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.
What if you could compress months of data analysis into minutes, with better results? Let's explore how agents can transform data into a value-generation engine, delivering prioritized business recommendations and immediate ROI.
This playbook walks you through building a cost-aware SQL agent that delivers actionable insights from BigQuery. The agent helps data teams democratize access to analytics and avoid unexpected costs through validation and a built-in protection mechanism.
This playbook walks you through building an ecommerce product analytics agent using BigQuery and LangGraph. The agent helps identify opportunities to increase average order value through product combinations, category analysis, and bundle recommendations.
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.
