How to build Churn Prediction in Snowflake using RudderStack data

---

The Architecture: Headless Product Intelligence

To implement Churn Prediction effectively, you need to move beyond simple event tracking, interactive funnel analysis and siloed dashboards. By leveraging your existing Snowflake infrastructure, you can create a single source of truth for your GTM and Product teams.

---

The "Logic Gap": Why Manual Implementation is Hard

1. Volumetric Blind Spots: Simple SQL queries often miss 'silent churn'—users who are still logging in but whose usage volume has dropped by 50%.
2. Maintenance Nightmare: Maintaining a predictive model in SQL requires constant updating as your product features and event names change.
3. Actionability Gap: A risk score in a database doesn't save a customer. You need to route that score to the CSM's Slack immediately.

---

Implementation: The Snowflake-Native Code

If you were to build this manually, your dbt model or SQL query in Snowflake might look like this:

/*
   Generic example for Churn Prediction
   Tailored for Snowflake architecture
*/

WITH raw_events AS (
    SELECT
        account_id,
        event_name,
        timestamp
    FROM `snowflake.raw_data.events`
    WHERE event_name IN ('app_uninstalled', 'subscription_cancelled', 'login_failure')
),

calculated_metrics AS (
    -- Snowflake-specific logic for Churn Prediction
    -- e.g., using specific window functions or time-travel
    SELECT
        account_id,
        COUNT(*) as signal_volume,
        DATE_TRUNC('day', timestamp) as metric_date
    FROM raw_events
    GROUP BY 1, 3
)

SELECT * FROM calculated_metrics;

---

The GrowthCues Advantage: Automate the Signal

While the code above provides a starting point, GrowthCues eliminates the need for manual SQL maintenance entirely. You can define Churn Prediction using our no-code Milestone Editor, and GrowthCues will automatically:

• Calculate the signal at the account level using your RudderStack data.
• Detect anomalies and behavioral shifts using robust statistical methods.
• Push actionable signals back into your Snowflake, where you can activate them directly into the tools your team uses (Slack, HubSpot, Salesforce).

GrowthCues acts as the Headless Intelligence Layer for your Snowflake. It connects directly to your RudderStack tables and automatically:

• Calculates Churn Prediction at the account level.
• Detects anomalies and behavioral shifts in real-time.
• Pushes actionable signals back into your warehouse, where you can activate them directly into the tools your team uses (Slack, HubSpot, Salesforce).

Deep Dive: Predicting Churn

Stop relying on gut feel. Read our guide on How to Automate Churn Prediction to build a proactive retention engine.

Ready to turn your raw Snowflake data into revenue?

Join the Technical Preview