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Data Analytics Project | BlinkIT Grocery Sales Analysis
Excited to share my latest Data Analytics project ,where I analyzed BlinkIT Grocery Sales Data and delivery data to uncover meaningful business insights 📈
📊 What I worked on:
Analyzed sales performance across product categories, outlet types, sizes, and locations
Identified top-performing item categories and customer preferences
Studied the impact of outlet size, establishment year, and visibility on sales
Converted raw data into actionable insights and business recommendations
💡 Key Insights:
Fruits & Vegetables, Snack Foods, and Frozen Foods drive maximum sales
Medium-sized outlets outperform others in overall revenue
FOR FULL PROJECT VIST GIT HUB –( https://lnkd.in/d34sdAPz
)
Feedback and suggestions are always welcome!
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
Related posts
Data Analytics Project | BlinkIT Grocery Sales Analysis
Excited to share my latest Data Analytics project ,where I analyzed BlinkIT Grocery Sales Data and delivery data to uncover meaningful business insights 📈
📊 What I worked on:
Analyzed sales performance across product categories, outlet types, sizes, and locations
Identified top-performing item categories and customer preferences
Studied the impact of outlet size, establishment year, and visibility on sales
Converted raw data into actionable insights and business recommendations
💡 Key Insights:
Fruits & Vegetables, Snack Foods, and Frozen Foods drive maximum sales
FOR FULL PROJECT VIST GIT HUB –( https://lnkd.in/d34sdAPz
)
Project Title:
Sales Performance Dashboard Using Power BI
Description Example:
Designed an interactive Power BI dashboard to track sales performance, revenue trends and monthly KPIs. Automated data transformation using Power Query and built dynamic Excel models for reporting accuracy.
Tools Used:
• Microsoft Excel
• Power Query
• Power BI
Clean layout.
Curious — did you design the data model in a star schema, or were the relationships handled directly inside Power BI?
In my experience, long-term maintainability depends more on the model layer than the visuals.
Strong breakdown of churn drivers.
Have you looked at cohort-based churn instead of aggregate churn rate?
Sometimes retention patterns by acquisition month reveal structural issues earlier than overall churn trends.
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