GrowUp Sales Analysis Dashboard (PowerBI)

Sales dashboard using power BI

1. Project Title

GrowUp Sales and Profit Analysis Dashboard

2. Overview

This Power BI dashboard provides an in-depth analysis of sales, profits, customer behavior, and returns for the company GrowUp. It is designed to help stakeholders make data-driven decisions by visualizing key metrics and trends.

Pages Included:

Overview: High-level metrics like total sales, profits, and top products. Sales: Detailed breakdown of sales trends, segments, and shipping. Profit: Profit analysis by category, product, and region. Returns: Insights into product returns and their impact. Customers: Customer segmentation and behavior analysis.

3. Key Features

Returns Analysis: Visualizes product categories with high return rates. Shows the financial impact of returns on profits.

Customer Insights: Highlights customer demographics and purchasing behavior. Analyzes sales contributions from different customer segments.

4. Screenshots

Overview Page

Sales Page

Profit Page

Returns Page

Customers Page

5. How to Use

Clone this repository to your local system. Open the Power BI file or explore screenshots. Interact with the dashboard for actionable insights.

6. Insights and Learnings

Highlight new insights from the Returns and Customers pages: "Returns are highest in the furniture category, impacting profitability." "Consumer segment leads in purchases but shows moderate return rates." "Top customers contribute to 60% of sales, indicating a loyal customer base.

7. Data Cleaning Process

The dataset was preprocessed to ensure accurate and meaningful analysis:

• Removed duplicates and handled missing values.
• Standardized columns for consistency.
• Applied transformations to enhance usability (e.g., date formatting, category merging).

8. DAX (Data Analysis Expressions)

This project incorporates advanced DAX coding to derive meaningful insights:

Created calculated columns and measures, such as:

Date

Date = 
VAR MinYear = YEAR ( MIN ( Orders[Order Date] ) )
VAR MaxYear = YEAR ( MAX ( Orders[Order Date] ) )
RETURN
ADDCOLUMNS (
    FILTER (
        CALENDARAUTO( ),
        AND ( YEAR ( [Date] ) >= MinYear, YEAR ( [Date] ) <= MaxYear )
    ),
    "Calendar Year", YEAR ( [Date] ),
    "Month Name", FORMAT ( [Date], "mmm" ),
    "Month Number", MONTH ( [Date] ),
    "Weekday", FORMAT ( [Date], "dddd" ),
    "Weekday number", WEEKDAY( [Date] ),
    "Quarter", "Q" & TRUNC ( ( MONTH ( [Date] ) - 1 ) / 3 ) + 1,
    "Month and Year", FORMAT ( [Date], "mmmm yyyy" )

Return Rate

Return Rate = DIVIDE([Total Returns], [Total Orders])

New Customer Count

New Customer Count = 
CALCULATE(
    COUNTROWS(Orders),
    FILTER(
        Orders,
        COUNTROWS(
            FILTER(Orders, Orders[Customer ID] = EARLIER(Orders[Customer ID]))
        ) = 1
    )
)

Contributing

Feel free to fork the repository and submit pull requests for improvements.