{
  "format": "dataset-manifest/1",
  "slug": "e-commerce-logistics-analytics",
  "version": 2,
  "seed": "e-commerce-logistics-analytics:1",
  "spec": {
    "format": "dataset/1",
    "slug": "e-commerce-logistics-analytics",
    "name": "E-commerce Logistics Analytics",
    "tagline": "Customer interactions, product orders, shipment details, and support inquiries from an online retail environment.",
    "description": "Customers browse and purchase a diverse array of products in a vast online retail environment, engaging with items from electronics to clothing. Their interactions are documented through detailed wishlists, orders, and various product categories, illustrating the dynamic nature of e-commerce. Alongside these customer activities, the logistics of shipping are carefully tracked, capturing details such as shipment statuses and seller interactions. Customer support inquiries are also logged, providing insights into the challenges and queries that arise during and after the purchasing process.\n\nThe tables in this dataset are interconnected to provide a comprehensive view of the business's operations. Each row in the customers table represents an individual customer, detailing their preferences and purchasing habits. The products table contains entries for each item available in the store, including categories and specifications. Orders connect customers to their selected products, with each row detailing the specifics of individual transactions. Shipments link orders to their logistics, tracking the journey of products from seller to customer. Lastly, support inquiries capture the issues and questions raised by customers, providing a window into their experiences and concerns. \n\nThis dataset is particularly useful for practitioners looking to answer questions such as: What are the most popular product categories? How do shipping times affect customer satisfaction? What trends can be observed in support inquiries related to specific products? Analyzing this data allows for practice in real-world scenarios of e-commerce logistics, helping to refine skills in data analysis, customer behavior understanding, and operational efficiency. It’s important to note that all data within this dataset is synthetic, designed to provide a realistic yet fictional representation of e-commerce logistics operations.",
    "domain": "logistics",
    "shape": "star-schema",
    "useCases": [
      "bi-dashboards",
      "analytics-demo"
    ],
    "keywords": [
      "e-commerce",
      "logistics",
      "customer interactions",
      "orders",
      "shipments",
      "products",
      "support inquiries"
    ],
    "locale": "en",
    "license": "CC0-1.0",
    "version": 1,
    "tables": {
      "customers": {
        "description": "This table records customer demographics and engagement metrics. Each row represents a unique customer identified by a customer ID.",
        "rows": 300,
        "columns": {
          "customer_id": {
            "kind": "pk",
            "description": "A unique identifier for each customer."
          },
          "full_name": {
            "kind": "fullName",
            "description": "The full name of the customer."
          },
          "email": {
            "kind": "email",
            "from": "full_name",
            "description": "The email address associated with the customer."
          },
          "registration_date": {
            "kind": "date",
            "from": "2020-01-01",
            "to": "2023-01-01",
            "description": "The date the customer registered.",
            "trend": "growing"
          },
          "loyalty_points": {
            "kind": "int",
            "min": 0,
            "max": 1000,
            "description": "The loyalty points accumulated by the customer.",
            "distribution": "high"
          }
        }
      },
      "products": {
        "description": "This table contains details about products available in the e-commerce platform. Each row represents a unique product.",
        "rows": 200,
        "columns": {
          "product_id": {
            "kind": "pk",
            "description": "A unique identifier for each product."
          },
          "product_name": {
            "kind": "productName",
            "description": "The name of the product."
          },
          "category": {
            "kind": "enum",
            "values": [
              "electronics",
              "clothing",
              "home",
              "toys",
              "books"
            ],
            "description": "The category to which the product belongs."
          },
          "price": {
            "kind": "decimal",
            "min": 5,
            "max": 500,
            "scale": 2,
            "description": "The price of the product.",
            "distribution": "long-tail"
          },
          "stock_quantity": {
            "kind": "int",
            "min": 0,
            "max": 1000,
            "description": "The quantity of the product available in stock."
          }
        }
      },
      "orders": {
        "description": "This table captures details about customer orders. Each row represents a unique order associated with a customer.",
        "rows": 800,
        "columns": {
          "order_id": {
            "kind": "pk",
            "description": "A unique identifier for each order."
          },
          "customer_id": {
            "kind": "fk",
            "ref": "customers.customer_id",
            "description": "The ID of the customer who placed the order."
          },
          "order_date": {
            "kind": "datetime",
            "from": "2021-01-01",
            "to": "2023-01-01",
            "description": "The date and time when the order was placed.",
            "dayProfile": "waking"
          },
          "total_price": {
            "kind": "decimal",
            "min": 10,
            "max": 2000,
            "scale": 2,
            "description": "The total price of the order.",
            "distribution": "long-tail"
          },
          "coupon_applied": {
            "kind": "bool",
            "trueRate": 0.3,
            "description": "Whether a coupon was applied to the order."
