3657. Find Loyal Customers

Description

Table: customer_transactions

+------------------+---------+
| Column Name      | Type    | 
+------------------+---------+
| transaction_id   | int     |
| customer_id      | int     |
| transaction_date | date    |
| amount           | decimal |
| transaction_type | varchar |
+------------------+---------+
transaction_id is the unique identifier for this table.
transaction_type can be either 'purchase' or 'refund'.

Write a solution to find loyal customers. A customer is considered loyal if they meet ALL the following criteria:

Made at least 3 purchase transactions.
Have been active for at least 30 days.
Their refund rate is less than 20% .

Return the result table ordered by customer_id in ascending order.

The result format is in the following example.

Example:

Input:

customer_transactions table:

+----------------+-------------+------------------+--------+------------------+
| transaction_id | customer_id | transaction_date | amount | transaction_type |
+----------------+-------------+------------------+--------+------------------+
| 1              | 101         | 2024-01-05       | 150.00 | purchase         |
| 2              | 101         | 2024-01-15       | 200.00 | purchase         |
| 3              | 101         | 2024-02-10       | 180.00 | purchase         |
| 4              | 101         | 2024-02-20       | 250.00 | purchase         |
| 5              | 102         | 2024-01-10       | 100.00 | purchase         |
| 6              | 102         | 2024-01-12       | 120.00 | purchase         |
| 7              | 102         | 2024-01-15       | 80.00  | refund           |
| 8              | 102         | 2024-01-18       | 90.00  | refund           |
| 9              | 102         | 2024-02-15       | 130.00 | purchase         |
| 10             | 103         | 2024-01-01       | 500.00 | purchase         |
| 11             | 103         | 2024-01-02       | 450.00 | purchase         |
| 12             | 103         | 2024-01-03       | 400.00 | purchase         |
| 13             | 104         | 2024-01-01       | 200.00 | purchase         |
| 14             | 104         | 2024-02-01       | 250.00 | purchase         |
| 15             | 104         | 2024-02-15       | 300.00 | purchase         |
| 16             | 104         | 2024-03-01       | 350.00 | purchase         |
| 17             | 104         | 2024-03-10       | 280.00 | purchase         |
| 18             | 104         | 2024-03-15       | 100.00 | refund           |
+----------------+-------------+------------------+--------+------------------+

Output:

+-------------+
| customer_id |
+-------------+
| 101         |
| 104         |
+-------------+

Explanation:

Customer 101:
- Purchase transactions: 4 (IDs: 1, 2, 3, 4)
- Refund transactions: 0
- Refund rate: 0/4 = 0% (less than 20%)
- Active period: Jan 5 to Feb 20 = 46 days (at least 30 days)
- Qualifies as loyal
Customer 102:
- Purchase transactions: 3 (IDs: 5, 6, 9)
- Refund transactions: 2 (IDs: 7, 8)
- Refund rate: 2/5 = 40% (exceeds 20%)
- Not loyal
Customer 103:
- Purchase transactions: 3 (IDs: 10, 11, 12)
- Refund transactions: 0
- Refund rate: 0/3 = 0% (less than 20%)
- Active period: Jan 1 to Jan 3 = 2 days (less than 30 days)
- Not loyal
Customer 104:
- Purchase transactions: 5 (IDs: 13, 14, 15, 16, 17)
- Refund transactions: 1 (ID: 18)
- Refund rate: 1/6 = 16.67% (less than 20%)
- Active period: Jan 1 to Mar 15 = 73 days (at least 30 days)
- Qualifies as loyal

The result table is ordered by customer_id in ascending order.

Solutions

Solution 1

MySQLPython3

# Write your MySQL query statement below
SELECT customer_id
FROM customer_transactions
GROUP BY 1
HAVING
    COUNT(1) >= 3
    AND SUM(transaction_type = 'refund') / COUNT(1) < 0.2
    AND DATEDIFF(MAX(transaction_date), MIN(transaction_date)) >= 30
ORDER BY 1;

import pandas as pd


def find_loyal_customers(customer_transactions: pd.DataFrame) -> pd.DataFrame:
    customer_transactions["transaction_date"] = pd.to_datetime(
        customer_transactions["transaction_date"]
    )
    grouped = customer_transactions.groupby("customer_id")
    agg_df = grouped.agg(
        total_transactions=("transaction_type", "size"),
        refund_count=("transaction_type", lambda x: (x == "refund").sum()),
        min_date=("transaction_date", "min"),
        max_date=("transaction_date", "max"),
    ).reset_index()
    agg_df["date_diff"] = (agg_df["max_date"] - agg_df["min_date"]).dt.days
    agg_df["refund_ratio"] = agg_df["refund_count"] / agg_df["total_transactions"]
    result = (
        agg_df[
            (agg_df["total_transactions"] >= 3)
            & (agg_df["refund_ratio"] < 0.2)
            & (agg_df["date_diff"] >= 30)
        ][["customer_id"]]
        .sort_values("customer_id")
        .reset_index(drop=True)
    )
    return result

3657. Find Loyal Customers

Description

Solutions

Solution 1

Comments