{
  "format": "dataset-manifest/1",
  "slug": "support-engagement-metrics",
  "version": 3,
  "seed": "support-engagement-metrics:1",
  "engine": 1,
  "engineVersion": "1.0.0",
  "spec": {
    "format": "dataset/1",
    "slug": "support-engagement-metrics",
    "name": "Support Engagement Metrics",
    "tagline": "Support requests, customer interactions, agent performance, and response metrics from customer support teams.",
    "description": "Support teams address customer inquiries and issues through various channels, aiming for effective resolutions while maintaining high satisfaction levels. The dataset captures the essence of these interactions, focusing on support requests initiated by customers, the agents who handle these requests, the products involved, and the responses given. Each row in the support_requests table represents an individual inquiry, detailing the customer's issue, the product in question, and the assigned agent. The customers table lists details about each customer, while the products table outlines the features of the items that may generate support inquiries. Responses detail the communications sent to customers regarding their inquiries, highlighting the interaction history and resolution times. Agents are tracked by their performance metrics, which are essential for understanding team efficiency and effectiveness.\n\nThe relationships between these tables provide a comprehensive view of the customer support process. For instance, a join between support_requests and responses reveals how quickly inquiries are addressed, while linking support_requests to agents shows which agents handle the most complex cases or achieve the highest satisfaction scores. Analyzing this data allows developers to explore critical questions: What are the common issues raised by customers? How do response times impact customer satisfaction? Which agents excel in resolving inquiries, and how can those insights drive team improvement?\n\nThis synthetic dataset is an excellent resource for developers looking to practice data analysis, particularly in scenarios relevant to customer support operations. By exploring the relationships and metrics captured in this data, users can gain insights into workflow efficiencies and customer behavior trends, making it an ideal training ground for honing analytical skills.",
    "domain": "customer-support",
    "shape": "hierarchical",
    "useCases": [
      "sql-practice",
      "teaching"
    ],
    "keywords": [
      "customer support",
      "agent performance",
      "response time",
      "customer satisfaction",
      "support requests",
      "data analysis"
    ],
    "locale": "en",
    "license": "CC0-1.0",
    "version": 1,
    "tables": {
      "support_requests": {
        "description": "Each row represents a support request made by a customer. It links to customers and tracks the inquiry details and timestamps.",
        "rows": 200,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each support request."
          },
          "customer_id": {
            "kind": "fk",
            "ref": "customers.id",
            "description": "Reference to the customer making the request."
          },
          "product_id": {
            "kind": "fk",
            "ref": "products.id",
            "description": "Reference to the product related to the support request."
          },
          "request_time": {
            "kind": "datetime",
            "from": "2023-01-01",
            "to": "2023-12-31",
            "description": "Timestamp of when the support request was made.",
            "dayProfile": "waking"
          },
          "issue_description": {
            "kind": "sentence",
            "max": 200,
            "description": "Description of the issue or inquiry raised by the customer.",
            "nullable": true
          },
          "status": {
            "kind": "enum",
            "values": [
              "open",
              "closed",
              "pending"
            ],
            "weights": [
              50,
              30,
              20
            ],
            "description": "Current status of the support request."
          },
          "priority_level": {
            "kind": "enum",
            "values": [
              "low",
              "medium",
              "high"
            ],
            "weights": [
              60,
              30,
              10
            ],
            "description": "Priority level assigned to the support request."
          }
        }
      },
      "customers": {
        "description": "Each row represents a customer who has engaged with support services. It contains demographic details and satisfaction ratings.",
        "rows": 150,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each customer."
          },
          "full_name": {
            "kind": "fullName",
            "description": "Full name of the customer."
          },
          "email": {
            "kind": "email",
            "from": "full_name",
            "description": "Email address of the customer."
          },
          "satisfaction_rating": {
            "kind": "int",
            "min": 1,
            "max": 5,
            "description": "Customer satisfaction rating based on their support experience.",
            "nullable": true,
            "distribution": "high"
          }
        }
      },
      "products": {
        "description": "This table contains details of the products for which support requests are made, linking to the requests to analyze product-related inquiries.",
        "rows": 20,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each product."
          },
          "product_name": {
            "kind": "productName",
            "description": "Name of the product.",
            "nullable": true,
            "when": {
              "column": "category",
              "in": [
                "Software"
              ]
            }
          },
          "category": {
            "kind": "enum",
            "values": [
              "Software",
              "Hardware",
              "Service"
            ],
            "description": "Category of the product."
          },
          "warranty_period": {
            "kind": "int",
            "min": 1,
            "max": 5,
            "distribution": "normal",
            "description": "Warranty period in years."
          },
          "release_date": {
            "kind": "date",
            "from": "2020-01-01",
            "to": "2023-12-31",
            "description": "Date when the product was released."
