I am trying to calculate monthly/weekly growth rate for my website in Google Analytics(GA). I can't calculate it using the existing metrics because google analytics currently does not show the count of all users till date for my website. The user metric in GA only shows active users within the selected date range but to calculate growth rate, I need to know total users till date irrespective of the selected date range. Does anyone know how to do that? For example:
Date range: last month
Total users(till date): 100
Total users(in selected date range): 50
Total New users: 10
Actual growth rate = Total new users / Total users(till date) = 10/100 = 10%
GA growth rate = Total new users / Total users(in selected date range) = 10/50 = 20%
Thus in GA I can only calculate time specific growth rate because it doesn't provide count of Total users(till date). I have tried custom metrics and custom variables but both of them have the same limitation.
Related
While working on a sales report for an entertainment company ( bars and nightclubs), I normally just sum sales and I get the daily sum of sales. but I was communicated that their business day starts at 6 am of each and closes at 5:59:59 am the next day. basically sales reported Monday are the sales from 6 am Sunday thru 5:59:59 am Monday.
the company operates throughout the US so we have multiple time zones as well
the table has the following columns:
Transaction id, location, Transaction_datetimeLocal, TransactionDateTimeUTC, Transaction amount
how do I define / filter the calculation to be from 6am one day to 5:59:59 am the next day USING Power BI / DAX
TIA
In Power BI you have your table with the local time. You need to add a calculated column with the following DAX formula:
Business Time = 'Table'[Local Time] - TIME(6, 0, 0)
From this new column you could the create your business date with
Business Date = 'Table'[Business Time].[Date]
This is how it looks in the Data view:
I am trying to create a saved search of time entries in Netsuite.
Under criteria, I have specified a date range. This varies as this is an available filter
For the purposes of this example, the date range is 1/4/2020 to 10/4/2020
The first column ('Total Customer Billable Hours') in this sums all time entries that are coded against project task type 'billable project'. The formula I am using for this:
Formula (Numeric), sum, Case when {project.task_type}='Billable' then {durationdecimal} else 0 end
For the second column, I want the sum of hours the employee would normally work (in the time period specified under criteria-1/4/2020 to 10/4/2020 in this example)
The formula I am using to sum this is
Formula(numeric), sum, {timesheet.workcalendarhoursdecimal}
However, this is multiplying the employee's weekly hours by the number of time entries that make up the 'Total customer billable hours' figure
i.e. if an employee works a 40 hour week, the formula is multiple 40 x 36 (the number of time entries that make up the customer billable figure for example)
What would the correct formula be so that the second column is only summing the employee's work calendar hours for the period specified in the criteria/available filter selection?
Try changing sum to maximum:
Formula(numeric), maximum, {timesheet.workcalendarhoursdecimal}
I have a series of random data points with order date, expiry date and product category.
I am trying to compute 2 age variables, wherein 1 would compute the rolling age using just the order date (hence no expiry date) between order dates.
In other words, I don't need age determined based on current date but ones' relative to each data point in the data. As such, the average would keep increasing as more products are purchased and first ones get old. The second age variable would essentially cap each order by expiry date, which would mean the rolling average would not necessarily be increasing.
I am trying to do this using R or Tableau.
I have the following problem,
I want to see the percentage of new sessions in a given month. I am specifically interested in new sessions by "direct" channelgrouping
in custom report I set dimensions for November: yearMonth, source, medium, channelgrouping. and metrics:percentNewSessions
it gives me 35%
then I create a custom reports with the same dimension but with Date as a metric.I average the %new session and get 38%
why does it differ? What should I trust?
Both of these measures are true, but they tell something different.
Another simple use case with date metric:
Day 1: visitor A
Day 1: visitor B
...
Day 7: visitor A
Day 7: visitor C
— Week unique visitors: 3
— Week sum of daily unique visitors: 4
Define your KPI, what is measured, how it is measured, the period for measurement, and always stick with the KPI parameters; else you're on another one :)
Good Evening,
I have two datasets of a shop referred to the same time period;
the first set's columns are: Date - Quantity Ordered - Initial Inventory -
Final Inventory After the Order(= Column 2 + 3)
Since orders are not made on a daily basis, I completed the set with zeros, in order to have a complete series of dates.
The second set's columns are: Date - Quantity Sold.
I would like to create a dataset in which the Inventory is updated according to sales, on a daily basis, merging the two dataframes.
For example: On the 01-01-2017, Quantity Ordered = 20, Initial Inventory = 5, Final Inventory = 25, Quantity Sold = 8, Actual Inventory = 25-8 = 17;
If I compute the difference between columns, sometimes it may happen that Actual Inventory = -3, because I completed the initial date series with zeros as missing values!
Does anyone know how to address this question?
Best Regards,
Alex