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.
Product Analytics is an essential tool for businesses aiming to understand their customers and optimize their offerings. By analyzing how and when products are purchased, companies can uncover valuable insights that drive sales and CLV. However, many ecommerce businesses struggle to analyze their product and order data.
The volume of data can be overwhelming, and extracting actionable insights takes time and requires SQL skills. As a result, valuable information is often left untapped. However, by utilizing LLM agents equipped with access to SQL-based tools, developers and data scientists can automate the analyses. This playbook will guide you through building such an agent.
We'll cover the following:
Developing Analytical Tools: We'll start by creating a BasketSizeAnalyzer to dissect shopping basket data and reveal patterns in customer purchasing behavior. We'll use the BasketSizeAnalyzer in combination with the RepeatPurchaseAnalyzer that we have developed in another playbook.
Generating Product Analytics Reports: Present analytical results effectively, turning raw data into actionable insights through detailed reports.
Build LLM-Powered ReAct Agents with LangGraph: We'll introduce the LangGraph library to construct ReAct agents capable of utilizing our SQL-based analytical tools to answer business questions.
In a previous playbook, we developed a repeat purchase analytics library. To complement this, we'll now see how to analyze basket sizes. Basket size analysis provides invaluable insights into how customers shop, what products they tend to buy together, and what factors influence the size and value of their purchases.
This will be the fourth playbook in which we will develop the infrastructure for SQL-based product analytics using LangChain, LangGraph, and LLMs. In this playbook, our primary focus will be to set up a helpful product analytics ReAct agent with LangGraph.
The agent can be deployed on the Google Cloud Platform and automate the extraction of insights from Shopify order data by exporting the insights to Looker Studio. We will cover how to deploy the agents and create reports in Looker Studio with actionable insights in upcoming playbooks.
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