Learn how to connect Meta Ads, GA4, and orders to see which conversions actually happened versus platform reports. We'll build the data foundation that enables agents to automatically surface insights, from ad attribution to customer lifetime value.
This playbook demonstrates how to build a modern Python data pipeline that extracts Meta Ads data to BigQuery, eliminating CSV exports and creating the warehouse foundation needed to reconcile platform-reported conversions with actual revenue across Meta, GA4, and sales data from a CRM.
This playbook extends the Meta Ads automation with AI image composition. We'll add a single LangGraph node that transforms product photos into marketing creatives using Gemini's vision model, then feeds these composed images into the existing campaign workflow.
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 shows how to use LangGraph workflows to generate campaign configs from simple inputs. We'll build a system that transforms product descriptions into complete campaign strategies with audience segments and ad variations, while preserving human approval before execution.
This playbook shows how to use configuration-driven campaign matrices to automate Meta Ads at scale. We'll build a system that transforms campaign configs into complete ad variations with targeting, budgets, and creatives, eliminating manual setup across dozens of ads.
In this tutorial, I will teach you LangChain as efficiently as possible by breaking down the framework into seven key components you need to understand to start developing more advanced LLM applications.
This tutorial will explore building dashboards using Large Language Models (LLMs) and LangChain. We will use LangChain chains to extract insights from BigQuery by first generating SQL and then pushing the generated SQL to BigQuery as views, forming the basis for interactive dashboards.
This guide helps data analysts, data scientists, and developers leverage LLMs for generating SQL queries from natural language questions, making complex data wrangling in BigQuery and other SQL databases more accessible and intuitive.
Learn how to connect Meta Ads, GA4, and orders to see which conversions actually happened versus platform reports. We'll build the data foundation that enables agents to automatically surface insights, from ad attribution to customer lifetime value.
This playbook demonstrates how to build a modern Python data pipeline that extracts Meta Ads data to BigQuery, eliminating CSV exports and creating the warehouse foundation needed to reconcile platform-reported conversions with actual revenue across Meta, GA4, and sales data from a CRM.
This playbook extends the Meta Ads automation with AI image composition. We'll add a single LangGraph node that transforms product photos into marketing creatives using Gemini's vision model, then feeds these composed images into the existing campaign workflow.
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.
In this playbook, we'll build a landing page performance analyzer that converts GA4 event data into conversion optimization strategies through intelligent SQL generation and AI-driven page-level insights
This playbook shows how to use LangGraph workflows to generate campaign configs from simple inputs. We'll build a system that transforms product descriptions into complete campaign strategies with audience segments and ad variations, while preserving human approval before execution.
