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.​