressing these gaps, marketing performance metrics become increasingly unreliable as scale increases.

Core insight

Each system serves a distinct role: execution, storage, and activation.

Attempting to centralize all logic inside a CRM introduces scale ceilings over time. Separating execution from data storage and activation allows systems to remain flexible while keeping reporting consistent and decision-grade.

Recommended system roles

• CRM (HubSpot): Executes campaigns and workflows. Serves as the operational system of record for contacts, companies, and deals, but is not designed for unlimited history or complex modeling.
• CDP (Composable): Collects customer and product events, resolves identity, and syncs enriched data into downstream systems such as the CRM, advertising platforms, and product tooling.
• Data warehouse: Central, vendor-neutral source of truth. Owns historical data, joins across systems, analytics, and downstream feature development.

Risks if unaddressed

• Tool sprawl and duplicated spend across overlapping platforms
• HubSpot scale ceilings related to properties, objects, and API limits
• Email and paid media performance degradation due to compliance changes
• Unreliable ROI and ROAS measurement
• Duplicate records and inconsistent reporting caused by weak identity strategy

Phased recommendations

Phase 1 — Immediate

• Move core automations to Zapier
• Consolidate outbound tooling (Apollo or Instantly)
• Implement a CMP and configure LinkedIn Offline Conversions

Phase 2 — Near term

• Stand up a CDP (Segment or RudderStack)
• Add product analytics (Mixpanel or PostHog)

Phase 3 — Foundation build

• Stand up a data warehouse (Snowflake, BigQuery, or Redshift)
• Shift complex logic and reporting out of HubSpot where appropriate
• Formalize data governance and AI data handling policies

Outcome

This approach reduces overlapping spend, preserves architectural flexibility, and improves trust in marketing and revenue metrics, while avoiding a costly re-platform later.