I am working on the workforce analysis project. And I did some case when conditional calculations in Google Data Studio. However, when I successfully conducted the creation of the new field, I couldn't do the calculation again based on the fields I created.
Based on my raw data, I generated the start_headcount, new_hires, terminated, end_headcount by applying the Case When conditional calculations. However, I failed in the next step to calculate the Turnover rate and Retention rate.
The formula for Turnover rate is
terms/((start_headcount+end_headcount)/2)
for retention is
end_headcount/start_headcount
However, the result is wrong. Part of my table is as below:
Supervisor sheadcount newhire terms eheadcount turnover Retention
A 1 3 1 3 200% 0%
B 6 2 2 6 200% 500%
C 6 1 3 4 600% 300%
So the result is wrong. The turnover rate for A should be 1/((1+3)/2)=50%; For B should be 2/((6+6)/2)=33.33%.
I don't know why it is going wrong. Can anyone help?
For example, I wrote below for start_headcount for each employee
CASE
WHEN Last Hire Date<'2018-01-01' AND Termination Date>= '2018-01-01'
OR Last Hire Date<'2018-01-01' AND Termination Date IS NULL
THEN 1
ELSE 0
END
which means if an employee meets the above standard, will get 1. And then they all grouped under a supervisor. I think it might be the problem why the turnover rate in sum is wrong since it is not calculated on the grouped date but on each record and then summed up.
Most likely you are trying to do both steps within the same query and thus newly created fields like start_headcount, etc. not visible yet within the same select statement - instead you need to put first calculation as a subquery as in example below
#standardSQL
SELECT *, terms/((start_headcount+end_headcount)/2) AS turnover
FROM (
<query for your first step>
)
Related
I have a table with sales information at the transaction level. We want to institute a new model where we compensate sales reps if a customer has been makes a purchase after more than a year of dormancy. To figure out how much this would have cost historically, I want to add a column with a flag for whether or not each purchase was the Buyer's first in the past 365 days. What I'd like to do is a rowcount in Powerpivot, for all sales made by that customer in the past 365 days, and wrap it in an IF to set the result to 0 or 1.
Example:
Order Date Buyer First Purchase in Year?
1/1/2015 1 1
1/2/2015 2 1
2/1/2015 1 0
4/1/2015 2 0
3/1/2016 2 1
5/1/2017 2 1
Any assistance would be greatly appreciated.
Excellent business use case! It's quite relevant in the business world.
To break this down for you, I will create 3 columns: 2 with some calculations, and 1 with the result. Once you understood how I did this, you can combine all 3 column formulas and make a single column for your dataset, if you like.
Here's a picture of the results:
So here's the 3 columns that I created:
Last Purchase - in order to run this calculation, you need to know when the buyer made their last purchase.
CALCULATE(MAX([Order Date]),FILTER(Table1,[Order Date]<EARLIER([Order Date]) && [Buyer]=EARLIER([Buyer])))
Days Since Last Purchase - now you can compare the Last Purchase date to the current Order Date.
DATEDIFF([Last Purchase],[Order Date],DAY)
First Purchase in 1 Year - finally, the results column. This simply checks to see if it has been more than 365 days since the last purchase OR if the last purchase column is blank (which means it was the first purchase), and creates the flag you want.
IF([Days Since Last Purchase]>365 || ISBLANK([Days Since Last Purchase]),1,0)
Now, you can easily combine the logic of these 3 columns into a single column and get what you want. Hope this helps!
One note I wanted to add is that for this type of analysis it's not a wise move to do row counts as you had originally suggested, as your dataset can easily expand later on (what if you wanted to add more attribute columns?) and then you would have problems. So this solution that I shared with you is much more robust.
My dataset provides a monthly snapshot of customer accounts. Below is a very simplified version:
Date_ID | Acc_ID
------- | -------
20160430| 1
20160430| 2
20160430| 3
20160531| 1
20160531| 2
20160531| 3
20160531| 4
20160531| 5
20160531| 6
20160531| 7
20160630| 4
20160630| 5
20160630| 6
20160630| 7
20160630| 8
Customers can open or close their accounts, and I want to calculate the number of 'new' customers every month. The number of 'exited' customers will also be helpful if this is possible.
So in the above example, I should get the following result:
Month | New Customers
------- | -------
20160430| 3
20160531| 4
20160630| 1
Basically I want to compare distinct account numbers in the selected and previous month, any that exist in the selected month and not previous are new members, any that were there last month and not in the selected are exited.
I've searched but I can't seem to find any similar problems, and I hardly know where to start myself - I've tried using CALCULATE and FILTER along with DATEADD to filter the data to get two months, and then count the unique values. My PowerPivot skills aren't up to scratch to solve this on my own however!
Getting the new users is relatively straightforward - I'd add a calculated column which counts rows for that user in earlier months and if they don't exist then they are a new user:
=IF(CALCULATE(COUNTROWS(data),
FILTER(data, [Acc_ID] = EARLIER([Acc_ID])
&& [Date_ID] < EARLIER([Date_ID]))) = BLANK(),
"new",
"existing")
Once this is in place you can simply write a measure for new_users:
=CALCULATE(COUNTROWS(data), data[customer_type] = "new")
Getting the cancelled users is a little harder because it means you have to be able to look backwards to the prior month - none of the time intelligence stuff in PowerPivot will work out of the box here as you don't have a true date column.
It's nearly always good practice to have a separate date table in your PowerPivot models and it is a good way to solve this problem - essentially the table should be 1 record per date with a unique key that can be used to create a relationship. Perhaps post back with a few more details.
This is an alternative method to Jacobs which also works. It avoids creating a calculated column, but I actually find the calculated column useful to use as a flag against other measures.
=CALCULATE(
DISTINCTCOUNT('Accounts'[Acc_ID]),
DATESBETWEEN(
'Dates'[Date], 0, LASTDATE('Dates'[Date])
)
) - CALCULATE(
DISTINCTCOUNT('Accounts'[Acc_ID]),
DATESBETWEEN(
'Dates'[Date], 0, FIRSTDATE('Dates'[Date]) - 1
)
)
It basically uses the dates table to make a distinct count of all Acc_ID from the beginning of time until the first day of the period of time selected, and subtracts that from the distinct count of all Acc_ID from the beginning of time until the last day of the period of time selected. This is essentially the number of new distinct Acc_ID, although you can't work out which Acc_ID's these are using this method.
I could then calculate 'exited accounts' by taking the previous months total as 'existing accounts':
=CALCULATE(
DISTINCTCOUNT('Accounts'[Acc_ID]),
DATEADD('Dates'[Date], -1, MONTH)
)
Then adding the 'new accounts', and subtracting the 'total accounts':
=DISTINCTCOUNT('Accounts'[Acc_ID])
I have a stored procedure that calculates requirements for customers based on input that we receive from them.
Displaying this information is not a problem.
What I'd like to do is show the most recent received amount and subtract that from the weekly requirements.
So if last Friday I shipped 150 items and this weeks requirements are 100 items for each day then I'd like the data grid to show 0 for Monday, 50 for Tuesday, 100 for Wednesday - Friday.
I have currently tried using with limited success the sample select statement -
Select Customer, PartNumber, LastReceivedQty, Day1Qty, Day2Qty, Day3Qty, Day4Qty, Day5Qty,
TotalRequired
FROM Requirements
Obviously the above select statement does nothing but display data as it is in the table. So when I add the case state as follows I get a bit closer to what I need but not fully and I'm unsure how to proceed.
Select Customer, PartNumber, LastReceivedQty,
"Day1Qty" = case When Day1Qty > 0 then Day1Qty - LastReceivedQty end
...
This method works ok as long as the LastReceivedQty is less than the Day1 requirements but it's incorrect because it allows a negative number to be displayed in day one rather than pulling the remainder from day2.
Sample Data looks like the following:
Customer PartNumber LastReceivedQty Day1Qty Day2Qty Day3Qty Day4Qty Day5Qty TotalRqd
45Bi 2526 150 -50 100 100 100
In the sample above the requirements for part number 2526 Day 1 are 100 and the last received qty is 150
The day1qty shows -50 as opposed to zeroing out day 1 and subtract from day2, 3, etc.
How do I display those figures without showing a negative balance on the requirement dates?
Any help/suggestions on this is greatly appreciated.
I want to model a fact table for our users to help us calculate DAU (Daily active Users), WAU (Weekly active users) and MAU (Monthly active users).
The definitions of these measures are as follows:
1. DAU are users who is active every day during last 28 days.
2. WAU are users who are active at least on one day in each 7 days period during last 28 days
3. MAU are users who are active at least 20 days during last 28 days
I have built a SSAS cube with my fact table and user dimension table as follows
Fact : { date, user_id, activity_name}
Dimension: { date, user_id, gender, age, country }
Now I want to build a cube over this data so that we can see all the measures in any given day for last 28 days.
I thought of initially storing 28 days of data for all users in the SQL server and then do count distinct on date to see which measures they fall into.. but this proved very expensive since the data per day is huge..almost 10 millions rows.
So my next thought was to model the fact table (before moving it to SQL) such that it has a new column called "active_status" which is a 32 bit binary type column.
