This playbook demonstrates how to identify ecommerce products (SKUs) that correlate with higher customer lifetime value using statistical models, and then utilize that data to optimize acquisition campaigns with mathematically justified bid adjustments.
This playbook walks you through building an AI-powered CLV analysis system that reveals which customers drive long-term profitability. The LangGraph pipeline applies statistical models to predict behavior and delivers AI recommendations for optimization.
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
In this playbook, we will launch a personalization agent capable of generating revenue uplifts. The agent helps you map raw customer data to the recommended marketing experiences across email, website, and paid media.
This playbook will walk you through building a repeat purchase analytics library. We'll cover everything from writing SQL queries to creating a Python class for your analysis tools. We'll then create a suite of LangChain tools out of the class and let LLMs figure out how to use the different tools.
In this playbook, we'll implement a market basket recommender to find products that can be used for cross-selling. We'll then use this with the Next Product Recommender and LangChain to optimize CLV.
