https://github.com/ashu3291/blinkit-app-store-

conducted a comprehensive analysis of Blinkit's sales performance, customer satisfaction and inventory distribution to improve the sales performance.
https://github.com/ashu3291/blinkit-app-store-

cleaning-data data dataanalysis-projects powerbi-visuals powerbidashboard sql

Last synced: 24 days ago
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conducted a comprehensive analysis of Blinkit's sales performance, customer satisfaction and inventory distribution to improve the sales performance.

• Host: GitHub
• URL: https://github.com/ashu3291/blinkit-app-store-
• Owner: Ashu3291
• Created: 2024-08-20T12:09:43.000Z (6 months ago)
• Default Branch: main
• Last Pushed: 2024-08-20T12:23:35.000Z (6 months ago)
• Last Synced: 2024-11-30T08:27:31.178Z (3 months ago)
• Topics: cleaning-data, data, dataanalysis-projects, powerbi-visuals, powerbidashboard, sql
• Homepage:
• Size: 938 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# blinkit-app-store-

Steps in project – 

•	Requirement gathering / Business requirements

•	Data walkthrough

•	Data connection

•	Data cleaning/ quality check

•	Data modelling

•	Data processing

•	DAX calculations

•	Dashboard lay outing

•	Charts development and formatting

•	Dashboard/ Report development

•	Insights generation

Business Requirement 

To conduct a comprehensive analysis of Blinkit’s sales performance, customer satisfaction and inventory distribution to identify key insights and opportunities for optimization using various KPIs and visualisations in POWER BI. 

KPIs Requirements –

Total sales – the overall revenue generated from all items sold.

Average sales – the average revenue per sale

Number of items – the total count of different items sold

Average rating – the average customer rating for items sold.

Charts requirements – 

1)	Total sales by fat content – 

Objective – analyse the impact of fat content on total sales.

Additional KPI metrics – assess how other KPIs (Average sales, Number of items, avg rating ) vary with fat content.

Chart type – Donut chart 

2)	Total sales by item type – 

Objective – identify the performance of different item type in terms of total sales

Additional KPI metrics – assess how other KPIs (avg sales, number of items, avg rating ) vary with fat content 

Chart type – Bar chart

3)	Fat content by outlet for total sales –

Objective – compare total sales across different outlets segmented by fat content 

Additional KPI metrics – assess how other KPIs (Average sales, Number of items, avg rating) vary with fat content.

Chart type – Stacked column chart  

4)	Total sales by outlet establishment – 

Objective – evaluate how the age or type of outlet establishment influences total sales 

Chart type – Line chart 

5)	Sales by outlet size – 

Objective – analyse the correlation between outlet size and total sales

Chart type – Donut chart 

6)	Sales by outlet location - 

Objective – assess the geographic distribution of sales across different locations.

Chart type – funnel map

7)	All metrics by outlet type – 

Objective – provide a comprehensive view of all key metrics (total sales, avg sales, number of items, avg rating) broken down by different outlet types.

Chart type – Matrix card