Basically, I'll store a binary number (or decimal equivalent) like 11000001101111011111111111111 which has a bit set on the days the user is active and off on the days user is not active.
This way I can compress 28 days worth of data in a single day before loading into data mart
Now the problem is , I think MDX doesn't support bitwise operations on columns in the expressions for calculated members like regular SQL does. I was hoping to create calculated measures daily_active_users, weekly_active_users and monthly_active_users using MDX that looks at this active_status bit for the user and does bitwise operation to determine the status.
Any suggestions on how to solve this problem? if MDX doesn't allow bitwise, what else can I do SSAS to achieve this.
thanks for the help
Additonal notes:
#Frank
Interesting thought about using a view to do the conversion from bitset to a dimension category..but I'm afraid it won't work. Because I have few dimensions connected to this fact table that have many-many relationships..for ex: I have a dimension called DimLanguage and another dimension called DimCountry and they have many-many relationship. And what ultimately I would like to do in the cube is to calculate the DAU/WAU/MAU which are COUNT(DISTINCT UserId) based on the combination of dimensions. So for ex; If a user is not MAU for dimension country US because he is only active 15 days out of 28 ....but he will be considered
You do not want to show the bitmap data to the users of the cube, but just the categories DAU, WAU, MAU, you should do the conversion from bitmap to category on data loading time. Just create a dimension table containing e. g. the following data:
id category
-- --------
1 DAU
2 WAU
3 MAU
Then define a view on your fact table that evaluates the bitmap data, and for each user and each date just calculates the id value of the category the user is in. This is then conceptually a foreign key to the dimension table. Use this view instead of the fact table in your cube.
All the bitmap evaluations are thus done on the relational side, where you have the bit operators available.
EDIT
As your requirement is that you need to aggregate the bitmap data in Analysis Services using bitwise OR as the aggregation method, I see no simple way to do that.
What you could do, however, would be to have 28 single columns, say Day1 to Day28, which would be either 0 or 1. These could be of type byte to save some space. You would use Maximum as aggregation method, which is equivalent to binary OR on a single bit.
Then, it would not be really complex to calculate the final measure, as we know the values are either zero or one, and thus we can just sum across the days:
CASE
WHEN Measures.[Day1] + ... + Measures.[Day28] = 28 THEN 'DAU'
WHEN Measures.[Day1] + ... + Measures.[Day7] >= 1 AND
Measures.[Day8] + ... + Measures.[Day14] >= 1 AND
Measures.[Day15] + ... + Measures.[Day21] >= 1 AND
Measures.[Day22] + ... + Measures.[Day28] >= 1 THEN 'WAU'
WHEN Measures.[Day1] + ... + Measures.[Day28] >= 20 THEN 'MAU'
ELSE 'Other'
END
The order of the clauses in the CASE is relevant, as the first condition matching is taken, and your definitions of WAU and MAU have some intersection.
If you have finally tested everything, you would make the measures Day1 to Day28 invisible in order not to confuse the users of the cube.
I have a table that lists every task an operator completed during a day. This is gathered by a Shop Floor Control program. There is also a column that has the total hours worked that day, this field comes from their time punches. The table looks something like this:
Operator 1 Bestupid 0.5 8 5/12/1986
Operator 1 BeProductive 0.1 8 5/12/1986
Operator 1 Bestupidagain 3.2 8 5/12/1986
Operator 1 Belazy 0.7 8 5/13/1986
Operator 2 BetheBest 1.7 9.25 5/12/1986
I am trying to get an efficiency out of this by summing the process hours and comparing it to the hours worked. The problem is that when I do any kind of summary on the hours worked column it sums EVERY DETAIL LINE.
I have tried:
If Previous (groupingfield) = (groupingfield) Then
HoursWorked = 0
Else
HoursWorked = HoursWorked
I have tried a global three formula trick, but neither of the above leave me with a summable field, I get "A summary has been specified on a non-recurring field"
I currently use a global variable, reset in the group header, but not WhilePrintinganything. However it is missing some records and upon occasion I will get two hoursworked > 0 in the same group :(
Any ideas?
I just want to clarify, I have three groups:
Groups: Work Center --> Operator --> Date
I can summarize the process hours across any group and that's fine. However, the hours worked prints on every detail line even though it really should only print once per Date. Therefore when I summarize the Hours Worked for an operator the total is WAY off because it is adding up 8hours for each entry instead of 8 hours for each day.
Try grouping by the operators. Then create a running total for the process hours that sum for each record and reset on change of group. In the group footer you can display the running total and any other stats for that operator you care to.
Try another running total for the daily hours but pick maximum as the type of summary. Since all the records for the day will have the same hours work the maximum will be correct. Reset with the change of the date group and you should be good to go.