E-Commerce Analytics: The Complete Guide to Madhav Sales Dashboard in Power BI

1. Power BI for Global E-Commerce Analytics

In the competitive landscape of e-commerce, data is your most valuable asset. Power BI enables enterprise-level analytics by integrating multiple data silos into a unified view.

// Power Query Example
let
    Source = Shopify.Contents("https://store.api"),
    Orders = Source{[Name="Orders"]}[Data]
in
    Orders

// DAX Currency Conversion
Sales USD = 
SUMX(
    Sales,
    Sales[Amount] * RELATED(ExchangeRates[Rate])
)

2. Data Architecture for Multi-Channel Sales

A robust data pipeline ensures accuracy and reliability. Below is the architecture used for the Madhav Dashboard:

3. Key Dashboard Components

4. Advanced DAX Formulas for Sales

To go beyond basic sums, we utilize time-intelligence functions in DAX to reveal trends.

Rolling 28-Day Revenue

Rolling Revenue = 
CALCULATE(
    SUM(Sales[Revenue]),
    DATESINPERIOD(
        Calendar[Date],
        LASTDATE(Calendar[Date]),
        -28,
        DAY
    )
)

This formula smooths out daily volatility to show the true direction of your sales trend.

5. Real-Time Inventory Management

Visualizing stock levels across global warehouses prevents stockouts and overstocking. The heatmap below allows logistics managers to spot issues instantly.

Figure 2: Heatmap showing stock density vs. regional demand.

6. Customer Lifetime Value (CLV)

Understanding the long-term value of a customer helps in setting accurate ad spend limits.

// CLV Calculation Measure
Customer LTV = 
[Average Order Value] * [Purchase Frequency] * [Customer Lifespan]

7. Cross-Border Sales Analytics

// Region-Specific Sales
EU Sales = 
CALCULATE(
    [Total Sales],
    Customers[Region] = "Europe"
)

// On-Time Performance
On-Time % = 
DIVIDE(
    [On-Time Deliveries],
    [Total Orders]
)

8. Actionable Business Insights

Data without action is noise. Here are the specific insights derived from this dashboard implementation: