https://github.com/kshitiz1302/blinkit_sales_analysis_dashboard
Simple and visually interactive PowerBi Sales Dashboard
https://github.com/kshitiz1302/blinkit_sales_analysis_dashboard
interactive-dashboards interactive-visualizations powerbi powerbi-custom-visuals powerbi-dashboards powerbi-desktop powerbi-report powerbi-visuals
Last synced: 5 months ago
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Simple and visually interactive PowerBi Sales Dashboard
- Host: GitHub
- URL: https://github.com/kshitiz1302/blinkit_sales_analysis_dashboard
- Owner: kshitiz1302
- Created: 2024-12-24T14:48:44.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-12T13:09:45.000Z (over 1 year ago)
- Last Synced: 2025-01-12T14:24:13.780Z (over 1 year ago)
- Topics: interactive-dashboards, interactive-visualizations, powerbi, powerbi-custom-visuals, powerbi-dashboards, powerbi-desktop, powerbi-report, powerbi-visuals
- Homepage:
- Size: 1.81 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Blinkit Sales Anaysis Dashboard
## Problem Statement
- The Blinkit Sales Analysis Power BI project aims to provide an insightful and interactive dashboard for analyzing sales performance across various dimensions.
- This project will facilitate data-driven decision-making by visualizing key metrics, identifying trends, and uncovering actionable insights within Blinkit's sales data.
- The project will empower stakeholders with valuable insights into sales performance, enabling informed decision-making and strategic planning.
### Steps followed
- Step 1 : Load data into Power BI Desktop, dataset is a csv file.
- Step 2 : Open power query editor & in view tab under Data preview section, check "column distribution", "column quality" & "column profile" options.
- Step 3 : Extract, clean, and transform data from various sources to ensure accuracy and consistency.
- Step 4 : Generate useful and insightful KPIs according to the business requirement.
- Step 5 : Build the Power BI dashboard with interactive features and visualizations based on the defined requirements.
- step 6 : Create a matrics to insert a proper slicer for whole dashboard that includes all KPIs, charts and many more
for creating new matrix following DAX expression was written;
Metrics = {
("Total Sales", NAMEOF('BlinkIT Grocery Data'[Total Sales]), 0),
("Avg Sales", NAMEOF('BlinkIT Grocery Data'[Avg Sales]), 1),
("Avg Rating", NAMEOF('BlinkIT Grocery Data'[Avg Rating]), 2),
("No of Items", NAMEOF('BlinkIT Grocery Data'[No of Items]), 3)
}
Snap of new calculated column ,
- Step 7 : New measure was created to find total revenue.
Following DAX expression was written for the same,
Total Sales = SUM('BlinkIT Grocery Data'[Sales])
A card visual was used to represent count of customers.
- Step 8 : New measure was created to find average sales,
Following DAX expression was written for the same,
Avg Sales = AVERAGE('BlinkIT Grocery Data'[Sales])
A card visual was used to represent this value.
- Step 9 : New measure was created to find out average rating per item.
Following DAX expression was written to find total distance,
Avg Rating = AVERAGE('BlinkIT Grocery Data'[Rating])
A card visual was used to represent this average rating.
- Step 10 : New measure was created to find out total number of items
No of Items = COUNTROWS('BlinkIT Grocery Data')
# Snapshot of Dashboard (Power BI Service)
# Insights
A single page report was created on Power BI Desktop & it was then published to Power BI Service.
Following inferences can be drawn from the dashboard;
### [1] Total Number of Sales = $1.20M
Highest number of Sales = Fruits & Vegetables ($178.12K)
Lowest number of Sales = Seafoods ($9.08K)
Highest number of Sales by outlet size = Medium size($507.9K)
Lowset number of Sales by outlet size = High ($248.99K)
### [2] Average Ratings
a) Meat - 4.0/5
b) Fruits & Vegetables - 3.91/5
c) Hpousehold - 3.95/5
d) Health & Hygiene - 3.93/5
e) Soft Drinks - 3.89/5
f) Hard Drinks - 3.84/5
g) Baking Goods - 3.95/5
h) Frozen Foods - 3.93/5
i) Dairy - 3.92/5
j) Seafood - 3.91/5
k) Starchy Foods - 3.91/5
l) Breads - 3.83/5
m) Breakfast - 3.90/5
n) Snack Foods - 3.90/5
o) Canned - 3.95/5
p) Others - 3.93/5
while calculating average rating, null values have been ignored as they were not relevant for some customers.
These ratings will change if different visual filters will be applied.
### [3] Some other insights
- Total 8523 items sold by different outlets at different location.
- Items with Low fat content have the highest sales of 776.32K compared to regular fat i.e., 425.36K.
- Data reveals that sales reached their peak in 2018, highlighting it as the highest-performing year in terms of revenue generation.