{"id":26787086,"url":"https://github.com/kshitiz1302/blinkit_sales_analysis_dashboard","last_synced_at":"2026-01-08T00:05:41.226Z","repository":{"id":269712225,"uuid":"907850894","full_name":"kshitiz1302/Blinkit_Sales_Analysis_Dashboard","owner":"kshitiz1302","description":"Simple and visually interactive PowerBi Sales 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Blinkit Sales Anaysis Dashboard\n\n\n## Problem Statement\n\n- The Blinkit Sales Analysis Power BI project aims to provide an insightful and interactive dashboard for analyzing sales performance across various dimensions. ​\n\n\n- This project will facilitate data-driven decision-making by visualizing key metrics, identifying trends, and uncovering actionable insights within Blinkit's sales data.​\n\n- The project will empower stakeholders with valuable insights into sales performance, enabling informed decision-making and strategic planning.​\n\n\n### Steps followed \n\n- Step 1 : Load data into Power BI Desktop, dataset is a csv file.\n- Step 2 : Open power query editor \u0026 in view tab under Data preview section, check \"column distribution\", \"column quality\" \u0026 \"column profile\" options.\n- Step 3 : Extract, clean, and transform data from various sources to ensure accuracy and consistency.​\n\n- Step 4 : Generate useful and insightful KPIs according to the business requirement.​\n\n- Step 5 : Build the Power BI dashboard with interactive features and visualizations based on the defined requirements.\n\n- step 6 : Create a matrics to insert a proper slicer for whole dashboard that includes all KPIs, charts and many more\n\nfor creating new matrix following DAX expression was written;\n      \n       Metrics = {\n    (\"Total Sales\", NAMEOF('BlinkIT Grocery Data'[Total Sales]), 0),\n    (\"Avg Sales\", NAMEOF('BlinkIT Grocery Data'[Avg Sales]), 1),\n    (\"Avg Rating\", NAMEOF('BlinkIT Grocery Data'[Avg Rating]), 2),\n    (\"No of Items\", NAMEOF('BlinkIT Grocery Data'[No of Items]), 3)\n}\n        \nSnap of new calculated column ,\n\n![Screenshot (1)](https://github.com/user-attachments/assets/dd2c856b-4126-4069-9ebc-050307838d4b)\n\n\n        \n- Step 7 : New measure was created to find total revenue.\n\nFollowing DAX expression was written for the same,\n        \n        Total Sales = SUM('BlinkIT Grocery Data'[Sales])\n        \nA card visual was used to represent count of customers.\n\n![Screenshot (4)](https://github.com/user-attachments/assets/0bb2aabd-58cb-4767-809f-b2424affcbe9)\n\n\n        \n - Step 8 : New measure was created to find average sales,\n \n Following DAX expression was written for the same,\n \n         Avg Sales = AVERAGE('BlinkIT Grocery Data'[Sales])\n \n A card visual was used to represent this value.\n \n\n \n ![Screenshot (5)](https://github.com/user-attachments/assets/88f0e04c-425d-43e8-8487-0e1381968fd3)\n\n\n \n - Step 9 : New measure was created to find out average rating per item.\n \n Following DAX expression was written to find total distance,\n \n         Avg Rating = AVERAGE('BlinkIT Grocery Data'[Rating])\n    \n A card visual was used to represent this average rating.\n \n \n ![Screenshot (6)](https://github.com/user-attachments/assets/914d9a12-daf3-4ae5-a2aa-cfd165ead577)\n \n - Step 10 : New measure was created to find out total number of items\n\n\n         No of Items = COUNTROWS('BlinkIT Grocery Data')\n \n \n![Screenshot (7)](https://github.com/user-attachments/assets/bf685955-01b1-4cbd-b04a-87e1ab114939)\n\n# Snapshot of Dashboard (Power BI Service)\n\n![Screenshot (8)](https://github.com/user-attachments/assets/2bb44b34-c6cd-43e4-a70f-6bf70e2ab8bf)\n\n \n\n\n# Insights\n\nA single page report was created on Power BI Desktop \u0026 it was then published to Power BI Service.\n\nFollowing inferences can be drawn from the dashboard;\n\n### [1] Total Number of Sales = $1.20M\n\n   Highest number of Sales = Fruits \u0026 Vegetables ($178.12K)\n\n   Lowest number of Sales = Seafoods ($9.08K)\n\n   Highest number of Sales by outlet size = Medium size($507.9K)\n\n   Lowset number of Sales by outlet size = High ($248.99K)\n\n\n           \n### [2] Average Ratings\n\n    a) Meat - 4.0/5\n    b) Fruits \u0026 Vegetables - 3.91/5\n    c) Hpousehold - 3.95/5\n    d) Health \u0026 Hygiene - 3.93/5\n    e) Soft Drinks - 3.89/5\n    f) Hard Drinks - 3.84/5\n    g) Baking Goods - 3.95/5\n    h) Frozen Foods - 3.93/5\n    i) Dairy - 3.92/5\n    j) Seafood - 3.91/5\n    k) Starchy Foods - 3.91/5\n    l) Breads - 3.83/5\n    m) Breakfast - 3.90/5\n    n) Snack Foods - 3.90/5\n    o) Canned - 3.95/5\n    p) Others - 3.93/5\n  \n  while calculating average rating, null values have been ignored as they were not relevant for some customers. \n  \n  These ratings will change if different visual filters will be applied.  \n  \n\n ### [3] Some other insights\n \n - Total 8523 items sold by different outlets at different location.​\n\n- Items with Low fat content have the highest sales of 776.32K compared to regular fat i.e., 425.36K.​\n\n- Data reveals that sales reached their peak in 2018, highlighting it as the highest-performing year in terms of revenue generation.​\n \n\n\n 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