? Executive Sales & Customer Intelligence Suite (Power BI)

? Project Overview

This project delivers a comprehensive 3-page business intelligence solution designed to transform raw transactional data into actionable executive insights. By building a robust semantic layer using DAX (Data Analysis Expressions), this dashboard tracks performance across three critical business dimensions: Sales, Customers, and Products.

? The "Data Source" Connection

This project is part of a modular data architecture. It consumes data from a structured SQL Data Warehouse (Gold Layer).

Source: SQL Server (Import Mode)

Pre-processing: While basic cleaning was done in SQL, the advanced business logic and interactivity were engineered directly in Power BI using DAX.

SQL Backend Repository: (https://github.com/baheyt...)

?️ Data Modeling (Star Schema)

The foundation of this analysis is a highly optimized Star Schema, ensuring scalability and calculation accuracy.

Fact Table: fact_sales (Sales transactions and quantities).

Dimension Tables: * dim_customers: Demographic and segmentation data.

dim_products: Product catalog and performance tiering.

dim_date: A custom-built time dimension.

? Custom Date Dimension (DAX)

To support advanced Time Intelligence, I generated a dedicated date table:

dim_date = CALENDAR(MIN('gold_fact_sales'[order_date]), MAX('gold_fact_sales'[order_date]))

Quarter = YEAR(dim_date[Date]) & " Q" & QUARTER(dim_date[Date])

? Advanced DAX Engineering

To handle complex business logic directly within the data model, I engineered a series of calculated columns and measures:

1. Sales & Financial Logic

COGS (Cost of Goods Sold):

COGS = RELATED('gold_dim_products'[product_cost]) * 'gold_fact_sales'[quantity]

Total Profit:

total_profit = SUM('gold_fact_sales'[sales_amount]) - SUM('gold_fact_sales'[COGS])

Net Profit Margin:

net_profit = [total_profit] / SUM('gold_fact_sales'[sales_amount])

12-Month Moving Average =

AVERAGEX(

DATESINPERIOD(dim_date[Date], LASTDATE(dim_date[Date]), -12, MONTH),

SUM('gold fact_sales'[sales_amount])

)

Customer Intelligence & Segmentation Dynamic Age Binning: (Calculated relative to the last order date in the dataset)

age_bins =

VAR age = DATEDIFF('gold dim_customers'[birth_date], MAX('gold fact_sales'[order_date]), YEAR)

RETURN

SWITCH(TRUE,

age < 30 , "18-29",

age < 40 , "30-39",

age < 50 , "40-49",

age < 60 , "50-59",

age >= 60 , "60 & Above"

)

Customer Lifespan: lifespan = DATEDIFF(MIN_ORDER_DATE, MAX_ORDER_DATE, MONTH)

Product Performance Tiering Product Segment: Automatically tiers the catalog into High, Mid, and Low performers based on revenue.

product_segment =

VAR total_sales = CALCULATE(SUM('gold fact_sales'[sales_amount]), ALLEXCEPT('gold dim_products','gold dim_products'[product_key]))

RETURN

SWITCH(TRUE(),

total_sales > 50000, "Top-performer",

total_sales > 10000, "Mid-Level",

"Low Performer")

? Key Analytical Insights

? Executive Sales Overview Revenue Health: Generated $29M in total revenue with a steady 40% Net Profit Margin. Global Footprint: The United States market leads as the primary revenue engine, followed by Australia.

? Customer Demographics Target Audience: Customers aged 30-49 are the most significant contributors, representing approximately 68% of total revenue. Retention Patterns: Engagement analysis shows a peak retention window within the first 24 months of a customer’s lifecycle.

? Product Performance Revenue Mix: The Road Bikes subcategory is the dominant revenue driver ($14.51M), accounting for over 51% of total sales. Cost vs. Profit: High-cost bike models drive the highest absolute profit, while accessories serve as volume-based foot-traffic drivers.

?️ Tools & Technologies

Power BI Desktop: UI/UX Design, Data Visualization, and Modeling.

DAX: Advanced analytical measures and calculated columns.

SQL Server: Source data hosting (Gold Layer).

GitHub: Version control and project documentation.