Cross-Selling and Re-Selling with LangChain

Ecommerce businesses are constantly seeking to boost CLV. The two key strategies for this are re-selling and cross-selling.

• Re-Selling: This involves encouraging customers to purchase products they've bought before or similar items. By analyzing past purchase behavior, businesses can tailor re-marketing efforts to individual preferences, increasing the likelihood of repeat purchases.
• Cross-Selling: This strategy recommends related or complementary products to customers based on their current selections. By understanding the relationships between products, businesses can suggest items that enhance or complement the customer's initial purchase, thereby increasing the average order value.

Using Market Basket Analysis for Cross-Selling Opportunities

One effective tool for identifying cross-selling opportunities is the market basket analysis. This analytical technique examines transaction data to uncover patterns of frequently purchased products. By understanding product associations, businesses can make informed customer recommendations and increase sales.

For example, if data shows that customers who buy running shoes often purchase running socks, the market basket analysis can suggest these items to someone adding a laptop to their cart. This increases the chances of additional sales and provides convenience to the customer by anticipating their needs.

How It Works

Market basket analysis operates through several key steps:

• Data Collection: The system collects transaction data, noting which products are purchased together in the same transaction. This data forms the basis for identifying relationships between products.
• Pattern Analysis: Algorithms are applied to the transaction data to find associations between different products. Standard techniques include association rule learning, which identifies rules like "If a customer buys product A, they are likely to buy product B."
• Recommendation Generation: Based on these patterns, the system predicts and recommends products a customer might be interested in. These recommendations can be presented in real-time as customers shop, enhancing cross-selling effectiveness.

Combining Two Key Components

When we are done writing the code for the market basket analysis tool, we have everything we need to combine two different types of recommenders

1. Market Basket Recommender Class (BasketRecommender): This class handles the market basket analysis, identifying products commonly purchased together. It processes transactional data to uncover product associations and suggests items that complement the customer's selection.
2. Next Product Recommender Class (NextProductRecommender): This class utilizes collaborative filtering to recommend products based on a customer's previous purchases. Analyzing the purchase history of similar customers can predict products that the customer might be interested in buying next.

Using both recommenders allows us to build feed LLMs with the customer context to make suitable suggestions for cross-selling and re-selling.

SQL and LLMs over ML

Combining simple SQL-based techniques with large language models (LLMs) is an efficient alternative to complex machine learning (ML) models. This approach leverages both technologies' strengths, creating an accessible and powerful recommendation system without the overhead of traditional ML pipelines.

SQL, or Structured Query Language, is a standard language for managing and querying relational databases. It is straightforward to write and interpret, allowing businesses to extract valuable insights from transactional data. Using SQL, you can perform analyses like market basket analysis to uncover patterns of products frequently purchased together, identify best-selling items, and understand customer purchasing behavior—all without intricate ML algorithms.

LLMs, such as OpenAIs o1-preview , can interpret complex data outputs and translate them into coherent, conversational recommendations. By employing high-IQ LLMs, businesses can create natural language interactions and personalized suggestions without requiring extensive training data or specialized ML development.

The playbook comprises solution architecture and full library code i Colab. We'll work with real Shopify data, making our solution practical and ready to use in real-world scenarios.

Data and Code