          }
        }
      },
      "responses": {
        "description": "Each row represents a response by an agent to a customer support request, including response times and resolution details.",
        "rows": 200,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each response."
          },
          "request_id": {
            "kind": "fk",
            "ref": "support_requests.id",
            "description": "Reference to the support request being responded to."
          },
          "agent_id": {
            "kind": "fk",
            "ref": "agents.id",
            "description": "Reference to the agent who responded."
          },
          "response_time": {
            "kind": "decimal",
            "min": 0,
            "max": 120,
            "scale": 2,
            "description": "Time taken to respond to the support request in minutes.",
            "nullable": true,
            "when": {
              "column": "resolution_outcome",
              "in": [
                "Resolved"
              ]
            },
            "distribution": "normal"
          },
          "resolution_outcome": {
            "kind": "enum",
            "values": [
              "Resolved",
              "Unresolved",
              "Escalated"
            ],
            "description": "Outcome of the response to the support request.",
            "weights": [
              8,
              2,
              1
            ]
          },
          "escalation_reason": {
            "kind": "enum",
            "values": [
              "technical_issue",
              "customer_request",
              "agent_discretion"
            ],
            "weights": [
              50,
              30,
              20
            ],
            "description": "Reason for escalating the support request."
          },
          "resolution_time": {
            "kind": "decimal",
            "min": 1,
            "max": 120,
            "distribution": "long-tail",
            "description": "Time taken to resolve the support request in minutes."
          }
        }
      },
      "agents": {
        "description": "Each row represents a support agent, detailing their performance metrics and engagement levels.",
        "rows": 30,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each agent."
          },
          "full_name": {
            "kind": "fullName",
            "description": "Full name of the agent."
          },
          "total_responses": {
            "kind": "int",
            "min": 0,
            "max": 200,
            "description": "Total number of responses given by the agent.",
            "nullable": true,
            "distribution": "long-tail"
          },
          "average_response_time": {
            "kind": "decimal",
            "min": 0,
            "max": 120,
            "scale": 2,
            "description": "Average time taken by the agent to respond in minutes.",
            "nullable": true,
            "distribution": "normal"
          }
        }
      }
    },
    "structure": "multi-table",
    "traits": [
      "categorical",
      "timestamped",
      "nullable"
    ],
    "analysisQuestions": [
      "What is the average response time for support requests?",
      "How does agent performance vary based on customer satisfaction ratings?",
      "Which product categories generate the most support requests?",
      "What percentage of responses lead to a resolution?",
      "Are there specific times when support requests peak?"
    ],
    "suggestedQueries": [
      {
        "title": "List all support requests with customer names",
        "sql": "SELECT sr.id, c.full_name, sr.issue_description FROM support_requests sr JOIN customers c ON sr.customer_id = c.id",
        "dialect": "postgres"
      },
      {
        "title": "Calculate average response time by agent",
        "sql": "SELECT a.full_name, AVG(r.response_time) AS avg_response_time FROM responses r JOIN agents a ON r.agent_id = a.id GROUP BY a.full_name",
        "dialect": "postgres"
      },
      {
        "title": "Count support requests by product category",
        "sql": "SELECT p.category, COUNT(sr.id) AS request_count FROM support_requests sr JOIN products p ON sr.product_id = p.id GROUP BY p.category",
        "dialect": "postgres"
      },
      {
        "title": "Find resolution rates of support requests",
        "sql": "SELECT COUNT(*) FILTER (WHERE r.resolution_outcome = 'Resolved')::float / COUNT(*) AS resolution_rate FROM responses r",
        "dialect": "postgres"
      },
      {
        "title": "Identify peak support request times",
        "sql": "SELECT DATE(sr.request_time) AS request_date, COUNT(*) AS request_count FROM support_requests sr GROUP BY request_date ORDER BY request_count DESC",
        "dialect": "postgres"
      }
    ],
    "usageScenarios": [
      {
        "title": "Customer Support Training",
        "body": "Support managers can use this dataset to train new customer support representatives. By analyzing various support requests, agents can understand common customer issues and learn how to respond effectively to enhance customer satisfaction."
      },
      {
        "title": "Performance Analysis",
        "body": "Customer support analysts can leverage this data to assess agent performance. By comparing response times and resolution rates among different agents, the organization can identify top performers and areas for improvement in their support team."
      },
      {
        "title": "Product Development Insights",
        "body": "Product managers can utilize this dataset to gain insights into product-specific inquiries. Trends in support requests can guide feature enhancements and address common customer pain points, ultimately improving the overall product experience."
      },
      {
        "title": "Strategic Decision Making",
        "body": "Executives can analyze the overall metrics to make strategic decisions about resource allocation in the support department. Understanding peak times and response outcomes can inform staffing and training initiatives to enhance support quality."
