How to Analyze a Customer Base With AI

You'll discover which acquisition channels bring in customers who stick around, not just the ones that convert fastest. You'll learn to classify your customer base by absolute value, so you can invest heavily in keeping your best customers while using cost-effective tactics for everyone else.

The AI flow works by:

• Pulling transaction data from BigQuery with clean, structured queries
• Applying statistical modeling for behavior prediction
• Classifying customers into lifecycle stages based on value and engagement probability.
• Generating AI-powered strategic recommendations for channels and retention.
• Orchestrating the entire workflow using LangGraph

The approach combines proven statistical models—BG/NBD for predicting purchase behavior and Gamma-Gamma for monetary value—with AI-powered strategic recommendations. Instead of spending hours analyzing spreadsheets, you get clear guidance on which channels to scale, which to pause, and how to structure retention campaigns for maximum impact.

The system reveals insights that transform business strategy. Your highest-converting acquisition source might not be your most profitable. Your biggest customer lifecycle stage might be completely worthless. Your retention efforts might be targeting the wrong people entirely.

We'll use the same data as we did in the Tree of CLV LangGraph flow

Shopify Revenue Agent Uncovers Leaks with SQL Trees

This playbook walks you through building an AI Flow that dissects ecommerce revenue drivers across a customer base. The LangGraph-based system breaks down top-line metrics into their component parts, identifies root causes, and delivers prioritized recommendations to improve performance

RabbitmetricsBenjamin Falkeborg

Data Extraction from BigQuery

We'll pull both transactional history and customer metadata using structured SQL queries that ensure data quality from the start.