Composable CDP for B2B Companies

Traditional CDPs were built for B2C—tracking anonymous visitors, building consumer segments, and powering marketing automation. B2B companies adopted them but often struggle with account-centric use cases, complex buying committees, and the need for sales and marketing alignment.

The composable CDP offers an alternative: use your data warehouse as the foundation and compose capabilities from best-of-breed tools. This approach gives you the flexibility B2B requires without the constraints of monolithic CDP platforms.

Traditional CDP vs. Composable CDP #

Traditional CDP Architecture

flowchart LR
    Sources --> CDP[CDP Platform] --> Destinations
    CDP --- Note[All logic lives inside the CDP]

Characteristics

• Vendor owns your customer data
• Limited customization
• Pre-built identity resolution
• Closed ecosystem

Composable CDP Architecture

flowchart LR
    Sources --> DW[Data Warehouse] --> AL[Activation Layer] --> Destinations
    DW --- Note1[You own the data<br/>Full customization]
    AL --- Note2[Flexible tools<br/>Best of breed]

Characteristics

• You own your data
• Full customization
• Build your own identity logic
• Open ecosystem

Why Composable for B2B #

B2B-Specific Requirements

Composable Advantages

Flexibility Build exactly what you need, not what the vendor provides.

Cost Efficiency Pay for warehouse storage (cheap) not CDP licensing (expensive).

Data Ownership Your data never leaves your control.

Best-of-Breed Choose the best tool for each job.

Building Your Composable CDP #

Architecture Components

flowchart TB
    subgraph Sources[DATA SOURCES]
        CRM1[CRM]
        Marketing1[Marketing]
        Product1[Product]
        Enrichment1[Enrichment]
        Intent1[Intent]
        Web1[Web]
    end

    subgraph Ingestion[INGESTION LAYER]
        Tools[Fivetran, Airbyte, Segment]
    end

    subgraph Warehouse[DATA WAREHOUSE - Snowflake, BigQuery]
        Raw[Raw Data]
        Staging[Staging]
        Identity[Identity]
        Unified[Unified Profiles]
    end

    subgraph Activation[ACTIVATION LAYER]
        ReverseETL[Reverse ETL, Orchestration: Cargo]
    end

    Sources --> Ingestion
    Ingestion --> Warehouse
    Warehouse --> Activation
    Activation --> CRM2[CRM]
    Activation --> Marketing2[Marketing]
    Activation --> Ads[Ads]

Component Selection

Ingestion Layer

Storage Layer

Transformation Layer

Activation Layer

B2B Data Models #

Account-Centric Model

-- Unified Account Profile
CREATE TABLE unified_accounts AS
SELECT
  -- Identity
  account_id,
  domain,
  company_name,

  -- Firmographics (from enrichment)
  industry,
  employee_count,
  annual_revenue,
  funding_stage,

  -- Technographics (from BuiltWith, etc.)
  tech_stack,
  crm_used,
  marketing_automation,

  -- Engagement (aggregated)
  total_engagement_score,
  last_engagement_date,
  web_visits_30d,
  email_opens_30d,

  -- Product (from product analytics)
  is_active_user,
  monthly_active_users,
  feature_adoption_score,

  -- Intent (from intent providers)
  intent_score,
  intent_topics,
  intent_trend,

  -- Revenue (from CRM)
  pipeline_value,
  closed_won_revenue,
  arr,

  -- Scoring (calculated)
  icp_fit_score,
  pql_score,
  health_score,

  -- Segmentation
  account_tier,
  target_segment,
  territory

FROM account_master
JOIN enrichment ON ...
JOIN product_usage ON ...
JOIN intent_data ON ...
JOIN crm_data ON ...;

Contact-Centric Model

-- Unified Contact Profile
CREATE TABLE unified_contacts AS
SELECT
  contact_id,
  account_id,
  email,
  name,

  -- Role
  title,
  department,
  seniority,
  persona,
  buying_role,

  -- Engagement
  engagement_score,
  last_activity_date,
  preferred_channel,

  -- Activity
  emails_opened_30d,
  content_downloaded,
  meetings_attended,

  -- Product
  is_product_user,
  last_login,
  feature_usage

FROM contact_master
JOIN engagement_data ON ...
JOIN product_usage ON ...;

