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

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
            

SAMPLE VISUAL:

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:

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.