I have a table called orders.
Link for the table here:
table
I want to get the total users' first order in each region, each day.
First, I tried to get: the first order for each unique user by doing this:
SELECT customer_id,
MIN(order_date) first_buy,
region
FROM orders
GROUP BY 1
ORDER BY 2, 1;
This resulted with:
customer_id, first_buy, region
BD-11500, 2017-01-02, Central
DB-13060, 2017-01-03, West
GW-14605, 2017-01-03, West
HR-14770, 2017-01-03, West
SC-20380, 2017-01-03, West
VF-21715, 2017-01-03, Central
And so on.
You can see there are 4 unique users on 2017-01-03 in West.
I want to get this result:
first_buy, region, count_user
2017-01-02, Central, 1
2017-01-03, West, 4
2017-01-03, Central, 1
I haven't tested this but I think this will give what you wanting to achieve
SELECT first_buy, region, COUNT(customer_id) AS count_user
FROM (SELECT customer_id, MIN(order_date) first_buy, region
FROM orders
GROUP BY customer_id) AS t
GROUP BY first_buy, region
Try this:
SELECT
first_buy = (SELECT MIN(order_date) FROM orders WHERE orders.region = ord.region),
ord.region,
count_user = ISNULL((SELECT COUNT(*) FROM orders WHERE orders.region = ord.region GROUP BY orders.customer_id), 0)
FROM orders ord
GROUP BY ord.region
Related
I have this sample dataset and would like to display customer_name, region, and order date.
The issue with my dataset is that the customer has multiple regions and multiple order dates. I would like to see only the customer name, first region per order (If the first order was in US East then US East ?), and all order date
Customer 1, US East, 2021-12-10
Customer 1, US West, 2022-07-26
result>
Customer 1, US East, 2021-12-10
Customer 1, US East, 2022-07-26
select
'Customer 1','US East', '2021-12-10'
union
select
'Customer 1','US West', '2022-07-26'
union
select
'Customer 2','Europe West', '2021-01-26'
union
select
'Customer 2','Europe', '2020-01-26'
Using FIRST_VALUE:
SELECT Customer, FIRST_VALUE(Region) OVER(PARTITION BY Customer
ORDER BY date) AS region
,Date
FROM tab
I have to calculate the avg of gross revenue on bigquery (the key is item_id).
SELECT
t0.order_create_date AS day,
t0.site_country AS country,
p0.product_brand AS brand,
p0.product_gender AS gender,
p0.product_department AS department,
t0.item_qty AS items_sold,
t0.item_sale_price AS gross_revenue,
t0.item_net_price AS net_revenue,
FROM
`transactions` t0
LEFT JOIN
`products` p0
ON
t0.item_id = p0.item_id
ORDER BY
country,
day ASC
I tried this :
SELECT
t0.order_create_date AS day,
t0.site_country AS country,
p0.product_brand AS brand,
p0.product_gender AS gender,
p0.product_department AS department,
t0.item_qty AS items_sold,
t0.item_sale_price AS gross_revenue,
AVG(t0.item_sale_price) AS average_value,
t0.item_net_price AS net_revenue,
FROM
`transactions` t0
LEFT JOIN
`products` p0
ON
t0.item_id = p0.item_id
ORDER BY
country,
day ASC
Biquery result:
SELECT list expression references t0.order_create_date which is neither grouped nor aggregated at [2:3]
The problem is that you didn't aggregate or by all the other columns, except the average_value one. Here you can read more about Group By.
From the names of the columns you are creating, I suppose you also want to have other information such as gross and net revenue. You would have to use some aggregate function on them too, otherwise the error would continue.
Something like the following should probably work:
SELECT
t0.order_create_date AS day,
t0.site_country AS country,
p0.product_brand AS brand,
p0.product_gender AS gender,
p0.product_department AS department,
sum(t0.item_qty) AS items_sold,
sum(t0.item_sale_price) AS gross_revenue,
AVG(t0.item_sale_price) AS average_value,
sum(t0.item_net_price) AS net_revenue,
FROM
transactions t0
LEFT JOIN
products p0
ON
t0.item_id = p0.item_id
GROUP BY
day,
country,
brand,
gender,
department
ORDER BY
country,
day ASC
I have a sample DB below. I'm looking to see how many TV and Internet bundles we sold. In the sample data, only Bob and Trevor sold that bundle so we sold 2.
How do I write the query for the number of bundles sold by each Sales rep and the total price of the bundles sold?
Thanks
I imagine that, for a bundle to happen, the same sales person needs to have sold both products to the same customer.
