Automate PQL Activity alerts with GitHub Actions/Python on Snowflake using Segment and GrowthCues Core

Implement $0 PQL Activity monitoring. Learn to set up GitHub Actions/Python to trigger proactive alerts from Snowflake data via Segment and GrowthCues Core's semantic layer.

The Context Problem: Escaping Reactive GTM Dashboards

Traditional dashboards often present GTM metrics reactively – by the time you see a "red" health score or a dip in usage, the opportunity for proactive intervention has passed. Building real-time, automated alerts for complex B2B signals like PQL Activity directly from your Snowflake typically involves brittle custom SQL and complex orchestration. This "logic gap" means GTM Engineers and RevOps teams spend more time extracting insights than acting on them.

GrowthCues Core provides the standardized, AI-ready metrics you need to power effective, headless GTM automation.

Architecture: Headless GTM Automation with GrowthCues Core

This diagram illustrates how GrowthCues Core transforms raw Segment events in your Snowflake into actionable PQL Activity signals, which are then used by GitHub Actions/Python to trigger automated alerts.

The "GrowthCues Core" Code Block: Automating PQL Activity Monitoring

GrowthCues Core calculates essential B2B GTM metrics, including PQL Activity, directly in your Snowflake. This open-source semantic layer provides reliable, standardized definitions.

Here’s a simplified Python snippet from a GitHub Actions workflow that queries GrowthCues Core for PQL Activity thresholds in Snowflake and triggers an alert:

# main.py - simplified Python script for GitHub Actions
import os
from google.cloud import bigquery # or snowflake.connector for Snowflake

def get_alerts():
    client = bigquery.Client(project=os.environ.get("GCP_PROJECT_ID")) # Adjust for Snowflake
    
    # Query GrowthCues Core's fct_account_metrics_daily (or other relevant table)
    # The specific table and column will depend on CORE_METRIC_CONCEPT
    # Example for "Silent Churn" (volume_change_ratio_7d < 0.5)
    # Example for "Expansion Velocity" (net_new_users_7d > X)
    # Example for "PQL Activity" (pql_score > Y)
    # Example for "Team Activation" (team_activation_score < Z)
    
    # Placeholder for dynamic metric query logic
    metric_column = "" 
    threshold = 0.0
    operator = "<"
    if "PQL Activity" == "Silent Churn":
        metric_column = "volume_change_ratio_7d"
        threshold = 0.5
        operator = "<"
    elif "PQL Activity" == "Expansion Velocity":
        metric_column = "net_new_users_7d"
        threshold = 5 # Example: 5 new users in 7 days
        operator = ">"
    elif "PQL Activity" == "PQL Activity":
        metric_column = "pql_score"
        threshold = 70 # Example: PQL Score > 70
        operator = ">"
    elif "PQL Activity" == "Team Activation":
        metric_column = "team_activation_score"
        threshold = 0.6 # Example: Team Activation Score < 0.6 (low activation)
        operator = "<"

    query = f"""
    SELECT
        account_id,
        {metric_column} as metric_value,
        metric_date
    FROM `your_project.growthcues_core.fct_account_metrics_daily`
    WHERE metric_date = DATE_SUB(CURRENT_DATE(), INTERVAL 1 DAY)
        AND {metric_column} {operator} {threshold}
        AND {metric_column} IS NOT NULL
    LIMIT 5
    """
    
    query_job = client.query(query)
    results = query_job.result()
    
    alerts = []
    for row in results:
        alerts.append(f"Account {row.account_id}: PQL Activity Alert (Value: {row.metric_value:.2f})")
    return alerts

if __name__ == "__main__":
    triggered_alerts = get_alerts()
    if triggered_alerts:
        print(f"Detected {len(triggered_alerts)} GTM alerts:")
        for alert in triggered_alerts:
            # In a real scenario, this would post to Slack, send email, etc.
            print(alert) 
    else:
        print(f"No PQL Activity alerts detected.")

Note: Replace your_project.growthcues_core.fct_account_metrics_daily with your actual table reference. The metric_column, threshold, and operator logic in the Python script would be dynamically generated or configured based on PQL Activity.

Step-by-Step: Implement Headless GTM Automation with GrowthCues Core

Deploy GrowthCues Core: Clone the GrowthCues Core dbt project and deploy it to your Snowflake, configuring it to consume raw event data from Segment.
Identify Core Metric: Use GrowthCues Core to calculate standardized PQL Activity from your product data in Snowflake.
Develop Automation Script: Write a Python script (or similar) that queries the relevant GrowthCues Core output table in your Snowflake for specific thresholds or anomalies related to PQL Activity.
Configure GitHub Actions/Python Workflow: Set up a scheduled GitHub Actions/Python workflow (e.g., GitHub Actions, Airflow, Prefect) to execute your script daily or at a desired frequency.
Trigger Proactive Alerts: Integrate your script with communication tools (e.g., Slack, email, PagerDuty) or CRM to deliver proactive PQL Activity alerts to your GTM Engineers, RevOps at the right time.

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.