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Project Title: Blinkit Grocery Sales Analysis Dashboard
Project Summary:
This interactive dashboard provides a comprehensive analysis of $1.20M in total sales for Blinkit, transforming raw retail data into actionable business intelligence.
Core Focus
Sales Performance: Tracking key KPIs including Average Sales ($141) and Customer Ratings (3.9).
Inventory Insights: Analyzing performance across 8,500+ items, highlighting top categories like Fruits and Snack Foods.
Outlet Analytics: Evaluating revenue distribution across Outlet Sizes (Small/Medium/High) and Geographic Tiers (Tier 1–3).
Trend Mapping: Visualizing establishment growth and outlet types (Grocery vs. Supermarkets) to identify high efficiency locations
Solid breakdown of KPIs — especially tying outlet size and geographic tier into performance.
I’m curious: did you look at whether customer ratings correlate with revenue per outlet tier? Sometimes high-rating stores in Tier 2/3 outperform purely due to loyalty concentration...
Great observations, You’re spot on about the Tier 2/3 loyalty. In this dataset, the high customer ratings in those tiers actually stabilized the lower transaction volume compared to Tier 1. Regarding margins, this specific view focuses on topline revenue growth to identify...
Makes sense focusing first on market share — especially in expansion-phase retail.
If I were layering this further, I’d prioritize:
• Contribution margin by category (not just gross revenue)
• Revenue per square foot by outlet size
• Repeat purchase rate by tier (to validate the...
Excellent suggestions. Moving from gross revenue to category profitability and repeat purchase rates would definitely provide a truer picture of long term loyalty vs short term expansion gains. Your points on pricing elasticity across tiers are especially valuable as a next step for this model. Appreciate the insight
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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.
Most dashboards fail before they are even built.
Not because of bad charts.
Not because of wrong formulas.
Because the data underneath is structurally broken.
Here’s what I see often in small businesses:
• Dates stored as text
• Multiple columns for the same metric
• Inconsistent naming (Revenue / Sales / Total Sales)
• Manual copy-paste every week
• No clear data flow
Then they ask:
“Why doesn’t this dashboard update properly?”
A dashboard is just a mirror.
If the data structure is messy, the reflection will be distorted.
Before I build any report, I focus on 3 things:
Standardized column logic
Single source of truth
Repeatable data flow (no manual dependency)
Clean structure → Reliable metrics → Better decisions.
If your reporting feels fragile, the issue usually isn’t Excel.
It’s the foundation.
Completely agree. Dashboards amplify whatever foundation they sit on — messy data → misleading clarity. Standardization and single source of truth are massively underrated.
Strong hierarchy decisions here.
Invoice builders are tricky — easy to either overcrowd or oversimplify. This sits in a really good middle ground.
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