{
  "format": "dataset-manifest/1",
  "slug": "event-ticket-resale-platform",
  "version": 1,
  "seed": "event-ticket-resale-platform:1",
  "spec": {
    "format": "dataset/1",
    "slug": "event-ticket-resale-platform",
    "name": "Ticket Resale Insights",
    "tagline": "Profiles of customers, events, venues, payments, and refunds in the ticket resale market for concerts and sports.",
    "description": "Customers browse listings to purchase tickets for concerts, sports events, and theater performances, engaging with a platform that facilitates ticket resale. Each customer interacts with various events, ranging from local shows to major concerts, creating a marketplace filled with diverse choices. Profiles of these customers reveal their preferences and behaviors, shedding light on how demand fluctuates based on venue and artist popularity. Venues play a crucial role in this ecosystem, as they host events that attract specific demographics, influencing ticket sales and resale dynamics.\n\nThe data includes several interconnected tables: 'customers' captures individual user information, detailing their demographics and purchasing habits; 'events' provides insights into the specifics of each ticket listing, including event type, date, and location; 'venues' associates events with the physical locations where they occur, highlighting their unique characteristics; 'payments' tracks the transaction details, including payment methods and amounts; and 'refunds' records instances where customers return tickets, revealing trends in buyer satisfaction and market volatility. By establishing relationships between these tables, one can discern patterns such as the types of events that lead to successful sales versus those that result in refunds.\n\nThis dataset answers critical questions about customer behavior, ticket demand, and market trends in the ticket resale industry. It enables analysis of factors that influence ticket purchases and the likelihood of refunds, helping users understand the dynamics of event attendance and seller reliability. The data is synthetic, created to mimic real-world scenarios, providing a safe environment for developers to practice and build their data analysis skills.",
    "domain": "customer-support",
    "shape": "hierarchical",
    "useCases": [
      "sql-practice",
      "teaching"
    ],
    "keywords": [
      "ticket resale",
      "customer profiles",
      "event listings",
      "payment methods",
      "refunds",
      "seller verification"
    ],
    "locale": "en",
    "license": "CC0-1.0",
    "version": 1,
    "tables": {
      "customers": {
        "description": "This table holds information about customers who browse and purchase tickets. Each row represents a unique customer profile linked to their transactions.",
        "rows": 250,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each customer."
          },
          "full_name": {
            "kind": "fullName",
            "description": "The full name of the customer."
          },
          "email": {
            "kind": "email",
            "from": "full_name",
            "description": "Customer's email address."
          },
          "registration_date": {
            "kind": "date",
            "from": "2021-01-01",
            "to": "2023-10-31",
            "description": "Date when the customer registered.",
            "trend": "growing"
          },
          "loyalty_points": {
            "kind": "int",
            "min": 0,
            "max": 1000,
            "description": "Points accumulated through purchases."
          }
        }
      },
      "events": {
        "description": "This table features details about events available for ticket resale. Each row corresponds to a specific event listing, linked to its venue and performer.",
        "rows": 300,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each event."
          },
          "event_name": {
            "kind": "words",
            "min": 3,
            "max": 10,
            "description": "Name of the event."
          },
          "venue_id": {
            "kind": "fk",
            "ref": "venues.id",
            "description": "Foreign key linking to the venue of the event."
          },
          "performer": {
            "kind": "words",
            "min": 2,
            "max": 5,
            "description": "Name of the performer or team."
          },
          "event_date": {
            "kind": "date",
            "from": "2023-01-01",
            "to": "2023-12-31",
            "description": "Date when the event takes place.",
            "trend": "seasonal"
          },
          "tickets_available": {
            "kind": "int",
            "min": 1,
            "max": 500,
            "description": "Number of tickets available for resale."
          }
        }
      },
      "venues": {
        "description": "This table includes details about various venues where events occur. Each row represents a unique venue linked to its events.",
        "rows": 100,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each venue."
          },
          "venue_name": {
            "kind": "words",
            "min": 2,
            "max": 4,
            "description": "Name of the venue."
          },
          "location": {
            "kind": "city",
            "description": "City where the venue is located."
          },
          "capacity": {
            "kind": "int",
            "min": 100,
            "max": 20000,
            "description": "Total seating capacity of the venue."
