Questions
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What is the difference in forecasted sales for sales rebate account for year 2022 and 2023
|
SELECT
[account name],year(Date),
sum(isnull(sales_forecast,0)) - LAG(sum(isnull(sales_forecast,0))) OVER (PARTITION BY [account name] ORDER BY year(Date)) AS sales_forecastDifference
FROM
forecasted_table
where [account name] like '%sales rebate%' and year(date) = 2023
group by [account name],year(Date)
ORDER BY
[account name],year(Date)
|
What is the difference in forecasted sales for entity ABC for year 2022 and 2023
|
SELECT
[entity],year(Date),
sum(isnull(sales_forecast,0)) - LAG(sum(isnull(sales_forecast,0))) OVER (PARTITION BY [entity] ORDER BY year(Date)) AS sales_forecastDifference
FROM
forecasted_table
where [entity] = 'ABC' and year(date) = 2023
group by [entity],year(Date)
ORDER BY
[account name],year(Date)
|
find the percentage change in forecasted sales compared to the previous year for each Entity?
|
SELECT
entity, year(Date),
case when LAG(sum(isnull(sales_forecast,0))) OVER (PARTITION BY entity ORDER BY year(Date)) = 0 then null
else decimal(((sum(isnull(sales_forecast,0)) - LAG(sum(isnull(sales_forecast,0))) OVER (PARTITION BY entity ORDER BY year(Date))) /LAG(sum(isnull(sales_forecast,0))) OVER (PARTITION BY entity
ORDER BY year(Date)))*100),2) end as sales_forecast_growth
FROM
forecasted_table
group by entity, year(Date)
ORDER BY
entity, year(Date)
|
For each department, find the percentage change in forecasted sales compared to the previous year?
|
SELECT
[department name],year(Date),
case when LAG(sum(isnull(sales_forecast,0))) OVER (PARTITION BY [department name] ORDER BY year(Date)) = 0 then null
else decimal(((sum(isnull(sales_forecast,0)) - LAG(sum(isnull(sales_forecast,0))) OVER (PARTITION BY [department name] ORDER BY year(Date))) /LAG(sum(isnull(sales_forecast,0))) OVER (PARTITION BY [department name]
ORDER BY year(Date)))*100),2) end as sales_forecast_growth
FROM
forecasted_table
group by [department name],year(Date)
ORDER BY
[department name],year(Date)
|
For each location, find the percentage change in forecasted sales compared to the previous year?
|
SELECT
[location],year(Date),
case when LAG(sum(isnull(sales_forecast,0))) OVER (PARTITION BY [location] ORDER BY year(Date)) = 0 then null
else decimal(((sum(isnull(sales_forecast,0)) - LAG(sum(isnull(sales_forecast,0))) OVER (PARTITION BY [ location] ORDER BY year(Date))) /LAG(sum(isnull(sales_forecast,0))) OVER (PARTITION BY [location]
ORDER BY year(Date)))*100),2) end as sales_forecast_growth
FROM
forecasted_table
group by [location],year(Date)
ORDER BY
[location],year(Date)
|
For each geography, find the percentage change in forecasted sales compared to the previous year?
|
SELECT
[geography],year(Date),
case when LAG(sum(isnull(sales_forecast,0))) OVER (PARTITION BY [geography] ORDER BY year(Date)) = 0 then null
else decimal(((sum(isnull(sales_forecast,0)) - LAG(sum(isnull(sales_forecast,0))) OVER (PARTITION BY [ geography] ORDER BY year(Date))) /LAG(sum(isnull(sales_forecast,0))) OVER (PARTITION BY [geography]
ORDER BY year(Date)))*100),2) end as sales_forecast_growth
FROM
forecasted_table
group by [geography],year(Date)
ORDER BY
[geography],year(Date)
|
Which Department is expected to see the most significant growth in the 2023?
|
SELECT top 1
[department name],year(Date),
case when LAG(sum(isnull(sales_forecast,0))) OVER (PARTITION BY [department name] ORDER BY year(Date)) = 0 then null
else decimal(((sum(isnull(sales_forecast,0)) - LAG(sum(isnull(sales_forecast,0))) OVER (PARTITION BY [department name] ORDER BY year(Date))) /LAG(sum(isnull(sales_forecast,0))) OVER (PARTITION BY [department name]
ORDER BY year(Date)))*100),2) end as sales_forecast_growth
FROM
forecasted_table
where year(date) = 2023
group by [department name],year(Date)
ORDER BY
sales_forecast_growth desc
|
Which Account is expected to see the most significant growth in sales in the 2023?
