Deploying a Churn Predictor

Apply trained Random Forest model over the newly read data

Produce score cards and visualizations

Deploying a Churn Predictor with Snowflake

This workflows connects to a Snowflake database, retrieves the latest customer data, and applies a pre-trained churn prediction model to estimate the likelihood of each customer leaving. The results are then prepared and visualized to provide clear insights into customer churn risk.

Connect to Snowflake and write new Telco data to database

📍Technical Note:

If you don't have a Snowflake account, you can sign up for a free 30-day trial account (when signing up, select Enterprise edition and choose any Snowflake cloud/region - preferably AWS or Azure).

Configure the Snowflake Connector node:

After signing up, navigate to your Snowflake Account details. There, you'll find key information to configure the node:

• Account identifier (input it in Full account name field)

• Login name (input it in Authentication > Username & password field). The password field is the password used to log in to your Snowflake account.

• Role (input it in Default access control role). Make sure the role is ACCOUNTADMIN.

• Stay in your Account details and navigate to the Config File tab. Select the warehouse you prefer (e.g., SNOWFLAKE_LEARNING_WH, if you're using a free trial account) and input the warehouse name in the Virtual warehouse field.

📍Technical Note:

[For users with a free 30-day trial account] In the Write Telco data to DB component, keep the default configurations and simply execute the component. The Table name is defined automatically.

[For users with a regular account] Double-click on the Write Telco data to DB component and provide your preferred Database and Schema names (they must already exist). The Table name is defined automatically.

💡Pro tip: If your working with very large datasets, we recommend replacing the DB Writer node with the DB Table Structure Creator + DB Loader nodes for faster, bulk data loading to database.