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Problem solution · SQL

Customer Purchasing Behavior Analysis

Customer Purchasing Behavior Analysis: a SQL solution using relational aggregation. Learn the idea, check the complexity, and read the full code, with credit to walkccc LeetCode Solutions.

Technique
Relational aggregation
Source
walkccc LeetCode Solutions
Length
37 lines
Start with the idea.

Try the problem first. If you get stuck, read the approach below, then write your own solution. The full code is at the bottom.

Approach

Relational aggregation

For Customer Purchasing Behavior Analysis, the query transforms and combines relational rows, then filters or aggregates them into the requested result.

  1. Identify the source rows and join keys.
  2. Apply filters before aggregation when possible.
  3. Group, rank, or project the final columns required by the result.

Code notes

  • 37 lines of SQL from the credited upstream file 3230.sql.
  • The implementation keeps its working state in language-native values and containers.
  • No explicit loop blocks detected.

Complexity

Review join cardinality, grouping keys, and available indexes when estimating query cost.

Check the problem constraints before deciding whether this complexity will pass.

Source

Code and credit

This code comes from walkccc LeetCode Solutions by P.-Y. Chen (walkccc) and is used under the MIT licence.

Open problem ↗Original source ↗
Full codeCustomer Purchasing Behavior Analysis · SQLSQL
Use this to learn the idea, then write your own version.
WITH  RankedCategoriesPerCustomer AS (    SELECT      Transactions.customer_id,      Products.category,      RANK() OVER(        PARTITION BY Transactions.customer_id        ORDER BY COUNT(Products.category) DESC,          MAX(Transactions.transaction_date) DESC      ) AS `rank`    FROM Transactions    INNER JOIN Products      USING (product_id)    GROUP BY 1, 2  ),  TransactionsMetadata AS (    SELECT      Transactions.customer_id,      ROUND(SUM(Transactions.amount), 2) AS total_amount,      COUNT(Transactions.transaction_id) AS transaction_count,      COUNT(DISTINCT Products.category) AS unique_categories,      ROUND(AVG(Transactions.amount), 2) AS avg_transaction_amount,      RankedCategoriesPerCustomer.category AS top_category    FROM Transactions    INNER JOIN Products      USING (product_id)    INNER JOIN RankedCategoriesPerCustomer      USING (customer_id)    WHERE RankedCategoriesPerCustomer.`rank` = 1    GROUP BY 1  )SELECT  *,  ROUND(transaction_count * 10 + total_amount / 100, 2) AS loyalty_scoreFROM TransactionsMetadataORDER BY loyalty_score DESC, customer_id;

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