Bike-Sales-Analysis-Dashboard

Introduction:

This Power BI project aims to provide a comprehensive sales analysis using the provided dataset. The dataset contains information such as the date, customer details, product information, order quantity, unit cost, unit price, profit, cost, and revenue. By leveraging Power BI's data visualization capabilities, we will create an interactive and insightful dashboard that enables users to gain valuable insights into sales performance, customer behavior, and product trends.

Data Source:

The dataset consists of the following columns:

Date: The date of the sales transaction. Day: The day of the week. Month: The month of the sales transaction. Year: The year of the sales transaction. Customer_Age: The age of the customer. Age_Group: The age group the customer belongs to. Customer_Gender: The gender of the customer. Country: The country where the sales transaction occurred. State: The state where the sales transaction occurred. Product_Category: The broad category of the product. Sub_Category: The specific sub-category of the product. Product: The name of the product. Order_Quantity: The quantity of the product ordered. Unit_Cost: The cost per unit of the product. Unit_Price: The price per unit of the product. Profit: The profit generated from the sales transaction. Cost: The total cost of the product. Revenue: The total revenue generated from the sales transaction. Dashboard Objectives:

Sales Overview:

• Provide an overview of sales performance by visualizing total revenue, profit, and cost across different time periods (day, month, year).
• Customer Analysis: Analyze customer demographics by age group, gender, and country, and identify the most valuable customer segments.
• Product Analysis: Explore sales performance by product category, sub-category, and individual products, identifying top-selling items and product trends.
• Geographic Analysis: Visualize sales performance by country and state, identifying the most lucrative regions and potential growth opportunities.
• Sales Trends: Identify sales trends over time, highlighting peak seasons, and understanding how sales performance has evolved.
• Key Metrics: Present key metrics such as average order quantity, unit cost, unit price, and profit margin to gain deeper insights into the business's financial performance.

Implementation Plan:

• Data Import and Transformation: Import the dataset into Power BI, perform necessary data cleaning, and transform it into a structured format suitable for analysis.
• Data Modeling: Create relationships between tables, define measures, and calculate derived fields to facilitate dynamic analysis.
• Dashboard Design: Design an intuitive and visually appealing dashboard layout with multiple pages to accommodate different analytical perspectives.
• Visualizations: Utilize a variety of Power BI visualizations (e.g., charts, graphs, maps) to present the data in an interactive and insightful manner.
• Filters and Slicers: Implement filters and slicers to allow users to slice and dice the data based on various dimensions (e.g., time, customer, product).
• Drill-Down and Drill-Through: Enable drill-down and drill-through functionalities to provide users with detailed information at different levels of granularity.
• Interactivity: Utilize Power BI's interactive features to enable users to explore the data by interacting with the visualizations.
• Performance Optimization: Optimize the dashboard's performance by applying appropriate data loading techniques and data aggregation methods.
• Testing and Validation: Validate the accuracy of the dashboard by cross-referencing with the original dataset and ensuring the visualizations provide meaningful insights.
• Deployment and Training: Publish the Power BI dashboard to a suitable platform and provide necessary training to end-users on how to effectively utilize the dashboard for sales analysis.

Insights:

• The highest revenue, profit, and cost was recorded in 2015.
• Between 2011 and 2016, the company was able to get 1,333,705 orders which resulted in total revenue of $94,688,588 and a total profit of $41,08,055 from 6 different countries.
• Adult age category generated 66% of the total revenue while the female gender generated 51% of the total revenue.
• From 2011- 2014, adult females generated the highest revenue, then in 2016 adult male generated the highest revenue. This shows that adult females are the largest consumer of the company’s products so far.

Conclusion:

This Power BI project aims to create a comprehensive sales analysis dashboard using the provided dataset. By visualizing the sales data through various perspectives, the dashboard will enable users to gain valuable insights into sales performance, customer behavior, and product trends. The interactive nature of the dashboard will empower users to make data-driven decisions, identify growth opportunities, and optimize sales strategies.