New Integration
Google BigQuery
You
Thursday 11:41am
Why is revenue down?
ASSISANT
Thursday 11:44am
There are many things you can do, looking at the MoM trends of the Revenue Dashboard will help to see exactly how the revenue has decreased, or we could directly query the revenue data to pull out revenue split by different demographics such as country.
Revenue Dashboard - Month on Month Revenue
Chart showing the number of distinct orders placed in the last week, excluding canceled and returned orders...
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Rewrite using our definition of @Revenue
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// Query to Execute Count Orders Last Week
SELECT
DATE_TRUNC(DATE(oi.created_at), WEEK(MONDAY)) AS week_start_date,
COUNT(DISTINCT oi.order_id) AS number_of_orders
FROM `bigquery-public-data.thelook_ecommerce.order_items` AS oi -- Joining on order_items to pull out order_id
WHERE oi.status NOT IN ('Cancelled', 'Returned') -- Filtering out cancelled and returned items
AND DATE(oi.created_at) >= DATE_SUB(
DATE_TRUNC(CURRENT_DATE(), WEEK(MONDAY)),
INTERVAL 1 WEEK
)
AND DATE(oi.created_at) < DATE_TRUNC(CURRENT_DATE(), WEEK(MONDAY))
GROUP BY 1
ORDER BY 1 DESC
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Column Name
customer_id
description
featured_from
featured_to
Plan
Released At
released_at
Created At
created_at
Description
Customer ID labelled from sales team
Description of this customer from the sales/marketing team
The time at which the customer was officially engaged with us
The time at which the customer officially stopped engaging
The plan the customer is engaged with
When this badge was viewable within the student’s class
community
The time when this customer was created
Data Type
Integer
Text
Datetime
Datetime
Multi Select
Datetime
Datetime
Example
“15”
“Customer is on ABC plan”
“04-11-2023T18:06:24.134353”
“04-11-2023T18:06:24.134353”
“Premium”
“04-11-2023T18:06:24.134353”
“04-11-2023T18:06:24.134353”
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