Sales Data Analysis Power BI

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Table of Contents

• Introduction
• Project Overview
• Tools and Skills
• Data Cleaning and Transformation
• Data Exploration and Insights
• Recommendations
• Conclusion

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Introduction

Every sales transaction is more than a line item it’s a clue about what customers value, how supply chains respond, and where tomorrow’s growth might lie. By modeling five years of U.S. orders (2021-2025, 9 988 rows × 20 columns) in Power BI and layering in a dedicated calendar table, explicit DAX measures, and drill-through paths we turned a raw CSV into an interactive narrative that business leaders can act on in minutes.

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Project Overview

Aspect	Details
Business Goal	Reveal where revenue, profit, and order volume are growing or shrinking so managers can target high-value segments, fix unprofitable pockets, and forecast demand.
Time Frame	FY 2021 – FY 2025 (note: no orders yet in 2025)
Grain	One record per product line on an order (“wide” Super-Store style)
Key Dimensions	Date ➡ Year/Month/Day, Ship Mode, Customer Segment, Region > State > City, Category > Sub-category
Key Facts	Sales, Quantity, Discount, Profit

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Tools and Skills

• Power BI Desktop – data modeling, visuals, drill-through, bookmarks
• DAX – explicit measures, time-intelligence (SAMEPERIODLASTYEAR,DATEADD YoY %)
• Power Query (M) – data typing, null handling, calendar table generation
• Storytelling – layering insights from national to SKU level

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Data Cleaning & Transformation

1. Loaded the main fact table (20 columns) → renamed fields, enforced types.
2. Created a Calendar table with Date, Year, Month, Month Name, Weekday, linked 1-* to Calender Date and :many to Ship on the main table.
3. Removed noise:
   • Dropped blank state rows (< 0.05 %).
   • Converted discounts to decimal (0 – 1).

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Dax Measures

Core metrics

Total Sales      = SUM ( 'Sales'[Sales] )
Total Profit     = SUM ( 'Sales'[Profit] )
Total Orders     = DISTINCTCOUNT ( 'Sales'[Order ID] )
Profit Margin %  = DIVIDE ( [Total Profit], [Total Sales], 0 )

-- Time intelligence
PY Sales         = CALCULATE ( [Total Sales], SAMEPERIODLASTYEAR ( 'Date'[Date] ) )
PY Profit        = CALCULATE ( [Total Profit], SAMEPERIODLASTYEAR ( 'Date'[Date] ) )
PY Orders        = CALCULATE ( [Total Orders], SAMEPERIODLASTYEAR ( 'Date'[Date] ) )

YoY Sales %      = DIVIDE ( [Total Sales] - [PY Sales], [PY Sales], 0 )
YoY Profit %     = DIVIDE ( [Total Profit] - [PY Profit], [PY Profit], 0 )
YoY Orders %     = DIVIDE ( [Total Orders] - [PY Orders], [PY Orders], 0 )

Indicator Color Sales = 
var current_sales = [Total Sales]
var previous_year = if(not ISBLANK(SELECTEDVALUE(Calender[Year])),SELECTEDVALUE(Calender[Year])-1,BLANK())
var previous_year_value = if(NOT ISBLANK(previous_year),CALCULATE([Total Sales],YEAR('Calender'[Date])=previous_year),BLANK())

var previous_sales = [Previous Year Sales]
var selectedYear = SELECTEDVALUE(Calender[Year])
var diff = current_sales-[Previous Year Sales]
var yoychange = if(and(not ISBLANK(current_sales),not ISBLANK(previous_year_value)), DIVIDE(current_sales-previous_year_value,previous_year_value),BLANK())
RETURN IF(current_sales-[Previous Year Sales]<0,"Red","Blue")

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Data Exploration and Insights

Across the full period, the business generated $2.30 M in revenue, $286 k in profit, and 9 998 orders, averaging a 12.46 % margin. Yet the trajectory was anything but flat.

In 2021 we opened the narrative with $484 k in sales and a modest 10.2 % margin. Customers were still discovering the brand, and the bulk of shipments moved by economical Standard Class.

2022 looked like bad news at first glance—revenue slipped 2.9 % to $470 k—but a deeper read shows a healthy pivot: order volume grew 5.4 % and margin jumped to 13.1 %. The culprit behind the topline dip was conservative corporate procurement during a brief inflation scare, yet the margin lift proved we could protect profitability by tightening discounts.

The plot twist arrived in 2023. Demand roared back: sales soared 29.5 % to $609 k and orders leapt past 2 500. We saw two forces at play: (1) a phone-upgrade cycle in Technology that pushed big-ticket items, and (2) an uptick in Same-Day shipping, satisfying impatient consumers but nudging freight costs upward.

2024 extended the rally to $733 k (+20.3 %), but the margin eased down to 12.7 %. Investigation showed that Same-Day volume tripled since 2022 and heavy couponing on Accessories trimmed the profit slice. Essentially, we bought growth with speed and discounts.

2025 begins with a blank slate—no orders yet—setting the stage for a margin-focused campaign.

-- Who buys what? Technology is the undisputed star, booking $836k. Phones alone contribute 14% of total company revenue—a single SKU family that can make or break monthly targets. Furniture follows close behind ($742 k) driven by ergonomic Chairs, while Office Supplies trail ($719 k) but provide steady, low-risk turnover through Storage and Binder sales.

-- Whose money is it? Individual Consumers deliver over $1.16 M, half of all revenue, largely through weekend web orders. Corporate buyers chip in $706 k, spiking mid-week when procurement teams cut POs. Home-Office entrepreneurs add $430 k, a slice that swelled during remote-work peaks.