Building a GTM Data Stack: The Essential Components
A comprehensive guide to building a modern go-to-market data stack that powers sales, marketing, and customer success.
Table of Contents
The 4 Layers of a GTM Data Stack
A modern GTM data stack has four distinct layers:
1. Data Capture
Collecting data from all customer touchpoints (CRM, website, product, etc.)
2. Data Warehouse
Centralized storage for all your GTM data
3. Transformation
Cleaning, modeling, and enriching raw data
4. Activation
Pushing insights back to operational tools
Layer 1: Data Capture
The foundation of your GTM data stack is capturing data from every customer interaction.
Essential Data Sources:
CRM (HubSpot, Salesforce, Attio)
Contacts, companies, deals, activities
Product Analytics (Mixpanel, Amplitude)
User behavior, feature usage, engagement
Marketing Automation (Marketo, Pardot)
Email campaigns, landing pages, form submissions
Website (Segment, Google Analytics)
Visitor behavior, page views, conversions
Customer Success (Gainsight, Vitally)
Health scores, support tickets, NPS
Recommended Tools:
- →Segment/RudderStack: Event tracking and customer data platform
- →Fivetran/Airbyte: Pre-built connectors for 100+ SaaS tools
- →Custom APIs: For tools without native integrations
Layer 2: Data Warehouse
Your data warehouse is the single source of truth for all GTM data.
Top Warehouse Options:
Snowflake
Best for: Enterprise scale, complex queries
$$$
BigQuery
Best for: Google ecosystem, serverless
$$
Redshift
Best for: AWS-native, cost-conscious
$
💡 Pro Tip
Start with BigQuery or Snowflake. Both are easy to set up and scale with you. Avoid building your own warehouse on Postgres unless you have a dedicated data engineering team.
Layer 3: Transformation
Raw data from your sources needs cleaning, modeling, and enrichment before it's useful.
Key Transformation Tasks:
- →Data Cleaning: Deduplication, null handling, standardization
- →Dimensional Modeling: Building fact and dimension tables
- →Metric Calculation: CAC, LTV, NRR, pipeline velocity
- →Enrichment: Adding firmographic, technographic data
Essential Tool: dbt (data build tool)
dbt has become the standard for data transformation. It allows you to:
- →Write SQL transformations that version control like code
- →Test data quality automatically
- →Document your data models
- →Orchestrate transformation pipelines
Layer 4: Activation
The most powerful layer—pushing insights and segments from your warehouse back to operational tools.
Reverse ETL Use Cases:
Sales Scoring
Push ML-based lead scores from warehouse to CRM
Marketing Segmentation
Sync warehouse-built segments to email/ads platforms
Customer Health
Send usage-based health scores to CS tools
Personalization
Update website personalization with behavioral data
Top Reverse ETL Tools:
- →Hightouch: Most mature, best UI, supports 150+ destinations
- →Census: Developer-friendly, great for complex use cases
- →Polytomic: Good for smaller teams, simpler pricing
Reference Architecture
Here's a sample GTM data stack for a B2B SaaS company with 50-200 employees:
Modern GTM Stack Example
💡 Implementation Timeline
- • Weeks 1-2: Set up warehouse and initial connectors
- • Weeks 3-4: Build core dbt models and metrics
- • Weeks 5-6: Set up reverse ETL and activation
- • Weeks 7-8: Build dashboards and train teams
Need Help Building Your GTM Data Stack?
We help companies design and implement modern GTM data infrastructure. From warehouse setup to reverse ETL activation.
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