Identity Resolution

Build your own matching logic:

-- Account Identity Resolution
CREATE TABLE identity_graph AS
WITH matches AS (
  SELECT
    a.source_id,
    b.source_id AS matched_id,
    CASE
      WHEN a.domain = b.domain THEN 'exact_domain'
      WHEN similarity(a.name, b.name) > 0.9
           AND a.city = b.city THEN 'fuzzy_name_city'
      -- More rules...
    END AS match_type
  FROM source_accounts a
  CROSS JOIN source_accounts b
  WHERE a.source_id < b.source_id
    AND (a.domain = b.domain
         OR similarity(a.name, b.name) > 0.9)
)
SELECT
  generate_unified_id() AS unified_account_id,
  source_id,
  match_type
FROM matches;

Activation Patterns #

Pattern 1: Score Sync

Source: account_scores (warehouse)
Destination: Salesforce

Sync:
- icp_score → ICP_Score__c
- engagement_score → Engagement_Score__c
- account_tier → Account_Tier__c

Frequency: Every 4 hours

Pattern 2: Audience Sync

Source: target_accounts_tier1 (warehouse)
Destination: LinkedIn Ads

Sync:
- company_name
- domain
- linkedin_company_id

Frequency: Daily

Pattern 3: Alert Triggers

Source: high_intent_accounts (warehouse view)
Destination: Slack + Salesforce Task

Trigger: When new accounts appear in view
Action: Alert sales, create follow-up task

Composable CDP with Cargo #

Cargo serves as the activation and orchestration layer:

Direct Warehouse Connection

Workflow: Warehouse-Powered Scoring

Source: Snowflake/BigQuery

Query:
SELECT account_id, score, tier
FROM ml_models.account_scores
WHERE score_date = CURRENT_DATE

→ Sync to Salesforce
→ Sync to HubSpot
→ Trigger workflows based on score changes

Real-Time Enrichment

Workflow: Enrich and Store

Trigger: New account identified

→ Enrich from multiple providers
→ Store in warehouse (append)
→ Calculate scores
→ Sync to operational systems

Multi-System Activation

Workflow: Unified Activation

Source: Warehouse unified profiles

→ CRM: Update accounts and contacts
→ Marketing: Update segments
→ Ads: Sync audiences
→ Sales engagement: Update attributes

All from single source of truth.

Implementation Roadmap #

Month 1: Foundation

• Set up warehouse
• Implement ingestion for core sources
• Build basic staging models

Month 2: Identity & Unification

• Build identity resolution
• Create unified account model
• Create unified contact model

Month 3: Activation

• Implement reverse ETL
• Sync scores to CRM
• Sync audiences to ads

Month 4: Optimization

• Add more sources
• Refine models
• Expand activation use cases

Best Practices #

1. Start with use cases - Build for specific needs, not theoretical completeness
2. Own your identity - Build your own resolution logic
3. Document everything - Future you will thank present you
4. Monitor data quality - Garbage in, garbage out
5. Plan for growth - Design for 10x your current scale

The composable CDP gives B2B companies the flexibility to build customer data infrastructure that truly fits their needs—account-centric, sales-aligned, and fully customizable.

Ready to build your composable CDP? Cargo provides the activation layer to turn your warehouse data into operational intelligence.

Key Takeaways #

• Traditional CDPs were built for B2C: they struggle with account-centric use cases, buying committees, and sales alignment that B2B requires
• Composable CDP = warehouse as foundation: sources → ingestion → data warehouse → activation layer → destinations—you own the data
• Cost efficiency: pay for warehouse storage (cheap) instead of CDP licensing (expensive)—often 50-80% savings
• Build your own identity resolution: match accounts by domain and fuzzy name, contacts by email and domain+name—full control over logic
• 4-month implementation roadmap: foundation (month 1) → identity/unification (month 2) → activation (month 3) → optimization (month 4)

Frequently Asked Questions #