I would approach this with two levels of aggregation. First group by sales person and customer in a subquery to identify the bundles, then, in an outer query, count how many such bundles happened for each sales person:
SELECT sales_person, COUNT(*) bundles_sold, SUM(total_price) total_price
FROM (
SELECT sales_person, customer_name, SUM(total_price) total_price
FROM mytable
WHERE product_name in ('TV', 'Phone')
GROUP BY sales_person
HAVING COUNT(DISTINCT product_name) = 2
) x
You can simply group the salesman's by counting the distinct products they sold -
SELECT Sales_Person, FLOOR(COUNT(DISTINCT product_name)/2) NO_OF_BUNDLES, sum(total_price)
FROM YOUR_TAB
WHERE product_name IN ('TV', 'Internet')
GROUP BY Sales_Person
HAVING COUNT(DISTINCT product_name) >= 2
Using cte as below:
with cte1(sales_person, customer_name, product_count) as
(
select sales_person, customer_name, count(product_name)
from sales
where product_name in ('TV', 'Internet')
group by sales_person, customer_name
having count(product_name) = 2
)
select sales_person, count(product_count)
from cte1
group by sales_person
I would suggest two levels of aggregation:
select sales_person, count(*), sum(total_price)
from (select sales_person, customer_name,
sum(total_price) as total_price,
max(case when product_name = 'tv' then 1 else 0 end) as has_tv,
max(case when product_name = 'phone' then 1 else 0 end) as has_phone,
max(case when product_name = 'internet' then 1 else 0 end) as has_internet
from t
group by sales_person, customer_name
) sc
where has_phone = 0 and
has_tv = 1 and
has_internet = 1
group by sales_person;
I recommend this structure because it is pretty easy to change the conditions in the where clause to return this for any bundle -- or even to aggregate by the three flags and return the totals for all bundles in one query.
Transaction -> Transaction_id, buyer_id, seller_id, object_id,Shipping_id, Price, Quantity, site_id,transaction_date, expected_delivery_date, check_out_status
leaf_category_id, defect_id
Buyer -> Buyer_id, name, country
Seller -> Seller_id, name, country, segment, standard
Listing -> object_id, seller_id, auction_start_date
auction_end_date, listing_site_id, leaf_category_id
quantity
For the sellers from UK who transacted on the second week of december(6 December 2015 to 12 December 2015), find the number of sellers
who have atleast twice the total transaction amount (qty*price) in the following week.
I have tried below query to get sellers who transacted in dec 2nd week but facing error when calculating sellers having twice the transaction amount from those sellers in following week.
With trans_dec_uk as
(
select s.seller_id,t.transaction_date, sum(t.Qty * Price) trans_amount
from transaction t join seller s
on t.seller_id =s.seller_id
where s.country ='UK'
and t.transaction_date between '12-05-2015' and '12-18-2015'
group by s.seller_id,t.transaction_date
)
select count(seller) from trans_dec_uk
where trans_amount = 2 * to_char(sysdate+7,'DD-MM')
with uk_sellers as (
select * from <dataset>.Seller where country = 'UK'
),
first_week_uk as (
select seller_id, sum(Price*Quantity) as first_week_total
from <dataset>.Transaction
inner join uk_sellers using(seller_id)
where transaction_date between '2015-12-05' and '2015-12-11'
group by 1
),
second_week_uk as (
select seller_id, sum(Price*Quantity) as second_week_total
from <dataset>.Transaction
inner join uk_sellers using(seller_id)
where transaction_date between '2015-12-12' and '2015-12-18'
group by 1
)
select count(distinct seller_id) as the_answer
from first_week_uk
inner join second_week_uk using(seller_id)
where second_week_total >= 2*first_week_total
I want to create a chart that will represent the performance of some stores.
The x axis will represent the total revenue of the store over the average revenue of all the stores.
And the y axis will represent the average frequency the customers of a specific store visit this store over the average frequency all the customers visit all the stores.
This is what I have tried :
select
distinct [Order].shop_id
, SUM(total) /((select AVG(o.shopTotal)
from
(
select
distinct shop_id , SUM(total) as shopTotal
from [Order]
group by shop_id
)o)*0.1) as revPerAvgRev
,COUNT(distinct orderno) /((select AVG(orders)
from
(
select
distinct shop_id , room, COUNT(distinct orderno) as orders
from [Order]
group by shop_id , room
)o)*0.1) as freqPerAvgFreq
from [Order]
group by [order].shop_id
order by revPerAvgRev desc
select distinct shop_id ,room , count(distinct orderno)
from [Order]
group by shop_id , room
I believe the average frequency that the customers visit the stores is ok.
But I cannot manage to calculate correclty the average frequency each customer visit the store.
Note: Each customer only visits one store.
It is a little difficult to follow exactly what you want. You ask for frequencies but the code sample is showing revenue. You can get the proportion of revenue by using a window function to calculate the total:
select shop_id, shopTotal,
cast(shopTotal as float)/ sum(shopTotal) over () as Revenue,ProportionOfTotal,
numvisits,
cast(numvisits as float) / sum(numvisits) over () as VisitsProportionOfTotal
from (select shop_id , SUM(total) as shopTotal, count(*) as numvisits
from [Order]
group by shop_id
) s