          }
        }
      },
      "payments": {
        "description": "This table captures various payment methods used by customers for their ticket purchases. Each row is linked to a customer.",
        "rows": 250,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each payment record."
          },
          "customer_id": {
            "kind": "fk",
            "ref": "customers.id",
            "description": "Foreign key linking to the customer making the payment."
          },
          "payment_method": {
            "kind": "enum",
            "values": [
              "credit_card",
              "paypal",
              "bank_transfer"
            ],
            "description": "Method of payment used by the customer."
          },
          "amount": {
            "kind": "decimal",
            "min": 10,
            "max": 300,
            "scale": 2,
            "description": "Total amount paid for the tickets."
          },
          "payment_date": {
            "kind": "datetime",
            "from": "2023-01-01",
            "to": "2023-10-31",
            "description": "Date and time of the payment.",
            "dayProfile": "waking"
          }
        }
      },
      "refunds": {
        "description": "This table tracks refund requests made by customers. Each row links to a payment and contains details about the refund process.",
        "rows": 50,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each refund request."
          },
          "payment_id": {
            "kind": "fk",
            "ref": "payments.id",
            "description": "Foreign key linking to the related payment."
          },
          "refund_amount": {
            "kind": "decimal",
            "min": 5,
            "max": 300,
            "scale": 2,
            "description": "Amount to be refunded."
          },
          "refund_date": {
            "kind": "datetime",
            "from": "2023-01-01",
            "to": "2023-10-31",
            "description": "Date and time when the refund was requested.",
            "dayProfile": "waking"
          },
          "status": {
            "kind": "enum",
            "values": [
              "pending",
              "approved",
              "rejected"
            ],
            "description": "Current status of the refund request."
          }
        }
      }
    },
    "structure": "multi-table",
    "traits": [
      "nullable",
      "timestamped",
      "categorical",
      "high-cardinality"
    ],
    "analysisQuestions": [
      "What payment methods are most commonly used by customers?",
      "How does the number of tickets available affect event popularity?",
      "What are the trends in refund requests over time?",
      "Which venues host the most events throughout the year?",
      "How do loyalty points correlate with total spending?"
    ],
    "suggestedQueries": [
      {
        "title": "Join Customers and Payments",
        "sql": "SELECT customers.full_name, payments.amount FROM customers JOIN payments ON customers.id = payments.customer_id;",
        "dialect": "postgres"
      },
      {
        "title": "Count Events by Venue",
        "sql": "SELECT venues.venue_name, COUNT(events.id) AS event_count FROM venues JOIN events ON venues.id = events.venue_id GROUP BY venues.venue_name;",
        "dialect": "postgres"
      },
      {
        "title": "Average Refund Amount by Status",
        "sql": "SELECT status, AVG(refund_amount) AS avg_refund FROM refunds GROUP BY status;",
        "dialect": "postgres"
      },
      {
        "title": "Popular Payment Methods by Event",
        "sql": "SELECT events.event_name, payments.payment_method, COUNT(payments.id) AS payment_count FROM events JOIN payments ON events.id = payments.event_id GROUP BY events.event_name, payments.payment_method;",
        "dialect": "postgres"
      }
    ],
    "usageScenarios": [
      {
        "title": "Data Analysts Exploring Trends",
        "body": "Data analysts can utilize this dataset to explore customer behavior and trends in the ticket resale market. By analyzing customer profiles and payment methods, they can identify patterns that may influence marketing strategies and improve customer engagement."
      },
      {
        "title": "Teaching SQL to Students",
        "body": "Educators can leverage this dataset in classroom settings to teach SQL skills. Students can practice creating queries to analyze ticket sales, refunds, and customer interactions, enhancing their understanding of relational databases and data manipulation."
      },
      {
        "title": "Customer Support Insights",
        "body": "Customer support teams can analyze refund requests and payment methods to enhance their service offerings. By understanding common issues that lead to refunds, they can identify areas for improvement in the ticket purchasing process."
      },
      {
        "title": "Event Organizers Evaluating Performance",
        "body": "Event organizers can use this dataset to evaluate the performance of their events. By comparing ticket sales across different venues and performers, they can make data-driven decisions for future events, ensuring they meet audience expectations."
      }
    ]
  },
  "counts": {
    "customers": 250,
    "payments": 250,
    "refunds": 50,
    "venues": 100,
    "events": 300
  },
  "stats": {
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