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Snowflake for Developers/Guides/Intervention Detection for Retail Support
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Intervention Detection for Retail Support

Jim Warner, Cameron Shimmin
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Overview

It centralizes data preparation, statistical modeling, and visualization in Snowsight Notebooks with Snowpark, enabling teams to measure ROI without running randomized controlled trials.

Key Features

  • Causal Impact Measurement: Estimate the lift in customer spending following support interactions, isolating causation from correlation.
  • Counterfactual Analysis & Visualization: Generate and plot “what would have happened without intervention” using regression-based counterfactuals and confidence intervals.
  • End-to-End in Snowflake: Use SQL for data prep and Snowpark Python with statsmodels for modeling in a single Snowsight Notebook—no data movement.
  • Rapid, Reproducible Setup: A single setup.sql script creates roles, warehouse, database/schema, and loads realistic sample retail data.

How It Works

  • Environment Setup: Execute scripts/setup.sql to create the role, virtual warehouse, database/schema, and populate purchases and support_tickets via stored procedures.
  • Data Preparation: Join purchases to support tickets by customer; compute time deltas (days/weeks) around the intervention; aggregate into a modeling table (support_purchase_analysis) with features like WEEK, INTERVENTION, and INTERVENTION_WEEK.
  • Modeling (Snowpark Python): Pull the prepared table into a pandas DataFrame with Snowpark and fit an OLS model in statsmodels to estimate intervention effects.
  • Counterfactuals & Plots: Set treatment variables to zero to compute counterfactual predictions; visualize actual vs. predicted and counterfactual with a marked intervention point and 95% confidence bands using matplotlib.
  • Execution in Snowsight Notebooks: Import notebooks/0_start_here.ipynb, select the created database/schema and warehouse, add statsmodels and matplotlib, and run cells top to bottom.

Business Impact

  • Quantified Support ROI: Puts a measurable dollar value on support interactions with statistical confidence.
  • Faster, Defensible Decisions: Provides an RCT alternative for production settings where experiments are impractical.
  • Operational Visibility: Reveals effect size and decay over time to optimize staffing, channels, and follow-up strategy.
  • Platform Simplicity: Consolidates data, modeling, and visualization within Snowflake to reduce complexity and cost.

Use Cases and Applications

  • Retail & CPG: Measure the impact of service recovery, returns assistance, and concierge programs on subsequent spend.
  • Marketing & CX: Quantify lift from outreach after negative experiences; prioritize cohorts for proactive support.
  • Subscription/Technology: Assess how support or success calls influence retention, upsell, and expansion.
  • Operations & Strategy: Compare alternative support workflows or org changes using causal analysis instead of full RCTs.

Get Started

Updated 2026-04-28

This content is provided as is, and is not maintained on an ongoing basis. It may be out of date with current Snowflake instances