Given a dataset of customer transactions including `customer_id`, `product_id`, `transaction_date`, and `revenue`, write a SQL query to identify the top 5 customers by total revenue for each month in 2023. The output should include `month`, `customer_id`, and `total_revenue`.

Utilize a CTE-based approach for clarity and modularity. First, extract the month from transaction_date and calculate monthly_revenue per customer_id using GROUP BY. Second, apply the RANK() window function partitioned by month and ordered by monthly_revenue in descending order to assign a rank to each customer within their respective month. Finally, filter the results to include only customers with a rank of 5 or less, ensuring the output includes month, customer_id, and total_revenue for 2023. This MECE approach ensures all relevant data is processed and filtered efficiently.

WITH MonthlyCustomerRevenue AS (
    SELECT
        EXTRACT(MONTH FROM transaction_date) AS month,
        customer_id,
        SUM(revenue) AS total_revenue
    FROM
        customer_transactions
    WHERE
        EXTRACT(YEAR FROM transaction_date) = 2023
    GROUP BY
        EXTRACT(MONTH FROM transaction_date),
        customer_id
),
RankedCustomerRevenue AS (
    SELECT
        month,
        customer_id,
        total_revenue,
        RANK() OVER (PARTITION BY month ORDER BY total_revenue DESC) AS rnk
    FROM
        MonthlyCustomerRevenue
)
SELECT
    month,
    customer_id,
    total_revenue
FROM
    RankedCustomerRevenue
WHERE
    rnk <= 5
ORDER BY
    month, total_revenue DESC;