https://github.com/shivamsharma32/-grocery-chain-analysis
Sales Analysis and Customer Insights for a Multi-Department Grocery Chain
https://github.com/shivamsharma32/-grocery-chain-analysis
Last synced: 2 months ago
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Sales Analysis and Customer Insights for a Multi-Department Grocery Chain
- Host: GitHub
- URL: https://github.com/shivamsharma32/-grocery-chain-analysis
- Owner: shivamsharma32
- Created: 2024-06-13T02:47:04.000Z (12 months ago)
- Default Branch: main
- Last Pushed: 2024-06-13T02:55:17.000Z (12 months ago)
- Last Synced: 2025-02-07T22:23:05.720Z (4 months ago)
- Size: 2.8 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
**Sales Analysis and Customer Insights for a Multi-Department Grocery Chain**
**Problem Statement**
The project aimed to enhance sales analysis and customer insights for a multi-department grocery chain operating across diverse locations. The goal was to maximize store sales, improve customer experience, and optimize promotional strategies through data-driven analysis. Key objectives included identifying sales trends, understanding customer demographics, and evaluating the effectiveness of various promotional activities.
**Approach:**
**1. Data Import and Cleaning:**
o Imported the dataset into Power BI.
o Ensured data quality by handling missing values, duplicates, and formatting issues.
**2. Data Exploration and Transformation:**
o Conducted exploratory data analysis to understand the data structure and identify key metrics.
o Created calculated columns and measures to derive meaningful insights.
**3. Data Analysis:**
o Total Records: Counted the total number of records in the dataset.
o Unique Values: Identified unique values for the 'promotion_name' and 'sales_country' columns.
o Summary Statistics: Calculated mean, median, and standard deviation for 'store_sales'.
o Common Values: Determined the most common gender and education level.
o Averages: Computed the average 'unit_sales' and 'cost' for different 'media_type' values.
o Distribution and Proportions: Analyzed the distribution of 'marital_status' and proportion of houseowners.
o Missing Values: Identified columns with missing values.
o Range Analysis: Determined the range of 'avg_yearly_income'.
o Correlations: Assessed correlations between numerical columns.
o Counts and Averages: Counted records for 'low_fat' and 'recyclable_package' products and computed average 'net_weight' for these categories.
o Sales Variations and Totals: Analyzed 'store_sales' variations across different 'food_departments' and calculated total 'store_sales' for each 'store_city'.
4. Visualization and Dashboard Creation:
o Created interactive dashboards in Power BI featuring charts, tables, and slicers.
o Visualized key insights such as sales trends, customer demographics, and promotion effectiveness.
o Designed the dashboard to enable easy filtering and drill-down capabilities for detailed analysis.
**Conclusion:**
The Power BI project successfully provided actionable insights into the grocery chain's sales performance and customer behavior. Key findings included the identification of the most effective promotions, understanding of customer demographics, and pinpointing areas for sales improvement. The interactive dashboard facilitated data-driven decision-making, enabling the company to optimize its sales strategies, enhance customer satisfaction, and ultimately drive higher revenue.
**Dashboards Overlook**
