Created & Analyzed by Saddam Ansari @Aspiring Data Analyst Linkedin
Live Dashboard at Novypro Live_link_Novypro
As part of my MeriSkill virtual internship, the "Sales Data Analysis" project aims to analyze extensive sales data to uncover valuable insights that can drive business decisions.
The primary objectives include identifying sales trends over time, determining top-selling products, calculating key revenue metrics such as total sales and profit margins, and creating compelling visualizations to effectively communicate the findings.
This project demonstrates proficiency in handling and analyzing large datasets, ultimately providing data-driven recommendations to optimize sales strategies.
This project is crucial for making informed business decisions. For instance, by analyzing sales data, a retail company discovered that its highest sales occurred in December, with winter clothing being the top-seller. This insight allowed the company to increase inventory for winter clothes before the holiday season, boosting sales and profit margins.
Such analysis helps businesses identify trends, optimize inventory, enhance marketing strategies, and ultimately improve profitability. It demonstrates how data-driven insights can lead to actionable recommendations, driving business growth and competitive advantage.
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For this project, I have been provided with a comprehensive dataset of sales from an American electronics store. The dataset contains approximately 186,000 rows and includes various details about each transaction. Here's a glimpse of the dataset:
- Order ID: Unique identifier for each order.
- Product: The name of the product sold.
- Quantity Ordered: Number of units of the product ordered.
- Price Each: Price of a single unit of the product.
- Order Date: Date and time when the order was placed.
- Purchase Address: Address where the product was delivered.
- Month: Month of the order, extracted from the Order Date.
- Sales: Total sales amount for the order (Quantity Ordered * Price Each).
- City: City where the product was delivered, extracted from the Purchase Address.
- Hour: Hour of the day when the order was placed, extracted from the Order Date.
This dataset provides a detailed view of the sales transactions, allowing for a comprehensive analysis of sales performance, customer behavior, and market trends. By leveraging this data, we can derive actionable insights to optimize sales strategies and drive business growth.
In this HR Attrition Analysis project, I utilized a combination of powerful tools to ensure a comprehensive and visually appealing analysis:
- Power BI: Leveraged for data visualization and analysis, creating interactive and insightful dashboards to highlight key trends and patterns in employee attrition.
- Excel: Instrumental in data preparation, used for data profiling, cleaning, and transformation to ensure the dataset was ready for detailed analysis in Power BI.
- PowerPoint: Utilized for creating some of the graphics and visual elements, helping craft a clear and professional presentation of the findings.
- Canva: Used for designing certain elements, enhancing the overall aesthetic appeal of the presentation and dashboards.
- ChatGPT: Assisted in generating content and refining the narrative for the project documentation, ensuring clarity and coherence in the presentation of insights.
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Based on the dataset provided and the project requirements, I have created a comprehensive Power BI dashboard consisting of three pages. Each page serves a specific analytical purpose to provide valuable insights into the sales data.
The first page, titled Overview, contains the main parts of the analysis. This page provides a high-level summary of key metrics and trends in the sales data. It includes visualizations and summaries that highlight overall sales performance, total revenue, and major sales trends over time.
The second page, titled Product Performance, focuses on the sales and performance of individual products. It provides detailed insights into which products are top-sellers, their sales volumes, and revenue contributions. This page helps in understanding product-specific performance and identifying the most and least popular products.
The third page, titled Location Performance, offers insights based on geographical data. It allows for easy comparison of sales performance across different locations. This page includes visualizations that display sales metrics by city, helping to identify regions with the highest and lowest sales and understand location-specific trends.
I will explain each section in more detail in the following sections. Please make sure to read through the entire overview to get a complete understanding of the dashboard and its functionalities.
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I have designed this dashboard to be both visually appealing and easy to understand, ensuring that the insights are accessible and actionable. Here are the key features that make this dashboard stand out:
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User-Friendly Design: The layout is intuitive, making it easy to navigate between different pages and understand the visualizations.
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Detailed Explanations: Each section is accompanied by detailed explanations, providing context and helping users interpret the data correctly.
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High-Quality Visuals: The use of high-quality visuals, including charts, graphs, and maps, makes the data more engaging and easier to comprehend.
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Comprehensive Insights: The dashboard covers various aspects of sales performance, from overall trends to product-specific and location-specific insights.
I am confident that you will find this project unique and insightful, unlike any other project you have seen before. Please take the time to explore each section in detail for a full understanding of the analysis and findings.
