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amitchandak
Super User
Super User

LTD Vs Period Retention considers that customer has never come before a given period. The definition of the new, retained, and lost customers are based on LTD(life till Date) and Period Data.

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amitchandak
Super User
Super User

YoY Retention is only focused on Year vs Year. The definition of the new, retained, and lost customers are only based on 2 Years of data i.e. This Year and Last Year.

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amitchandak
Super User
Super User

Power BI Distributing/Allocating the Monthly Target(Convert to Daily Target): Measure ( Daily/MTD)

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amitchandak
Super User
Super User

Whenever we have targets, typically, they're at the month or Year level. All are other data like sales purchase is typically at the level. Now we want to analyze both these data together there is a challenge one data is at year level another data is at the day level. To deal with such scenarios we have options of creating a day level table or creating a measure that can give us the Day level target.

In this blog we are going to take the approach of creating a Measure, that will provide us data at the day level.

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amitchandak
Super User
Super User

Whenever we have targets, typically, they're at the month or at the year level. All are other data like sales purchase is typically at the level. Now we want to analyze both these data together there is a challenge one data is at the year level another data is at the day level. To deal with such scenarios we have options of creating a day level table or creating a measure that can give us the Day level target.

In this blog we are going to take the approach of creating a Table, that will provide us data at the day level.

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amitchandak
Super User
Super User

Customer retention refers to the ability of a company or product to retain its customers over some specified period. Here, we will measure how long we were able to retain a customer since its first date of purchase.

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amitchandak
Super User
Super User

Customer retention refers to the ability of a company or product to retain its customers over some specified period. There are many ways to measure the data. In the last blog (blog link), we have checked MoM (Month on Month) Retention. Here, we will go through Period over Period or POP.

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amitchandak
Super User
Super User

When it comes to dealing with DirectQuery, most of us think that columns are not allowed. But this is not true. We can directly create columns to let us explore more. Can we use date table create in Power BI for getting time intelligence?

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amitchandak
Super User
Super User

Power BI Dateadd function works with continuous dates. What happens if you need dateadd on a noncontinuous date?

Date can act as Date Add function. Let us explore.

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amitchandak
Super User
Super User

Traveling across dates is pretty easy in Power BI. But what happens if someone asks you to add working days into a date, or to get 10 rolling Workdays?

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amitchandak
Super User
Super User

We have used weeks those start on any weekday. What happens when we need a week to start with Year Start Date and to reset every year?

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amitchandak
Super User
Super User

Just like a year can start from any month, organizations can have a week starting on any weekday. Let us explore weeks that can start from any of the seven days of the week.

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amitchandak
Super User
Super User

A Financial Calendar can start from any month of the year, and one needs to generate a Calendar based on that. Let us explore how to create a calendar that can start from any of the 12 months.

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amitchandak
Super User
Super User

Power BI Time Intelligence provides powerful functions to deal with Year, Quarter, and Month. But WTD and this Week vs Last Week do not have any out-of-the-box solution. Let’s quickly deal with Week Time Intelligence.

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amitchandak
Super User
Super User

Power BI supports Direct Query and, when the datasets are huge, the user prefers to go for the direct query. Not all is functionality available in Direct Query Mode. In the last article, we have discussed how time intelligence works well with Direct Query. In this article, we will explore more complex measures.

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