          }
        }
      },
      "shipments": {
        "description": "This table tracks shipment details for orders. Each row represents a shipment related to an order.",
        "rows": 800,
        "columns": {
          "shipment_id": {
            "kind": "pk",
            "description": "A unique identifier for each shipment."
          },
          "order_id": {
            "kind": "fk",
            "ref": "orders.order_id",
            "description": "The ID of the order associated with this shipment."
          },
          "shipment_date": {
            "kind": "datetime",
            "from": "2021-01-01",
            "to": "2023-01-01",
            "description": "The date and time when the shipment was processed.",
            "dayProfile": "business"
          },
          "delivery_status": {
            "kind": "enum",
            "values": [
              "pending",
              "shipped",
              "delivered",
              "returned"
            ],
            "description": "The current status of the shipment."
          },
          "shipping_partner": {
            "kind": "enum",
            "values": [
              "FedEx",
              "UPS",
              "DHL",
              "USPS"
            ],
            "description": "The logistics partner handling the shipment."
          }
        }
      },
      "support_inquiries": {
        "description": "This table logs customer support inquiries related to their purchases. Each row represents a unique inquiry.",
        "rows": 400,
        "columns": {
          "inquiry_id": {
            "kind": "pk",
            "description": "A unique identifier for each support inquiry."
          },
          "customer_id": {
            "kind": "fk",
            "ref": "customers.customer_id",
            "description": "The ID of the customer who made the inquiry."
          },
          "inquiry_date": {
            "kind": "datetime",
            "from": "2021-01-01",
            "to": "2023-01-01",
            "description": "The date and time when the inquiry was made.",
            "dayProfile": "waking",
            "trend": "seasonal"
          },
          "issue_type": {
            "kind": "enum",
            "values": [
              "order_status",
              "product_issue",
              "refund",
              "general_question"
            ],
            "description": "The type of issue raised by the customer."
          },
          "resolution_status": {
            "kind": "enum",
            "values": [
              "resolved",
              "pending",
              "escalated"
            ],
            "description": "The resolution status of the inquiry."
          }
        }
      }
    },
    "structure": "multi-table",
    "traits": [
      "timestamped",
      "categorical",
      "nullable"
    ],
    "analysisQuestions": [
      "What are the most common issues raised by customers in support inquiries?",
      "Which product categories have the highest sales volume?",
      "How does the delivery status affect customer satisfaction?",
      "What trends can be observed in order placements over time?",
      "How do coupon applications influence total order values?"
    ],
    "suggestedQueries": [
      {
        "title": "Join Orders and Customers",
        "sql": "SELECT o.order_id, c.full_name FROM orders o JOIN customers c ON o.customer_id = c.customer_id;",
        "dialect": "postgres"
      },
      {
        "title": "Count Orders by Product Category",
        "sql": "SELECT p.category, COUNT(o.order_id) AS order_count FROM orders o JOIN products p ON o.product_id = p.product_id GROUP BY p.category;",
        "dialect": "postgres"
      },
      {
        "title": "Average Delivery Time by Shipping Partner",
        "sql": "SELECT s.shipping_partner, AVG(EXTRACT(EPOCH FROM (s.delivery_status - o.order_date)) / 3600) AS avg_delivery_time FROM shipments s JOIN orders o ON s.order_id = o.order_id GROUP BY s.shipping_partner;",
        "dialect": "postgres"
      },
      {
        "title": "Monthly Inquiry Trends",
        "sql": "SELECT DATE_TRUNC('month', i.inquiry_date) AS month, COUNT(i.inquiry_id) AS inquiry_count FROM support_inquiries i GROUP BY month ORDER BY month;",
        "dialect": "postgres"
      }
    ],
    "usageScenarios": [
      {
        "title": "Business Intelligence Dashboard",
        "body": "Data analysts can use this dataset to create dynamic dashboards that visualize customer behavior, order trends, and support inquiries. By integrating this data into BI tools, analysts can derive insights that help in strategic decision-making and operational improvements."
      },
      {
        "title": "Customer Support Analysis",
        "body": "Customer support teams can analyze the types of inquiries and their resolutions to improve service quality. By identifying common issues and response times, they can enhance customer satisfaction and streamline support processes."
      },
      {
        "title": "Sales and Marketing Strategies",
        "body": "Marketing teams can utilize sales data to identify popular products and effective coupon strategies. This analysis can guide promotional campaigns and inventory management, ensuring that high-demand items are adequately stocked."
      },
      {
        "title": "Logistics Performance Evaluation",
        "body": "Logistics managers can assess shipment performance based on delivery times and partner efficiency. By analyzing this data, they can optimize shipping operations and improve the overall customer experience."
      }
    ]
  },
  "counts": {
    "customers": 300,
    "orders": 800,
    "products": 200,
    "shipments": 800,
    "support_inquiries": 400
  },
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