4 min to read
Logistic Dashboard
Analyzing logistic, distribution, sales and profitability trends to identify strategic opportunities for enhancing business performance and focusing on the most promising areas for growth.
Overview
The dataset provided contains transactional records from a business, including sales, shipping, customer details, and product information. The data spans various geographic locations, customer segments, and product categories, allowing for an in-depth analysis of business performance across multiple dimensions.
Click this link to directly access the report: Logistic Dashboard
Business Question
- What are the key factors driving sales and profitability across different regions, customer segments, and product categories?
- How does shipping mode impact overall profitability and customer satisfaction?
- Which customer segments and regions should the business focus on to maximize growth and profitability?**
- What trends can be observed in sales and profits over time, and how can these trends be leveraged for strategic planning?
Project Structure
- Data Collection: The dataset was collected from a business’s transactional records and includes key attributes such as order details, customer information, product specifics, and financial metrics.
- Data Processing: Data is processed to ensure accuracy and consistency, with transformations applied to format dates and categorize products.
- Analysis: Exploratory Data Analysis (EDA) is performed to uncover patterns, trends, and insights from the dataset.
- Visualization: Key insights are visualized using charts and dashboards to facilitate data-driven decision-making.
Column Descriptions
- Row ID: Unique identifier for each row in the dataset.
- Order ID: Unique identifier for each order placed by a customer.
- Order Date: Date when the order was placed.
- Ship Date: Date when the order was shipped.
- Ship Mode: Mode of shipment used to deliver the order (e.g., Second Class, Standard Class).
- Customer ID: Unique identifier for each customer.
- Customer Name: Name of the customer.
- Segment: Segment to which the customer belongs (e.g., Consumer, Corporate).
- Country: Country where the order was placed.
- City: City where the order was placed.
- State: State where the order was placed.
- Postal Code: Postal code of the delivery address.
- Region: Geographic region where the order was placed (e.g., West, South).
- Product ID: Unique identifier for each product.
- Category: Category to which the product belongs (e.g., Furniture, Office Supplies).
- Sub-Category: Sub-category of the product.
- Product Name: Name of the product.
- Sales: Sales amount for the order.
- Quantity: Number of units ordered.
- Discount: Discount applied to the order.
- Profit: Profit earned from the order.
Workflow
- Data Import: The dataset is imported into the analysis environment for processing and analysis.
- Data Cleaning: Missing values, duplicates, and inconsistencies are addressed to ensure data quality.
- Data Transformation: Dates are formatted, and categorical variables are encoded for analysis.
- Exploratory Data Analysis (EDA): Patterns and trends are identified using summary statistics and visualizations.
- Modeling: Predictive models may be developed to forecast future sales and profitability based on historical data.
- Visualization: Insights are visualized in Looker Studio to enable stakeholders to make informed decisions.
Exploring the Data
The data is explored by segmenting it based on customer segments, regions, and product categories. The distribution of sales, profits, and discounts is analyzed to identify key trends and outliers. Geographic distribution and the impact of shipping modes on profitability are also explored.
Analyzing Trends
Key trends in the data include:
- Sales and Profit Distribution: Analyzing which regions and customer segments contribute most to sales and profits.
- Impact of Discounts: Understanding how discounts affect profitability across different product categories.
- Shipping Efficiency: Evaluating the effectiveness of different shipping modes on delivery time and customer satisfaction.
- Temporal Trends: Identifying seasonal patterns in sales and profit to inform inventory and marketing strategies.
Visualizations
The data is visualized using the following charts:
- Pie Charts: To show the distribution of orders by category and segment.
- Bar Charts: To display the impact of discounts on different product categories and to compare sales and profits across customer segments.
- Geographic Maps: To visualize sales and profit distribution across different cities and states.
- Time Series Charts: To analyze trends in sales and profits over time.
Conclusion
The analysis reveals that certain regions and customer segments drive the majority of sales and profits. The effectiveness of different shipping modes and the impact of discounts on profitability are also highlighted. These insights can guide strategic decisions in marketing, inventory management, and customer targeting to optimize business performance.
Source
The dataset used in this project can be found at the following link : Logistic Dashboard The dataset used in this project can be found at the following link: Logistic Dataset
