{
  "format": "dataset-manifest/1",
  "slug": "ecommerce-transactions-overview",
  "version": 1,
  "seed": "ecommerce-transactions-overview:1",
  "spec": {
    "format": "dataset/1",
    "slug": "ecommerce-transactions-overview",
    "name": "Ecommerce Transactions Overview",
    "tagline": "Customer interactions, wishlists, carts, and orders in an online retail environment.",
    "description": "Customers browse through numerous products, assessing their preferences and making purchasing decisions based on various influences. They explore wishlists to save items for future consideration, add products to their carts, and eventually finalize their purchases through orders. This dataset captures the intricate dance between user interactions and product selections within an online retail environment, shedding light on customer behavior and preferences. \n\nThe tables within this dataset connect in meaningful ways: Each row in the customers table represents an individual shopper with unique characteristics, while the products table contains details about the various items available for purchase. The wishlists table tracks products that customers have expressed interest in but have not yet purchased, while the carts table reflects items that customers have considered buying but have not finalized. Finally, the orders table records successful transactions that have been completed. These relationships enable analysis of customer journeys from initial interest to final purchase, highlighting the various touchpoints along the way. \n\nThis dataset allows developers to explore questions such as: What are the most popular product categories? How do seasonal marketing campaigns influence order volume? What is the correlation between wishlist activity and completed purchases? Understanding these dynamics can provide valuable insights into consumer behavior and eCommerce strategies. Additionally, observing the use of discounts and payment methods opens avenues for exploring customer engagement and satisfaction. \n\nPlease note that the data in this dataset is synthetic, constructed to simulate real-world scenarios in eCommerce, offering a rich environment for practice and analysis.",
    "domain": "ecommerce",
    "shape": "transactional",
    "useCases": [
      "sql-practice",
      "orm-seeding"
    ],
    "keywords": [
      "ecommerce",
      "transactions",
      "customers",
      "products",
      "wishlists",
      "carts",
      "orders",
      "analytics"
    ],
    "locale": "en",
    "license": "CC0-1.0",
    "version": 1,
    "tables": {
      "customers": {
        "description": "This table contains records of customers, including their demographic information. Each customer can have multiple orders and wishlist items.",
        "rows": 200,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for the customer."
          },
          "first_name": {
            "kind": "firstName",
            "description": "The customer's first name."
          },
          "last_name": {
            "kind": "lastName",
            "description": "The customer's last name."
          },
          "email": {
            "kind": "email",
            "from": [
              "first_name",
              "last_name"
            ],
            "description": "The customer's email address."
          },
          "signup_date": {
            "kind": "date",
            "from": "2020-01-01",
            "to": "2023-12-31",
            "description": "Date when the customer signed up.",
            "trend": "growing"
          }
        }
      },
      "products": {
        "description": "This table records various products available for sale, including their categories and pricing.",
        "rows": 150,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for the product."
          },
          "name": {
            "kind": "productName",
            "description": "The name of the product."
          },
          "category": {
            "kind": "enum",
            "values": [
              "Electronics",
              "Clothing",
              "Home",
              "Toys",
              "Books"
            ],
            "description": "Category of the product."
          },
          "price": {
            "kind": "decimal",
            "min": 5,
            "max": 500,
            "scale": 2,
            "description": "Price of the product."
          },
          "stock_quantity": {
            "kind": "int",
            "min": 0,
            "max": 1000,
            "description": "Number of items available in stock."
          }
        }
      },
      "wishlists": {
        "description": "This table captures customer wishlists, linking customers to products they are interested in.",
        "rows": 300,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for the wishlist item."
          },
          "customer_id": {
            "kind": "fk",
            "ref": "customers.id",
            "description": "Reference to the customer who created the wishlist."
          },
          "product_id": {
            "kind": "fk",
            "ref": "products.id",
            "description": "Reference to the product in the wishlist."
          },
          "added_date": {
            "kind": "date",
            "from": "2020-01-01",
            "to": "2023-12-31",
            "description": "Date when the product was added to the wishlist.",
            "trend": "seasonal"
          }
        }
      },
      "carts": {
        "description": "This table logs items that customers have added to their shopping carts but have not yet purchased.",
        "rows": 250,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for the cart item."
