SALES DASHBOARD | Bhumika Sharma

GETTING STARTED :

Using my Power BI skills, I've created a dynamic dashboard for analyzing Global Superstore Sales, utilizing the Sample Superstore dataset. This dashboard provides insights into total sales and profit from 2016 to 2019, effectively tracking sales performance while showcasing my ability to visualize data. This analysis provides valuable insights into Superstore sales, making it easy for users to understand complex datasets effortlessly.

PROJECT OVERVIEW :

Topic: Superstore Sales
Data Source: Sample Superstore dataset
Tools Used: Excel & Power BI

SKILLED DEVELOPED :

Importing data from Excel to Power BI Desktop.
Data cleaning to remove null values from columns and rows.
Creating DAX measures with straightforward conditional logic.
Designing an interactive dashboard with various visualizations such as bar charts, doughnut charts, and matrix visuals, along with slicers for detailed data filtering.

EXECUTION STEPS :

Utilized Power Query for data reshaping and conducted ETL operations.
Developed advanced queries using DAX.
Incorporated Key Performance Indicators (KPIs) to visually represent insights such as total orders, total sales, and total profit.
Implemented slicers for detailed regional sales analysis.
Enhanced the dashboard by leveraging customized visualization types, such as doughnut charts, KPIs, matrix tables, line charts, and stacked bar charts, to meet comprehensive analytical needs.

EDUCATIONAL GAINS :

Deepened my practical understanding of Power BI’s extensive data visualization tools.
Enhanced ability to analyze complex datasets.
Crafted a user-friendly interface, enhancing accessibility for varied analytical uses.

This project was transformative for my data-handling skills, enabling me to deliver impactful insights.

OBJECTIVE -

To contribute to the success of a business, I utilize data analysis techniques, particularly focusing on time series analysis, to provide valuable insights and accurate sales forecasting. The interactive dashboard's nature enables users to filter and drill down into the data for deeper insights into specific aspects of the sales process.

DATA EXPORTING -

Data exporting was accomplished by importing Excel worksheets into Power BI.

DATA CLEANING -

To prepare the data for analysis, several cleaning steps were conducted on the raw dataset:

Values Replaced: Occurrences of '#N/A' in the Return column were replaced with '0' to ensure consistency.

Columns Removed: Additionally, two empty columns, labelled 'col1' and 'col2', were identified and removed as they contained no meaningful data.

Following these cleaning procedures, the dataset was ready for dashboard creation.

DASHBOARD VISUALIZATIONS -

CARDS/KPIs -

Key Performance Indicators (KPIs) are essential for gaining an overall understanding of business performance.

This report presents three significant KPIs:

The "Sum of Orders" KPI reflects the total quantity of products sold by the superstore.
The "Sum of Sales" KPI provides an overview of the superstore's total sales.
The "Overall Profit" KPI indicates the total profit generated by the superstore.

DONUT CHARTS -

LINE CHARTS -

The line chart above compares sales by year divided into 4 quarters from 2016 to 2019.

The trend indicates a steady year-over-year increase in sales across the four quarters. While there's a slow start at the beginning of each year, there's a notable uptick towards the end, likely due to festive seasons. To maximize sales, the store could consider launching appealing offers earlier in the year to leverage consumer spending habits.

Just like sales, the year-on-year profit of the superstore is analyzed in the line chart below for the years divided into 4 quarters from 2016 to 2019. In 2016, profit fluctuated significantly, with ups and downs throughout the year. Despite higher sales in the 4th Quarter, profit unexpectedly declined at one point, before spiking again. However, in 2019, profit showed fewer major shifts, with a notable spike at the beginning of the year followed by a gradual upward trend.

STACK BAR CHART -

1. Sales By Ship Modes

In the stacked bar chart, ship mode is compared with sales across furniture, technology, and office supplies. Despite the availability of same-day, first-class, and second-class delivery options, the standard delivery option remains the preferred choice for the majority of customers. This suggests that customers are not inclined to pay extra fees for faster delivery. In response, the store should consider reducing or eliminating these fees to better align with customer preferences and enhance overall satisfaction.

2. Sales by category

When comparing sales across product categories, there isn't a significant difference between the sales generated from technology and furniture category. However, the store is experiencing significantly lower sales margins in the office supplies category.

COLUMN MATRIX -

Total loss by segment

In the column matrix, the analysis of total loss by segment and category reveals that the consumer segment exhibits the highest loss compared to the home office and corporate segments. Furthermore, all three segments encounter greater losses within the furniture category.

SLICERS -

1. Region Slicer :

Slicers allow users to segment the visualization into four parts, representing different regions of the USA for enhanced clarity. Clicking on each region's slicer enables users to focus on the visualization related to that specific region.

2. States Slicer :

Similarly, another slicer is provided in dropdown form, containing all states. This slicer allows users to filter the visualization based on individual states, providing detailed insights for each specific state.

CONCLUSION -

Based on the above visualizations and analysis, several key conclusions can be drawn:

Delivery Preferences : Standard delivery is the preferred option despite other faster delivery choices, prompting a review of delivery fees for faster options.

Segment Profit Analysis: The consumer segment plays a significant role in sales, indicating a need to target this demographic for further growth opportunities.

Sales by Category: Sales margins are relatively consistent between technology and furniture products, but significantly lower for office supplies.

Total Loss Analysis: Consumer segments exhibit the highest losses, particularly in the furniture category, indicating potential areas for improvement in product offerings or pricing strategies.

Seasonal Trends: Sales and profit exhibit seasonality, with peaks during festive seasons, suggesting the effectiveness of promotions during these periods.

Slicer Profit Analysis: The East region yields the highest profits for the Superstore, suggesting opportunities for expansion or investment.

Overall, these insights can guide strategic decisions to capitalize on strengths, address weaknesses, and drive further growth for the Superstore.

Similarly, the comparison of the "Region" column with the "Profit" column, featuring 4 values, illustrates that the West region leads with 38% of total profits.

Lastly, the comparison of the "Category" column with the "Profit" column, encompassing 3 values, underscores the Technology category as the frontrunner, comprising 51% of total profits.

In the depicted chart, the "Segment" column is compared with the "Profit" column, which includes 3 distinct values. The doughnut chart highlights that the Consumer segment dominates profits, contributing to 47% of the total.

Project 1 : Sales Dashboard