https://github.com/anjalikumari021/ecommerce_sales_dashboard_using_powerbi
Developed an insightful Ecommerce Sales dashboard using Power BI to provide insights.
https://github.com/anjalikumari021/ecommerce_sales_dashboard_using_powerbi
charts dashboard data-cleaning data-visualization powerbi
Last synced: 3 months ago
JSON representation
Developed an insightful Ecommerce Sales dashboard using Power BI to provide insights.
- Host: GitHub
- URL: https://github.com/anjalikumari021/ecommerce_sales_dashboard_using_powerbi
- Owner: AnjaliKumari021
- Created: 2024-05-13T15:58:59.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-05-15T13:30:05.000Z (about 1 year ago)
- Last Synced: 2025-01-14T08:10:08.242Z (5 months ago)
- Topics: charts, dashboard, data-cleaning, data-visualization, powerbi
- Homepage:
- Size: 12.8 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# PowerBI - Ecommerce_Sales_Dashboard
## Objective
The main aim of this dashboard and reports is to calculate various kpi, analyze sales performance, brand activity, customer behavior and the effectiveness of promotional strategies across different products and categories. These analysis provide valuable insights into various aspects of business performance and help in making data-driven decisions.
## Steps followed:
#### 1. Data Gathering:
* Importing raw data .csv files (Sales_Data and Promotion_Data) into Power BI & transform to Power Query editor for cleaning and data processing.
#### 2. Data cleaning:
* Cleaning is done by removing empty column, removing duplicates.
* Checking data type of every column and customising date column for consistent entries.
#### 3. Data processing:
* New columns is created like month, quarter and year from the date column.
* Further a new column puchase is created using DAX expression .
#### 4. Data analysis:
* Analysis involves the creation of a range of visual representations, including bar charts, key performance indicators (KPIs), pie charts,donut chart and other relevant visualizations.
* These tools are utilized to understand data better and to present it in a clear and simple way.
## Key Questions of the Dashboard
```
How is price varying by brand/category/time/channel?
Is traffic varied by day/time/channel?
Define and calculate high level metrics like (Revenue,potential revenue, products, categories) by month, time, state and channel?
How is the search behavior like brand search by category/ category search by brand?
How is effect of Special Promotions?
How pricing fluctuations effecting sales?
```
## Key Insights
* In terms of customer spending, Apple is the brand that sells the most products overall.
* People invest the most in electronic goods out of all the categories, giving the e-commerce business revenues of almost $5 million.
* The least popular categories include sporting goods, children's products, accessories, and stationery.
* Between the hours of the morning (5 a.m.) and the evening (5 p.m.), users visit the e-commerce website via browser and app.
* The busiest days of the week for channel traffic are Friday, Saturday, and Sunday, and traffic is roughly evenly spread throughout all channels.
## Dashboard
