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.
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.
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?
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.
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.
The time Intelligence in Power BI makes it easy to calculate Year till date (YTD), Quarter till date (QTD) and Month till date (MTD) and to compare them to the same period last year and last period. How will this work in a Direct query environment?
Let us try to see the same in a Direct Query SQL Server Environment.
Power BI Time Intelligence functions provide a powerful way to get different types of time periods and time comparisons. Some of these are MTD, PMTD, PYMTD, MOM, MOYM, QTD, LQTD, QOQ, YTD, LYTD YOY etc. But dealing with a non-standard period can be a bit difficult, especially when months are not the standard months. In this article, we try to take on a case of non-standard periods (months).
Using DAX to compare data or metrics over time is incredibly efficient. Using a range of techniques in this post I'll show you how to analysis performance of these metrics over multi-year time periods.
One type of analysis I like to use quite often is comparing this year totals to last year totals. I want to also always make it as dynamic as possible. All I should have to do is change the context of my calculation (ie. bring in a new dimension like regions/products etc) and everything should automatically re-calculate.