https://github.com/sarathchandranpm/walmart-sales-analysis

Analysis of Walmart Myanmar's Q1 2019 sales data covering customer behavior, product performance, general operations, and sales patterns.
https://github.com/sarathchandranpm/walmart-sales-analysis

data-analysis mysql sql

Last synced: about 1 month ago
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Analysis of Walmart Myanmar's Q1 2019 sales data covering customer behavior, product performance, general operations, and sales patterns.

• Host: GitHub
• URL: https://github.com/sarathchandranpm/walmart-sales-analysis
• Owner: SarathchandranPM
• Created: 2024-12-24T16:26:04.000Z (about 2 months ago)
• Default Branch: main
• Last Pushed: 2024-12-25T15:39:21.000Z (about 2 months ago)
• Last Synced: 2024-12-25T16:27:09.548Z (about 2 months ago)
• Topics: data-analysis, mysql, sql
• Homepage:
• Size: 45.9 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
## Project Summary: Walmart Sales Analysis

### File Analysis

#### 1. `General Analysis`

Examines operational metrics:

- Geographic presence (cities, branches)

- Payment method popularity

- Revenue analysis:

  - Monthly revenue trends

  - City-wise revenue ranking

- COGS tracking by month

- Uses window functions for ranking

- Complex aggregations for financial metrics

#### 2. `Customer Analysis`

Focuses on customer demographics and satisfaction:

- Customer type distribution and preferences

- Gender analysis by branch

- Rating analysis by:

  - Time of day

  - Day of week

  - Branch

  - Gender

  - Customer type

- Uses pivot tables and case statements for cross-tabulation

- Implements aggregate functions for average ratings

#### 3. `Product Analysis`

Analyzes product performance:

- Product line metrics:

  - Sales volume

  - Revenue generation

  - VAT impact

- Performance categorization (Good/Poor)

- Gender-based preferences

- Customer satisfaction by product

- Implements subqueries and window functions

- Creates product performance matrices

#### 4. `Aales Analysis`

Investigates sales patterns:

- Temporal analysis:

  - Time of day patterns

  - Day of week trends

- Customer type revenue analysis

- VAT analysis by:

  - Customer type

  - City

- Uses grouping and aggregation

- Revenue pattern identification