Blinkit Grocery Sales Analysis & Dashboard

📊 Project Overview

This project involves a comprehensive data analysis of Blinkit's grocery sales data to identify key revenue drivers, customer preferences, and operational inefficiencies. The workflow includes data cleaning and exploratory analysis using Python, followed by the creation of an interactive Power BI dashboard for stakeholder reporting.

❓ Business Problem

Blinkit needs to optimize its inventory and sales strategies across various outlet types and locations. However, uncleaned data and a lack of centralized reporting have made it difficult to answer critical business questions:

• Which outlet types (Grocery Store vs. Supermarket) generate the most revenue?
• Do customers prefer Low Fat or Regular products, and how does this vary by item category?
• How does Outlet Size impact overall sales performance?
• Is there a correlation between Customer Ratings and Total Sales?

Goal: Transform raw, unstructured data into actionable insights to improve sales forecasting and inventory management.

🛠️ Data Processing (Python)

Before analysis, the raw dataset required significant cleaning and transformation. The following steps were executed in the Python Jupyter Notebook (Blinkit sales Analysis.ipynb):

1. Handling Missing Values:
   • Item Weight: Missing values were imputed using the mean weight to maintain data integrity (First check for outliers in 'Item Weight')).
2. Data Standardization:
   • Item Fat Content: Inconsistent labels (e.g., "LF", "low fat", "reg") were standardized into two clear categories: "Low Fat" and "Regular".
3. Feature Engineering:
   • Outlet Age: Created a new column to calculate the age of the outlet (Current Year - Establishment Year) to analyze the impact of store longevity on sales.
4. Data Reduction:
   • Dropped the Item Visibility column to focus on core sales drivers.

💡 Key Insights & Solutions

Based on the Python analysis and Power BI visualizations, the following insights were generated:

1. Sales Performance by Outlet

• Insight: Supermarket Type1 is the dominant revenue generator, significantly outperforming Grocery Stores and Supermarket Type2.
• Solution: Focus inventory expansion and marketing efforts on "Supermarket Type1" locations as they yield the highest ROI.

2. Product Preference (Fat Content)

• Insight: There is a marked preference for Low Fat products, which generate higher total sales compared to Regular products.
• Solution: Increase the stock of Low Fat variations in high-demand categories (like Snack Foods and Dairy) to prevent stockouts.

3. Outlet Size Efficiency

• Insight: Surprisingly, Medium-sized outlets contribute more to total sales than High-sized outlets.
• Solution: Re-evaluate the operational costs of "High" sized outlets. Medium-sized stores appear to be the "sweet spot" for efficiency and revenue.

4. Correlation Analysis

• Insight: The correlation matrix reveals a weak correlation between Item Weight and Sales, suggesting that packaging size is less of a deciding factor for customers than product type or visibility.

📈 Power BI Dashboard

The final dashboard provides an interactive view of the data, featuring:

• KPI Cards: Total Sales, Average Rating, Number of Items Sold.
• Sales by Item Type: Identifying top-selling categories (Fruits & Vegetables, Snack Foods).
• Outlet Distribution: Sales breakdown by Tier (Location) and Outlet Size.
• Interactive Filters: Slicers for Outlet Type, Year, and Item Category to allow deep-dive analysis.

💻 Technology Stack

• Python: Pandas, NumPy (Data Cleaning & Manipulation)
• Python: Matplotlib, Seaborn (Exploratory Data Analysis & Correlation Heatmaps)
• Power BI: Dashboarding & Data Visualization
• Jupyter Notebook: Environment for coding and testing

🚀 Conclusion

This project successfully transformed raw sales data into a strategic asset. By standardizing data and visualizing key metrics, stakeholders can now make data-driven decisions to boost sales in underperforming outlet types and capitalize on the growing demand for Low Fat products.