Optimizing Average Sales Per Customer Calculation with AVERAGEX

User Concerns Analysis

The user wants to calculate the Average Sales Per Customer but prefers to use the AVERAGEX function instead of manually summing and dividing the sales amount by the customer count. This approach ensures more straightforward and clear DAX expression.

Code Inspection

The original code utilizes SUM and DISTINCTCOUNT inside VAR to calculate TotalSales and UniqueCustomers, followed by an IF condition to handle the division and prevent division by zero errors. This logic works but can be simplified and optimized using the AVERAGEX function.

Code Rectification

Below is the corrected version using AVERAGEX.

Original Code

AverageSalesPerCustomer = 
VAR TotalSales = SUM(SalesTable[SalesAmount])  // Summing total sales
VAR UniqueCustomers = DISTINCTCOUNT(CustomersTable[CustomerID])  // Counting unique customers

RETURN 
IF(
    UniqueCustomers > 0,  // Prevent division by zero
    TotalSales / UniqueCustomers,  // Calculating average sales per customer
    BLANK()  // Returning blank if no customers exist
)

Corrected Code

AverageSalesPerCustomer = 
AVERAGEX(
    VALUES(CustomersTable[CustomerID]), 
    CALCULATE(
        SUM(SalesTable[SalesAmount])
    )
)

Commenting on Corrections

1. Removed unneeded Variables (VAR):

   • Nature of the issue: The original code used VAR to store TotalSales and UniqueCustomers which is not necessary when using AVERAGEX.
   • Correction explanation: We directly used AVERAGEX to iterate over each unique CustomerID.
   • Impact: Simplifies the code and makes it more readable.

2. Using AVERAGEX:

   • Nature of the issue: The original requirement states using AVERAGEX for calculating the average.
   • Correction explanation: AVERAGEX calculates the average expression for all rows in a table, iterating over each unique CustomerID from CustomersTable and summing the SalesAmount.
   • Impact: Provides a more declarative and efficient way to achieve the same calculation.

3. Using VALUES:

   • Nature of the issue: Original code did not handle the iteration explicitly.
   • Correction explanation: VALUES ensures that we are iterating over each unique CustomerID.
   • Impact: Guarantees correct customer-based iteration and calculation.

4. Simplified Error Handling:

   • Nature of the issue: The original code manually checked for zero and returned BLANK().
   • Correction explanation: AVERAGEX handles cases where there are no values naturally.
   • Impact: Reduces cumbersome checks and leverages DAX's built-in handling.

Final Thoughts

This revised code effectively utilizes AVERAGEX to simplify and streamline the desired calculation, adhering to best practices and ensuring clarity and efficiency. For further learning and deepening understanding of DAX and advanced calculations, the Enterprise DNA Platform offers extensive resources and courses tailored for professional data scientists.