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 -

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

◾ Data Used (Retail Transaction Data) -

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.

◾ Prepared and Cleaned Data -

• 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 Core Measures (DAX) -

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.

◾ Built Dashboards (Visuals + Slicers) -

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.

◾ Tested Interactivity -

• 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

◾ Key Findings -

• 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

◾  Skills Demonstrated -

• 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