Well, it’s a broad term and may be interpreted in many possible ways. Let’s start with understanding what is a customer lifetime value. In this article, I’ll not spend too much time explaining the ins and outs of the window functions, as I want to focus on explaining how you can leverage these functions to satisfy a very common business request – calculate the lifetime value of the customer. If you’re specifically interested in the OFFSET function, I encourage you to read this great article written by my friend Tom Martens, or this one by Štěpán Rešl. If you want to learn more about these functions and how they work behind the scenes, I strongly recommend reading this article from Jeffrey Wang – this is the best starting point for diving deeper into DAX window functions. At this moment, there are three DAX window functions: OFFSET, INDEX, and WINDOW. So, I was beyond excited when Power BI Desktop December 2022 update announced a brand new set of DAX functions – collectively called window functions – that should achieve the same goal as SQL window functions. Ok, we could have solved these challenges of performing different calculations over a certain set of rows, by writing more complex DAX – but, honestly, that was very often a really painful experience. Therefore, when I transitioned to Power BI, I was quite surprised (not to say disappointed) that there is no DAX equivalent to SQL window functions. I’ve described one of the possible use cases in this article, but there are literally a dozen of scenarios that can be quickly and intuitively solved by using window functions. In my previous “life” as a SQL professional, I’ve been using T-SQL window functions extensively for various analytic tasks.
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