In this project, I built an interactive Power BI dashboard to analyze retail store sales data representing a large-scale retail company similar to Walmart. The dashboard helps understand overall sales performance, profitability, customer segments, and product trends, with interactive slicers for easy exploration, along with a sales forecasting view to analyze historical trends and predict the next 15 days of sales using interactive visuals and slicers.
Let's have a step by step look at my analysis using Tableau -
My goal was to analyze retail sales data using Power BI to identify key business insights such as:
Overall sales, profit, and quantity performance
Monthly sales and profit trends
Best- and worst-performing product categories and sub-categories
Regional and state-level sales behavior
The dataset contains real-world retail information, including:
Order Details: Order ID, Order Date, Ship Date
Customer Information: Consumer, Corporate, Home Office segments
Geographic Data: Country, Region, State, City
Product Details:
Category (Office Supplies, Furniture, Technology)
Sub-Category (Phones, Chairs, Binders, Accessories, etc.)
Sales Amount
Profit
Quantity Sold
Ship Mode (Standard, Second Class, First Class, Same Day)
Payment Mode
This type of data is commonly analyzed by large retail companies like Walmart to track business performance and profitability.
Verified and corrected data types (dates, numeric fields, categories)
Checked for consistency across geographic and product fields
Organized data for accurate aggregations and visuals in Power BI
Created a dedicated Measures table and built key DAX measures, including:
Total Sales
Total Profit
Total Quantity
These measures were used across KPI cards and charts to ensure consistent calculations.
Designed a clean, one-page dashboard including:
Top KPI Cards showing Total Sales, Total Profit, and Quantity
Monthly Sales YoY line chart to analyze sales trends over time
Monthly Profit YoY line chart to track profitability trends
Sales by Category bar chart (Office Supplies, Furniture, Technology)
Sales by Sub-Category bar chart for detailed product analysis
Sales by Segment donut chart (Consumer, Corporate, Home Office)
Sales by Payment Mode donut chart
Sales by Ship Mode bar chart
Sales by Order Date
Interactive slicers were added for:
Region (Central, East, South, West) using button-style tabs
State-level selection using a map-like button slicer for detailed filtering
All slicers update the entire dashboard dynamically.
Verified that Region and State slicers correctly filter all visuals, including historical trends and forecast views
Ensured KPI cards, charts, and the 15-day sales forecast update consistently with slicer selections
Checked unit formatting (K for thousands, M for millions) across all visuals, including forecast values
Validated that zoomed-in trend and forecast visuals respond accurately to user interactions
Technology and Office Supplies contribute the highest share of total sales
The Consumer segment generates the largest portion of revenue
Sales and profit exhibit noticeable monthly and daily fluctuations, reflecting seasonality and demand variability
Shipping mode and payment method significantly impact overall sales performance
Regional and state-level analysis highlights performance differences across locations
Historical sales trends show an overall upward movement, which supports the short-term forecast
The 15-day sales forecast indicates moderate growth with variability, helping anticipate near-term demand and planning needs
Power BI dashboard development and layout design
DAX measures and KPI creation
Retail sales, profit, and customer segment analysis
Time-series trend analysis and short-term sales forecasting
Data cleaning and preparation
Interactive reporting using slicers and filters
Business-focused data storytelling and decision support
📊 Tools Used: Power BI, DAX
📁 Domain: Retail Analytics / Business Intelligence
◾ KPI CARDS (TOP METRICS) -
🔹Profit Card – 175K
Shows total profit generated across all selected filters (region, state, category).
🔹Sales Card – 2M
Displays total revenue from retail transactions.
🔹Quantity Card – 22K
Represents the total number of products sold.
◾ SALES BY PAYMENT MODE (DONUT CHART)
Purpose: Understand how customers pay.
COD – 42.62%
Online – 35.38%
Cards – 21.99%
Insight: Cash on Delivery dominates, indicating customer preference and potential logistics impact.
◾ MONTHLY SALES YOY (LINE / AREA CHART)
Purpose: Track sales trends over time (2019 vs 2020).
X-axis: Months
Y-axis: Sales
Two lines compare year-over-year performance.
Insight: Sales show steady growth with strong peaks toward year-end, highlighting seasonal demand.
◾ SALES BY SEGNMENT (DONUT CHART)
Purpose: Customer Contribution to Sales
Consumer – 48.09%
Corporate – 32.55%
Home Office – 19.35%
Insight: Consumer segment drives nearly half of total sales, making it the primary target group.
◾ MONTHLY PROFIT YOY (LINE CHART)
Purpose: Analyze profitability trends over time.
Highlights profit fluctuations
Identifies months with high or low margins
Insight: Profit does not always follow sales exactly, indicating cost and discount effects.
◾ SALES BY CATEGORY (BAR CHART)
Category Performance:
Office Supplies – 0.6M
Technology – 0.5M
Furniture – 0.5M
Insight: Office Supplies generate the highest revenue among categories.
◾ SALES BY SHIP MODE (HORIZONTAL BAR CHART)
Logistics Performance:
Standard Class – 0.50M
Second Class – 0.17M
First Class – 0.11M
Same Day – 0.05M
Insight: Standard shipping is most used, showing cost-efficient delivery preference.
◾ SALES BY SUB-CATEGORY (BAR CHART)
Top Sub-Categories:
Phones – 0.20M
Chairs – 0.18M
Binders – 0.17M
Storage – 0.15M
Accessories – 0.12M
Insight: Phones and Chairs are high-revenue products, useful for inventory and marketing focus.
◾ REGION SLICERS (BUTTON TABS)
Central | East | South | West
Allows users to instantly filter the entire dashboard by region.
◾ STATE SLICERS (MAP-LIKE BUTTON SLICERS)
• Replaces traditional map visuals
• States displayed as rounded buttons
• Multi-row tile layout
Insight: Provides map-style geographic filtering without map dependency issues, improving usability and performance.
◾ SALES BY ORDER DATE (LINE GRAPH) -
Purpose: Analyze historical daily sales trends and predict short-term future sales performance.
Displays daily sales data from 2019 to 2021
Green line represents actual historical sales
Orange line with shaded region represents the 15-day sales forecast
Upper chart shows long-term sales behavior
Lower chart provides a zoomed-in recent view for more accurate forecasting
Insight:
Sales exhibit high day-to-day variability, common in large-scale retail operations
Overall trend shows gradual growth, especially toward late 2020
Forecast indicates moderate sales continuation with fluctuations, helping anticipate near-term demand
Shaded forecast range highlights uncertainty, allowing better risk-aware planning
Business Value:
This visualization supports inventory planning, staffing decisions, and demand forecasting, enabling proactive decision-making rather than reactive responses.