          },
          "customer_id": {
            "kind": "fk",
            "ref": "customers.id",
            "description": "Reference to the customer who owns the cart."
          },
          "product_id": {
            "kind": "fk",
            "ref": "products.id",
            "description": "Reference to the product in the cart."
          },
          "added_date": {
            "kind": "date",
            "from": "2020-01-01",
            "to": "2023-12-31",
            "description": "Date when the product was added to the cart.",
            "trend": "growing"
          }
        }
      },
      "orders": {
        "description": "This table records completed transactions made by customers, linking them to the purchased products.",
        "rows": 500,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for the order."
          },
          "customer_id": {
            "kind": "fk",
            "ref": "customers.id",
            "description": "Reference to the customer who made the order."
          },
          "product_id": {
            "kind": "fk",
            "ref": "products.id",
            "description": "Reference to the product that was ordered."
          },
          "order_date": {
            "kind": "datetime",
            "from": "2020-01-01",
            "to": "2023-12-31",
            "description": "Date and time when the order was placed.",
            "dayProfile": "waking"
          },
          "quantity": {
            "kind": "int",
            "min": 1,
            "max": 10,
            "description": "Quantity of the product ordered."
          },
          "total_amount": {
            "kind": "decimal",
            "min": 5,
            "max": 5000,
            "scale": 2,
            "description": "Total amount for the order."
          }
        }
      }
    },
    "structure": "multi-table",
    "traits": [
      "categorical",
      "timestamped",
      "monetary"
    ],
    "analysisQuestions": [
      "What are the most popular product categories?",
      "How do seasonal marketing campaigns affect sales?",
      "Which customers frequently use coupons?",
      "What time of day do customers place the most orders?",
      "How does wishlist activity correlate with actual purchases?"
    ],
    "suggestedQueries": [
      {
        "title": "Join Customers with Orders",
        "sql": "SELECT customers.first_name, customers.last_name, orders.order_date FROM customers JOIN orders ON customers.id = orders.customer_id;",
        "dialect": "postgres"
      },
      {
        "title": "Count Orders by Product Category",
        "sql": "SELECT products.category, COUNT(orders.id) AS total_orders FROM orders JOIN products ON orders.product_id = products.id GROUP BY products.category;",
        "dialect": "postgres"
      },
      {
        "title": "Average Order Value Over Time",
        "sql": "SELECT DATE_TRUNC('month', orders.order_date) AS month, AVG(orders.total_amount) AS average_order_value FROM orders GROUP BY month ORDER BY month;",
        "dialect": "postgres"
      },
      {
        "title": "Top Customers by Total Spend",
        "sql": "SELECT customers.first_name, customers.last_name, SUM(orders.total_amount) AS total_spent FROM customers JOIN orders ON customers.id = orders.customer_id GROUP BY customers.id ORDER BY total_spent DESC LIMIT 10;",
        "dialect": "postgres"
      }
    ],
    "usageScenarios": [
      {
        "title": "Data Analysts Exploring Consumer Trends",
        "body": "Data analysts can utilize this dataset to explore consumer trends in ecommerce by analyzing customer interactions across wishlists, carts, and completed orders. They can identify which products are frequently wished for but not purchased, allowing marketers to tailor promotional efforts."
      },
      {
        "title": "Developers Creating ORM Models",
        "body": "Developers can use this dataset as a basis for creating ORM models in their applications. By seeding their databases with this synthetic data, they can test features such as user authentication, product catalog management, and order processing without relying on real customer data."
      },
      {
        "title": "Business Intelligence Professionals Building Dashboards",
        "body": "Business intelligence professionals can leverage this dataset to build dashboards that visualize sales performance, customer behavior, and product trends. By connecting to BI tools, they can create dynamic reports that help stakeholders make informed decisions."
      },
      {
        "title": "Marketing Teams Analyzing Campaign Effectiveness",
        "body": "Marketing teams can analyze the impact of their campaigns by reviewing order data correlated with specific promotional periods. This dataset allows them to assess which products were most popular during sales events and adjust future strategies accordingly."
      }
    ]
  },
  "counts": {
    "customers": 200,
    "products": 150,
    "carts": 250,
    "orders": 500,
    "wishlists": 300
  },
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