Power BI

Diving Deeper into DAX: From Fundamentals to Complex Scenarios in Power BI

Understanding DAX with Power BI allows you to gain unmatched insights and efficiency in the field of data analysis. With the use of sophisticated capabilities like calculated columns and temporal intelligence, this introduction demonstrates the transformative power of DAX through real-world applications. In this blog we explore the core of DAX, adding dynamic data storytelling and advanced analytics to your Power BI reports.

Basic Operations and Functions in DAX

Power BI's DAX (Data Analysis Expressions) is a formula language that elevates data analysis to new heights. This guide takes you from understanding basic operations and functions to applying DAX in sophisticated, real-world scenarios.

DAX enables various operations for data analysis:

Understanding these foundational elements is key to harnessing the full power of DAX. Beyond basic calculations, DAX is instrumental in:

Scenarios

Scenario 1: Sales Growth Month-over-Month

Sales Data
Date Total Sales Customer ID Purchase Frequency
01-01-2023 1200 C001 Frequent
15-01-2023 800 C002 Occasional
01-02-2023 1500 C001 Frequent
18-02-2023 700 C003 Occasional
01-03-2023 1600 C001 Frequent
20-03-2023 900 C002 Occasional

Scenario 2: Calculating the Customer Lifetime Value

Sample Data
Sales ID Customer ID Sale Amount Purchase Date
1 Customer 1 100 01-01-2023
2 Customer 2 105 03-01-2023
3 Customer 3 110 05-01-2023
4 Customer 4 115 07-01-2023
5 Customer 5 120 09-01-2023

Objective: Determine the percentage growth in sales compared to the previous month.

Simplified Logic:

Sales Difference MoM = Sale value (Current Month) – Sale value (Previous Month) Growth MoM = Sales Difference MoM / Sale value (Previous Month) * 100

DAX Query
MoM Sales % Difference = 
VAR CurrentMonthIndex = MAX(('Table'[Month no]))
VAR PreviousMonthSales = CALCULATE(
SUM('Table'[Sale]), 
FILTER(
ALL('Table'), 
VALUE('Table'[Month no]) = CurrentMonthIndex - 1
)
)

VAR CurrentMonthSales = SUM('Table'[Sale])
RETURN
IF(
PreviousMonthSales = 0 || ISBLANK(PreviousMonthSales), 
BLANK(), 
(CurrentMonthSales - PreviousMonthSales) / PreviousMonthSales

This calculation shows the growth in sales by comparing the total sales of the current month against the previous month.

SAMPLE VISUAL:
blog image
Simplified Logic

Gather Basic Metrics: Calculate the total revenue from all sales, count the total number of purchases, and identify the number of unique customers.

Calculate Per-Transaction and Per-Customer Averages: Determine the Average Purchase Value (APV) by dividing total revenue by the number of purchases, and calculate the Purchase Frequency (PF) as the average number of purchases per customer.

Estimate Customer Value (CV): Multiply APV by PF to find the average monetary value each customer contributes through their purchases.

Assess Customer Engagement Duration: Identify the earliest and latest purchase dates to calculate the total active days, then divide by the number of customers to get the Average Customer Lifespan (ACL).

Compute Customer Lifetime Value (CLV): Multiply the Customer Value by the Average Customer Lifespan to estimate the total value a customer is expected to bring over the entirety of their relationship with the company.

Customer Lifetime Value = 
VAR TotalRevenue = SUM('Sheet1'[SaleAmount])
VAR TotalPurchases = COUNT('Sheet1'[SaleID])
VAR TotalCustomers = DISTINCTCOUNT('Sheet1'[CustomerID])
VAR APV = DIVIDE(TotalRevenue, TotalPurchases)
VAR PF = DIVIDE(TotalPurchases, TotalCustomers)
VAR CV = APV * PF
VAR FirstPurchaseDate = CALCULATE(MIN('Sheet1'[PurchaseDate]), ALL('Sheet1'[SaleAmount]))
VAR LastPurchaseDate = CALCULATE(MAX('Sheet1'[PurchaseDate]), ALL('Sheet1'[SaleAmount]))
VAR TotalDays = DATEDIFF(FirstPurchaseDate, LastPurchaseDate, DAY)
VAR ACL = DIVIDE(TotalDays, TotalCustomers)
VAR CLV = CV * ACL
RETURN

This complex formula categorizes customers into VIP, Regular, or Casual based on their purchase frequency, offering deep insights for targeted marketing strategies

SAMPLE VISUAL:
blog image
Conclusion

Mastering DAX in Power BI is super helpful for anyone working with data. Just like learning special SQL tricks can make finding and understanding information easier, getting good at DAX means you can do cool things with your data in Power BI. At In22labs, we use these skills all the time. We make data load faster, figure out stuff by looking at data in different ways, keep our queries simple, and get exactly the data we need. This makes it quicker to make smart decisions and helps our business do better.

Tags

Written by

Vishnu Mithran

Published on

20/03/2024

Other Blogs

  • © 2024 In22labs. All rights reserved

Facebook Logo Facebook Logo Facebook Logo Facebook Logo
logo

In22labs
Typically replies within an hour

In22labs
Hi there 👋

How can I help you?
×
Chat with Us