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Data ActivationBigQuerySegmentExpansion Signals

Syncing Expansion Signals scores from Segment to Salesforce via Census on BigQuery using GrowthCues Core

Bridge the data activation gap. Learn to push Expansion Signals from BigQuery via Segment and GrowthCues Core's semantic layer to Salesforce using Census.

For: RevOps, Growth Leads

Bridge the data activation gap. Learn to push Expansion Signals from BigQuery via Segment and GrowthCues Core's semantic layer to Salesforce using Census.

The Context Problem: Bridging the Warehouse-to-CRM Gap

Your BigQuery is a treasure trove of product usage data from Segment, generating powerful Expansion Signals signals. However, activating these insights directly in business tools like Salesforce for your RevOps, Growth Leads is a common challenge. Manual data exports are slow and outdated, while custom integrations are brittle and resource-intensive. This "activation gap" prevents your GTM teams from acting on timely, data-driven signals.

GrowthCues Core ensures your Expansion Signals are standardized and ready for seamless activation in your CRM via Reverse ETL.

Architecture: Data Activation Playbooks with GrowthCues Core

This diagram illustrates how GrowthCues Core transforms raw Segment events in your BigQuery into actionable Expansion Signals signals, which are then pushed to Salesforce using Reverse ETL (Census) (Reverse ETL).

The "GrowthCues Core" Code Block: Activating Expansion Signals in Salesforce

GrowthCues Core provides standardized definitions and calculations for key B2B GTM metrics like Expansion Signals directly in your BigQuery.

Here’s a conceptual configuration for Reverse ETL (Census) to sync Expansion Signals from your BigQuery to Salesforce:

// Example JSON configuration for a Reverse ETL tool (e.g., Hightouch, Census, RudderStack)
{
  "sync_name": "Sync GrowthCues_Core Expansion Signals to Salesforce",
  "source": {
    "type": "BigQuery",
    "table": "your_project.growthcues_core.fct_account_metrics_daily" // Or fct_user_metrics_daily, etc.
  },
  "destination": {
    "type": "Salesforce",
    "object": "Account", // Or Lead, Contact
    "match_on": "external_id__c", // Matching key in Salesforce
    "update_mode": "upsert"
  },
  "mapping": [
    {
      "source_column": "account_id",
      "destination_field": "external_id__c"
    },
    {
      "source_column": "your_expansion-signals_score", // e.g., pql_score, expansion_signal_strength
      "destination_field": "growthcues__expansion-signals__c" // Custom field in Salesforce
    },
    {
      "source_column": "metric_date",
      "destination_field": "growthcues__last_updated__c"
    }
  ],
  "schedule": "daily"
}

Note: The specific source_column and destination_field will vary based on Expansion Signals and your Salesforce setup. This example assumes fct_account_metrics_daily for account-level metrics.

Step-by-Step: Implement Data Activation with GrowthCues Core

  1. Deploy GrowthCues Core: Clone the GrowthCues Core dbt project and deploy it to your BigQuery, configuring it to consume raw event data from Segment.
  2. Calculate Core Metrics: Use GrowthCues Core to compute Expansion Signals (e.g., PQL scores, Expansion Signals, User Lifecycle States) from your product usage data in BigQuery.
  3. Configure Reverse ETL: Set up Reverse ETL (Census) (e.g., Hightouch, Census, RudderStack) to connect to your BigQuery and the relevant GrowthCues Core output tables.
  4. Map and Sync: Define data mappings to push Expansion Signals scores and other relevant attributes from your BigQuery to the appropriate fields in Salesforce (or other CRM).
  5. Automate GTM Workflows: Empower your RevOps, Growth Leads to create automated tasks, alerts, and personalized outreach in Salesforce based on these newly synced, data-driven Expansion Signals signals.

The GrowthCues Core Advantage: AI-Ready, Open-Source, and Standardized

  • AI-Ready Data: Eliminates LLM hallucinations by providing structured context directly in your data warehouse.
  • Standardized Metrics: Ensures a single source of truth for all B2B GTM metrics, solving the "Truth Gap."
  • Open-Source & Extensible: MIT-licensed, offering full transparency, control, and customization over your core GTM logic.
  • Reduced Maintenance: Replaces brittle custom SQL with robust, community-driven dbt models.

Further Reading

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Open-source dbt models for product-led GTM. Start building standardized, AI-ready metrics in your data warehouse.

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