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Let's begin by exploring the first page of my dashboard. Below, you can see an image showcasing how Page One is designed.
I aimed to make this page as visually appealing as possible.
On the left-hand side, you will find buttons labeled Dashboard, Product Performance, and Location Performance. These buttons are designed to help you navigate through the different pages of the dashboard easily.
Users can open the filter section by clicking the filter button. Here, you can apply various filters to customize your view. Additionally, there is a "Clear All Filters and Slicers" button to remove any applied filters.
I have created three key performance indicators (KPIs) at the top of the page:
- Total Sales: Indicates the sum of total sales, displayed in dollars. Below it, you can see the month-over-month growth.
- Total Sold Quantity: Indicates the sum of quantities sold, with month-over-month change shown below.
- Total Orders: Indicates the count of total order IDs, with month-over-month growth displayed underneath.
Additionally, there are four secondary KPIs:
- Unique Orders: Indicates the total number of unique order IDs.
- Repeated Orders: Indicates the number of duplicated order IDs.
- Average Sales per Order: Indicates the average sales per order. This KPI is calculated using a simple DAX formula: Total Sales / Total Orders.
- Average Quantity Sold per Order: Indicates the average quantity sold per order.
You can see the monthly trend analysis based on the selected KPI (Total Sales, Total Orders, or Total Quantity). Users can choose which KPI to visualize the monthly trend for by clicking the respective button.
A table displays total sales, quantity, and orders based on the date. This helps in understanding sales performance over time.
Top and Bottom Products: At the bottom, I have included two visuals:
Shows the top five products in terms of sales.
Shows the bottom five products in terms of sales.
This comprehensive layout ensures that all critical metrics and insights are easily accessible and understandable.
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The second page of the dashboard focuses on product-based insights. This page maintains the same design and visual appeal as the first page but includes different visuals to provide a detailed analysis of product performance.
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Similar Design: The overall layout and navigation buttons remain the same as on the first page, ensuring a consistent user experience.
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Sales by Product: This visual shows the sales performance of each product. It helps identify the top-selling products and those that may need attention.
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Total Orders by Product: This visual displays the total number of orders for each product. It provides insight into the demand for each product.
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Total Quantity Sold by Product: This visual illustrates the total quantity sold for each product, helping to understand which products are moving quickly and which are lagging.
For detailed insights and to interact with the visuals, please visit the live dashboard. This product-focused page allows you to drill down into specific product performance metrics, providing valuable insights for inventory management, marketing strategies, and sales optimization.
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The third page of the dashboard focuses on location-based insights. This page also follows the same design and style as the previous pages but is tailored to provide geographical analysis of the sales data.
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Consistent Design: The layout and navigation buttons are consistent with the first and second pages, ensuring an intuitive and seamless user experience.
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City-Wise Values: This page shows values based on city, allowing for a detailed understanding of how sales, quantity sold, and orders are distributed across different locations.
- Map Visualization: Users can click on buttons to see the selected values (Sales, Quantity Sold, and Orders) displayed on a map. This helps in visualizing geographical trends and identifying high-performing and low-performing cities.
- Bar Chart: In addition to the map, users can also view the data in a bar chart format. This provides an alternative visualization to compare city-wise performance easily. To gain a full understanding and interact with these insights, please visit the live dashboard.
This location-focused page helps in identifying regional trends, making it easier to strategize location-specific marketing campaigns, optimize supply chains, and allocate resources effectively.
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In this Sales Data Analysis project, I have leveraged Power BI, Excel, and other tools to create a comprehensive and visually appealing dashboard. The dashboard provides valuable insights into sales performance, product performance, and location-based performance, helping to drive informed business decisions.
By analyzing sales data, I have uncovered trends, identified top-selling products, and provided actionable recommendations for optimizing sales strategies. This project demonstrates my ability to handle and analyze large datasets, create compelling visualizations, and derive data-driven insights.
I hope you find this project insightful and unique. Please visit the live dashboard for an interactive experience and explore the detailed analysis.
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I've successfully completed over 70 Power BI projects, all showcased in my Novypro portfolio. You're all invited to visit my portfolio and explore these amazing projects!
Additionally, I'm currently seeking internship or entry-level opportunities. If you have any opportunities available or need a freelance Power BI project completed, please connect with me on LinkedIn.
Looking forward to connecting with you all!
Saddam Ansari @Aspiring Data Analyst LinkedIn
Location: India
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