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While we do know that kale smoothies (and drinks overall) are not driving a lot of revenue, we don’t know why. A big part of data analysis is implementing your own metrics to get information out of the piles of data in your database.

In our case, the reason could be that no one likes kale, but it could be something else entirely. To find out, we’ll create a metric called reorder rate and see how that compares to the other products at SpeedySpoon.

We’ll define reorder rate as the ratio of the total number of orders to the number of people making those orders. A higher ratio means most of the orders are reorders. A lower ratio means more of the orders are first purchases.

### Instructions

1.

Let’s calculate the reorder ratio for all of SpeedySpoon’s products and take a look at the results. Counting the total orders per product is straightforward. We count the distinct `order_ids` in the `order_items` table.

Complete the query by passing in the `distinct` keyword and the `order_id` column name into the `count` function

``````select name, /**/
from order_items
group by 1
order by 1;``````

Here’s a hint on how to use the count function to count distinct columns in a table.

2.

Now we need the number of people making these orders.

To get that information, we need to join in the `orders` table and `count` unique values in the `delivered_to` field, and sort by the `reorder_rate`.

Complete the query below. The numerator should count the distinct `order_id`s. The denominator should count the distinct values of the `orders` table’s `delivered_to` field (`orders.delivered_to`).

``````select name, round(1.0 * count(/**/) /
count(/**/), 2) as reorder_rate
from order_items
join orders on
orders.id = order_items.order_id
group by 1
order by 2 desc;``````