|
SELECT top 1
[account name],year(Date),
case when LAG(sum(isnull(sales_forecast,0))) OVER (PARTITION BY [account name] ORDER BY year(Date)) = 0 then null
else decimal(((sum(isnull(sales_forecast,0)) - LAG(sum(isnull(sales_forecast,0))) OVER (PARTITION BY [account name] ORDER BY year(Date))) /LAG(sum(isnull(sales_forecast,0))) OVER (PARTITION BY [account name]
ORDER BY year(Date)))*100),2) end as sales_forecast_growth
FROM
forecasted_table
where year(date) = 2023
group by [account name],year(Date)
ORDER BY
sales_forecast_growth desc
|
Which Entity is expected to see the most significant growth in sales in the 2023?
|
SELECT top 1
[entity],year(Date),
case when LAG(sum(isnull(sales_forecast,0))) OVER (PARTITION BY [entity] ORDER BY year(Date)) = 0 then null
else decimal(((sum(isnull(sales_forecast,0)) - LAG(sum(isnull(sales_forecast,0))) OVER (PARTITION BY [entity] ORDER BY year(Date))) /LAG(sum(isnull(sales_forecast,0))) OVER (PARTITION BY [entity]
ORDER BY year(Date)))*100),2) end as sales_forecast_growth
FROM
forecasted_table
where year(date) = 2023
group by [entity],year(Date)
ORDER BY
sales_forecast_growth desc
|
Which Location is expected to see the most significant growth in sales in the 2023?
|
SELECT top 1
[location],year(Date),
case when LAG(sum(isnull(sales_forecast,0))) OVER (PARTITION BY [location] ORDER BY year(Date)) = 0 then null
else decimal(((sum(isnull(sales_forecast,0)) - LAG(sum(isnull(sales_forecast,0))) OVER (PARTITION BY [location] ORDER BY year(Date))) /LAG(sum(isnull(sales_forecast,0))) OVER (PARTITION BY [location]
ORDER BY year(Date)))*100),2) end as sales_forecast_growth
FROM
forecasted_table
where year(date) = 2023
group by [location],year(Date)
ORDER BY
sales_forecast_growth desc
|
How many departments are projected to have sales greater than 1 million in 2022?
|
select count(*) from (select [department name], sum(isnull(sales_forecast,0)) from forecasted_table where year(Date) = 2022 group by [department name] having sum(isnull(sales_forecast,0)) > 1000000)
|
How many locations are projected to have sales greater than 1 million in 2022?
|
select count(*) from (select [location], sum(isnull(sales_forecast,0)) from forecasted_table where year(Date) = 2022 group by [location] having sum(isnull(sales_forecast,0)) > 1000000)
|
How many accounts are projected to have sales greater than 1 million in 2022?
|
select count(*) from (select [account name], sum(isnull(sales_forecast,0)) from forecasted_table where year(Date) = 2022 group by [account name] having sum(isnull(sales_forecast,0)) > 1000000)
|
Top 5 accounts in 2023 having highest growth rate of forecasted sales?
|
SELECT top 5
[account name],year(Date),
case when LAG(sum(isnull(sales_forecast,0))) OVER (PARTITION BY [account name] ORDER BY year(Date)) = 0 then null
else decimal(((sum(isnull(sales_forecast,0)) - LAG(sum(isnull(sales_forecast,0))) OVER (PARTITION BY [account name] ORDER BY year(Date))) /LAG(sum(isnull(sales_forecast,0))) OVER (PARTITION BY [account name]
ORDER BY year(Date)))*100),2) end as sales_forecast_growth
FROM
forecasted_table
where year(date) = 2023
group by [account name],year(Date)
ORDER BY
sales_forecast_growth desc
|
Top 5 departments in 2023 having highest growth rate of forecasted sales?
|
SELECT top 5
[department name],year(Date),
case when LAG(sum(isnull(sales_forecast,0))) OVER (PARTITION BY [department name] ORDER BY year(Date)) = 0 then null
else decimal(((sum(isnull(sales_forecast,0)) - LAG(sum(isnull(sales_forecast,0))) OVER (PARTITION BY [department name] ORDER BY year(Date))) /LAG(sum(isnull(sales_forecast,0))) OVER (PARTITION BY [department name]
ORDER BY year(Date)))*100),2) end as sales_forecast_growth
FROM
forecasted_table
where year(date) = 2023
group by [department name],year(Date)
ORDER BY
sales_forecast_growth desc
|
Top 5 location in 2023 having highest growth rate of forecasted sales?
|
SELECT top 5
[location],year(Date),
case when LAG(sum(isnull(sales_forecast,0))) OVER (PARTITION BY [location] ORDER BY year(Date)) = 0 then null
else decimal(((sum(isnull(sales_forecast,0)) - LAG(sum(isnull(sales_forecast,0))) OVER (PARTITION BY [location] ORDER BY year(Date))) /LAG(sum(isnull(sales_forecast,0))) OVER (PARTITION BY [location]
ORDER BY year(Date)))*100),2) end as sales_forecast_growth
FROM
forecasted_table
where year(date) = 2023
group by [location],year(Date)
ORDER BY
sales_forecast_growth desc
|
Top 5 geography in 2023 having highest growth rate of forecasted sales?
|
SELECT top 5
[geography],year(Date),
case when LAG(sum(isnull(sales_forecast,0))) OVER (PARTITION BY [geography] ORDER BY year(Date)) = 0 then null
else decimal(((sum(isnull(sales_forecast,0)) - LAG(sum(isnull(sales_forecast,0))) OVER (PARTITION BY [geography] ORDER BY year(Date))) /LAG(sum(isnull(sales_forecast,0))) OVER (PARTITION BY [geography]
ORDER BY year(Date)))*100),2) end as sales_forecast_growth
FROM
forecasted_table
where year(date) = 2023
group by [geography],year(Date)
ORDER BY
sales_forecast_growth desc
|
Top 5 entity in 2023 having highest growth rate of forecasted sales?
|
SELECT top 5
[entity],year(Date),
case when LAG(sum(isnull(sales_forecast,0))) OVER (PARTITION BY [entity] ORDER BY year(Date)) = 0 then null
else decimal(((sum(isnull(sales_forecast,0)) - LAG(sum(isnull(sales_forecast,0))) OVER (PARTITION BY [entity] ORDER BY year(Date))) /LAG(sum(isnull(sales_forecast,0))) OVER (PARTITION BY [entity]
ORDER BY year(Date)))*100),2) end as sales_forecast_growth
FROM
forecasted_table
where year(date) = 2023
group by [entity],year(Date)
ORDER BY
sales_forecast_growth desc
|
Which Department is expected to see the least significant growth in the 2023?
|
SELECT top 1
[department name],year(Date),
case when LAG(sum(isnull(sales_forecast,0))) OVER (PARTITION BY [department name] ORDER BY year(Date)) = 0 then null
else decimal(((sum(isnull(sales_forecast,0)) - LAG(sum(isnull(sales_forecast,0))) OVER (PARTITION BY [department name] ORDER BY year(Date))) /LAG(sum(isnull(sales_forecast,0))) OVER (PARTITION BY [department name]
ORDER BY year(Date)))*100),2) end as sales_forecast_growth
FROM
forecasted_table
where year(date) = 2023
group by [department name],year(Date)
ORDER BY
sales_forecast_growth asc
|
Which Account is expected to see the least significant growth in sales in the 2023?
|
SELECT top 1
[account name],year(Date),
case when LAG(sum(isnull(sales_forecast,0))) OVER (PARTITION BY [account name] ORDER BY year(Date)) = 0 then null
else decimal(((sum(isnull(sales_forecast,0)) - LAG(sum(isnull(sales_forecast,0))) OVER (PARTITION BY [account name] ORDER BY year(Date))) /LAG(sum(isnull(sales_forecast,0))) OVER (PARTITION BY [account name]
ORDER BY year(Date)))*100),2) end as sales_forecast_growth
FROM
forecasted_table
where year(date) = 2023
group by [account name],year(Date)
ORDER BY
sales_forecast_growth asc
|
Which Entity is expected to see the least significant growth in sales in the 2023?
|
SELECT top 1
[entity],year(Date),
case when LAG(sum(isnull(sales_forecast,0))) OVER (PARTITION BY [entity] ORDER BY year(Date)) = 0 then null
else decimal(((sum(isnull(sales_forecast,0)) - LAG(sum(isnull(sales_forecast,0))) OVER (PARTITION BY [entity] ORDER BY year(Date))) /LAG(sum(isnull(sales_forecast,0))) OVER (PARTITION BY [entity]
ORDER BY year(Date)))*100),2) end as sales_forecast_growth
FROM
forecasted_table
where year(date) = 2023
group by [entity],year(Date)
ORDER BY
sales_forecast_growth asc
|
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