with prod_mon_sales as (
select product_id, unit_price,
month(order_date) as mon,
sum(quantity*unit_price) as mon_total_sales,
sum(quantity) as mon_total_quantity
from orders join products using(product_id)
where year(order_date) = 2023
group by product_id, month(order_date)
), 

rank_s as (
select product_id, 
if(customer_age <= 60, concat(round(customer_age, -1)-9, '-', round(customer_age, -1)), '61+') as customer_age_group,
rank() over(partition by product_id order by sum(quantity) desc, customer_age) as rk
from orders join customers using(customer_id)
where year(order_date) = 2023
group by product_id, customer_id
), 

most_popular_cust as (
select product_id, customer_age_group from rank_s where rk=1
)

select product_id, 
sum(mon_total_sales) as total_sales, 
unit_price,
round(sum(mon_total_quantity), 2) as total_quantity,
round(sum(mon_total_sales)/12, 2) as avg_monthly_sales,
round(max(mon_total_quantity), 2) as max_monthly_quantity,
customer_age_group
from prod_mon_sales join most_popular_cust using(product_id)
group by product_id, customer_age_group
order by total_sales desc, product_id

判断年龄范围那里其实还可以简化一下：

if(customer_age <= 60, concat(round(customer_age, -1)-9, '-', round(customer_age, -1)), '61+')