Repeat purchase analysis is essential for understanding customer behavior and driving CLV in ecommerce. It provides valuable insights into customer loyalty, product performance, and overall business health.

This will be the third playbook in a series of four, in which we will develop the infrastructure for SQL-based product analytics using LangChain, LangGraph, and LLMs. In this playbook, we will build more tools for activating and retaining customers. We'll develop a repeat purchase analytics class, turn the class methods into tools with LangChain, and see how LLMs use multiple tools.

The Business Problem

The importance of repeat purchases cannot be overstated. Firstly, customer retention is generally more cost-effective than acquisition. By encouraging repeat purchases, businesses can maximize their return on investment in customer acquisition. Secondly, repeat customers often spend more and are more likely to try new products, contributing to increased revenue and potentially expanding your product reach.

1-time buyers: by far the largest customer segment

Around 80% of customers don't make repeat purchases, an expensive missed opportunity to increase customer lifetime value (CLV). But here's the good news—if you can get a customer to make a second purchase, you significantly increase the likelihood of subsequent purchases. It's like a snowball effect on CLV.

So why is repeat purchase analysis so critical? Let's break it down:

  1. It's cheaper to keep customers than to find new ones. Some studies suggest it can cost five times as much to attract a new customer than to keep an existing one.
  2. Repeat customers spend more. They're already familiar with your brand and products, making them more likely to buy again and try new things.
  3. It helps you make smarter decisions. By understanding what makes customers return, you can improve everything from your product lineup to your marketing campaigns.

Repeat purchase analysis isn't just about tracking who comes back. It's about uncovering patterns in customer behavior that can guide your activation and retention strategy. You can find out:

  • Which products keep customers coming back
  • What types of customers are most likely to buy again
  • What combinations of products turn one-time buyers into loyal customers

This information can transform your marketing strategy. Instead of using a one-size-fits-all approach, you can make data-driven decisions that work. For example:

  • You can focus your advertising on products that have high repeat purchase rates.
  • You can design welcome emails that guide new customers towards products that often lead to repeat purchases.
  • You can create personalized recommendations based on what similar customers have bought.

The second purchase is essential. It's often seen as a tipping point in customer loyalty. If you can get customers to buy a second time, they're much more likely to become long-term, high-value customers.

By analyzing the patterns of customers who make that crucial second purchase, you can spot the factors that encourage this behavior. Maybe it's certain products, price points, or the timing of your follow-up emails. Once you know what works, you can create targeted campaigns to nudge first-time buyers toward that all-important second purchase.

We'll work with real Shopify data, making our solution practical and ready to use in real-world scenarios.

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By the end of this playbook, you'll have a robust set of repeat purchase analytics tools to boost your customer retention and increase your customer lifetime value. Let's get started!

Data and Code

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