Snowflake-Labs/sfguide-Intervention-detection-for-retail-support

Snowflake-Labs/sfguide-Intervention-detection-for-retail-support

Description: Showcase how Snowflake can measure the business impact of customer support interventions using causal inference when RCTs are not feasible.

Language: Jupyter Notebook

License: MIT

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Forks: 0

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Created: 2025-09-11T19:58:18Z

Pushed: 2025-09-30T19:28:33Z

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README:

Intervention Detection for Retail Support

Overview

This demo demonstrates how to measure the business impact of customer support interventions using causal inference techniques in Snowflake. The purpose is to quantify the ROI of customer support by analyzing how support interactions affect customer purchase behavior when traditional randomized controlled trials aren't feasible. You'll set up a Snowflake environment with realistic retail data, build statistical models using integrated Python and SQL, and create visualizations that show measurable increases in customer spending following support interventions. By the end of this ~15-minute demo, you'll understand how to apply advanced analytics to prove the value of customer service investments and make data-driven decisions about support strategies.

Repository Structure

├── README.md # This file
├── LEGAL.md # Legal notice
├── LICENSE # License information
├── scripts/ # SQL and configuration files
│ ├── setup.sql # Database setup script
│ ├── teardown.sql # Cleanup script
├── notebooks/ # Jupyter Notebook files
│ ├── 0_start_here.ipynb # Main analysis notebook

📋 Getting Started

Prerequisites

📋 Account Requirements:

• ☑️ Snowflake account with appropriate privileges
• ☑️ Access to Snowflake Notebooks
• ☑️ Python packages: pandas, matplotlib, statsmodels (available in Snowflake Notebooks)

🧠 Knowledge Prerequisites:

• ☁️ Snowflake Account - Active account with appropriate privileges
• 📝 SQL Knowledge - Basic understanding of SQL syntax and concepts
• 📊 Domain Knowledge - Basic understanding of retail analytics and business context
• 📊 Data Analysis - Basic understanding of data analysis principles

🎯 Objectives

By completing this demo, you will:

• Master causal inference techniques when randomized trials aren't feasible in business scenarios
• Measure customer support ROI by quantifying intervention effects on purchase behavior
• Build statistical models using Snowflake's integrated Python environment with statsmodels
• Create counterfactual analysis to isolate true intervention impact from natural trends
• Generate actionable insights that inform strategic decisions about customer service investments

🌟 Demo Highlights

🔍 What You'll Discover:

• How customer support interactions create measurable increases in purchase behavior
• Statistical techniques for causal inference when experiments aren't possible
• Advanced SQL analytics combining temporal analysis with machine learning

🛠️ Technologies You'll Use:

• Snowflake's integrated Python environment with Snowpark
• Statistical modeling with statsmodels library
• Advanced SQL with date functions and complex joins
• Data visualization with matplotlib

📊 Key Analytical Insights:

• Quantify the immediate impact of customer support on spending
• Understand how intervention effects decay over time
• Learn to separate correlation from causation in business analytics

⏱️ Total Time: ~10-15 minutes

Setup Instructions

Step 1: Execute the Setup Script ⏱️ (~5 minutes)

1.1 Access Snowsight and Open Worksheets (or Workspaces)

##### Option A: Using Worksheets

1. Open Snowflake in your web browser (Snowsight interface) and log in to your account 2. Navigate to Projects → Worksheets in the left sidebar 3. Click + Worksheet to create a new SQL worksheet.

##### Option B: Using Workspaces

1. Open Snowflake in your web browser (Snowsight interface) and log in to your account 2. Navigate to Workspaces in the left sidebar 3. Click + Workspace to create a new workspace 4. Choose a workspace name (e.g., "Intervention Detection Demo") 5. Select your preferred workspace template or start with a blank workspace 6. Create a new SQL file within your workspace for the setup script

1.2 Import and Execute the Setup Script

1. Locate the scripts/setup.sql file from this repository 2. Open the file and copy all contents 3. Paste the SQL statements into your new Snowflake worksheet 4. Important: The script uses accountadmin role for initial setup 5. Execute all statements by clicking ▶ Run All

1.3 What the Setup Script Creates

The setup.sql script will automatically create:

🏗️ Infrastructure Components:

• Role: intervention_detection_role with appropriate permissions
• Warehouse: intervention_detection_wh (X-Small, auto-suspend after 60 seconds)
• Database: intervention_detection_retail
• Schema: intervention_detection_retail.analytics

📊 Data Tables:

• `purchases`: Customer purchase transactions with product details and pricing
• `support_tickets`: Customer support interactions linked to purchases

⚙️ Data Generation:

• Stored Procedures: Python-based procedures for generating realistic sample data
• Sample Data: 1,000 purchase records and 30 support tickets for analysis

🔐 Security Configuration:

• Proper role-based access controls and permissions for the demo environment

1.4 Verify Setup

The setup script includes verification commands at the end. You should see:

✅ Infrastructure Verification:

-- Database and schema confirmation
SHOW DATABASES LIKE 'INTERVENTION_DETECTION_RETAIL';
SHOW SCHEMAS IN DATABASE INTERVENTION_DETECTION_RETAIL;

-- Warehouse and role confirmation
SHOW WAREHOUSES LIKE 'INTERVENTION_DETECTION_WH';
SHOW ROLES LIKE 'INTERVENTION_DETECTION_ROLE';

✅ Table Structure Verification:

-- Table schema confirmation
DESCRIBE TABLE purchases;
DESCRIBE TABLE support_tickets;

✅ Data Generation Verification:

• 1,000 purchase records across 100 customers with 6 different products
• 30 support tickets linked to customer interactions
• Sample data preview showing realistic retail transactions and support cases

2️⃣ Step 2: Load and Execute the Demo Notebook ⏱️ (~5-10 minutes)

2.1 Access Snowflake Notebooks in Snowsight

1. In Snowsight, navigate to **Projects →...