Sales Store Analysis Report

Project Overview

This project involved analyzing a Sales Store dataset provided by my tutor during the Women Techsters Bootcamp. Using Power BI Desktop, I performed data transformation, analysis, and visualization to uncover key metrics and meaningful insights for stakeholders.

Data Preparation & Transformation Process

1. Data Loading:

   • Downloaded and imported the dataset into Power BI Desktop.

2. Data Cleaning and Transformation:

   • Checked for missing values and data inconsistencies.

   • Standardized columns for readability and converted data types (e.g., dates, numeric fields).

   • Created calculated columns and measures, including:

     • Total Sales = 233.65 million

     • Discount Sales = 20.06 million

     • Total Quantity Ordered = 862K

     • Average Customer Age = 46.49 years

3. Data Modeling:

   • Established relationships between relevant tables (Orders, Customers, and Products) to support interactive reporting.

KPIs and Visualizations

1. Key Performance Indicators (KPIs):

   • Total Sales: 233.65 million

   • Total Quantity Ordered: 862K units

   • Discount Sales: 20.06 million

   • Average Customer Age: 46.49 years

   • Number of States: 51

2. Visualizations:

   • Total Sales by Gender:

     • Male: 116.99 million

     • Female: The rest of the total sales.

   • Total Sales by Region:

     • South Region: 89.65 million (highest across the four regions).

   • Total Sales by Category:

     • Mobiles and Tablets: 130.11 million (highest among the 15 categories).

   • Total Sales by Month:

     • December: 57.67 million (highest total sales for any month).

3. Slicers:

   • Year: Filter sales performance by year.

   • Category: Explore trends across different product categories.

   • Status: Analyze orders based on status (e.g., Completed, Cancelled).

Actionable Insights for Stakeholders

1. Regional Focus:

   • The South Region accounted for the highest total sales (89.65 million). This suggests a strong market presence in the region, which can be further leveraged through targeted campaigns.
     Action: Increase marketing and promotional efforts in the South Region to drive even higher revenue.

2. Customer Demographics and Gender Trends:

   • The male gender brought in the highest sales (116.99 million). Understanding why men are purchasing more can help refine product offerings and messaging.
     Action: Create campaigns that further resonate with male customers, while exploring strategies to engage more female customers.

3. Category Insights:

   • Mobiles and Tablets generated the highest revenue (130.11 million), indicating a strong consumer demand for these products.
     Action: Increase inventory and promotional efforts for mobiles and tablets during peak periods to maximize revenue.

4. Seasonality Patterns:

   • December recorded the highest monthly sales (57.67 million), likely driven by holiday shopping.
     Action: Prepare for the holiday season by stocking high-demand products and running festive promotions to capitalize on the peak month.

5. Discount Strategy:

   • Discounted Sales totaled 20.06 million, confirming the impact of discounts on boosting revenue.
     Action: Offer targeted discounts during slow months or special events to drive more purchases.

Conclusion

This analysis offers actionable insights into regional performance, customer behavior, product categories, and seasonal trends. The interactive Power BI report provides stakeholders a comprehensive view of the business, empowering them to make data-driven decisions.

Next Steps

• Explore customer segmentation for more targeted marketing.

• Use predictive analytics to forecast future trends and revenue growth.

• Monitor the impact of discount campaigns on overall profitability.