      }
    ]
  },
  "counts": {
    "agents": 30,
    "customers": 150,
    "products": 20,
    "support_requests": 200,
    "responses": 200
  },
  "stats": {
    "support_requests": {
      "rows": 200,
      "columns": {
        "id": {
          "nulls": 0,
          "distinct": 200,
          "min": 1,
          "max": 200
        },
        "customer_id": {
          "nulls": 0,
          "distinct": 116,
          "min": 2,
          "max": 150
        },
        "product_id": {
          "nulls": 0,
          "distinct": 20,
          "min": 1,
          "max": 20
        },
        "request_time": {
          "nulls": 0,
          "distinct": 200,
          "min": "2023-01-01 07:47:26",
          "max": "2023-12-30 18:29:22"
        },
        "issue_description": {
          "nulls": 19,
          "distinct": 181
        },
        "status": {
          "nulls": 0,
          "distinct": 3,
          "min": "closed",
          "max": "pending"
        },
        "priority_level": {
          "nulls": 0,
          "distinct": 3,
          "min": "high",
          "max": "medium"
        }
      }
    },
    "customers": {
      "rows": 150,
      "columns": {
        "id": {
          "nulls": 0,
          "distinct": 150,
          "min": 1,
          "max": 150
        },
        "full_name": {
          "nulls": 0,
          "distinct": 148,
          "min": "Alice Cruz",
          "max": "Zoe Torres"
        },
        "email": {
          "nulls": 0,
          "distinct": 149
        },
        "satisfaction_rating": {
          "nulls": 16,
          "distinct": 5,
          "min": 1,
          "max": 5
        }
      }
    },
    "products": {
      "rows": 20,
      "columns": {
        "id": {
          "nulls": 0,
          "distinct": 20,
          "min": 1,
          "max": 20
        },
        "product_name": {
          "nulls": 14,
          "distinct": 6,
          "min": "Deluxe Umbrella",
          "max": "Ultra Backpack"
        },
        "category": {
          "nulls": 0,
          "distinct": 3,
          "min": "Hardware",
          "max": "Software"
        },
        "warranty_period": {
          "nulls": 0,
          "distinct": 3,
          "min": 2,
          "max": 4
        },
        "release_date": {
          "nulls": 0,
          "distinct": 20,
          "min": "2020-09-10",
          "max": "2023-07-07"
        }
      }
    },
    "responses": {
      "rows": 200,
      "columns": {
        "id": {
          "nulls": 0,
          "distinct": 200,
          "min": 1,
          "max": 200
        },
        "request_id": {
          "nulls": 0,
          "distinct": 135,
          "min": 1,
          "max": 200
        },
        "agent_id": {
          "nulls": 0,
          "distinct": 30,
          "min": 1,
          "max": 30
        },
        "response_time": {
          "nulls": 47,
          "distinct": 150,
          "min": 21.06,
          "max": 109.14
        },
        "resolution_outcome": {
          "nulls": 0,
          "distinct": 3,
          "min": "Escalated",
          "max": "Unresolved"
        },
        "escalation_reason": {
          "nulls": 0,
          "distinct": 3,
          "min": "agent_discretion",
          "max": "technical_issue"
        },
        "resolution_time": {
          "nulls": 0,
          "distinct": 190,
          "min": 1,
          "max": 118.55
        }
      }
    },
    "agents": {
      "rows": 30,
      "columns": {
        "id": {
          "nulls": 0,
          "distinct": 30,
          "min": 1,
          "max": 30
        },
        "full_name": {
          "nulls": 0,
          "distinct": 30,
          "min": "Alice Wright",
          "max": "Wei Miller"
        },
        "total_responses": {
          "nulls": 3,
          "distinct": 22,
          "min": 0,
          "max": 186
        },
        "average_response_time": {
          "nulls": 3,
          "distinct": 27,
          "min": 39.04,
          "max": 87.77
        }
      }
    }
  },
  "artifacts": [
    {
      "file": "csv/agents.csv",
      "format": "csv",
      "table": "agents",
      "key": "datasets/support-engagement-metrics/v3/csv/agents.csv",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/support-engagement-metrics/v3/csv/agents.csv",
      "bytes": 777,
      "sha256": "be6ed9cce8969c4b81b806bdcac0eeaf608a3ed7d557cae8fd32a92886f2c3b4",
      "rows": 30,
      "contentType": "text/csv; charset=utf-8"
    },
    {
      "file": "csv/customers.csv",
      "format": "csv",
      "table": "customers",
      "key": "datasets/support-engagement-metrics/v3/csv/customers.csv",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/support-engagement-metrics/v3/csv/customers.csv",
      "bytes": 6434,
      "sha256": "d7167df2a5334d87ea11882b0998440c95727a21327001e6ffcfd17fd5c2c9b6",
      "rows": 150,
      "contentType": "text/csv; charset=utf-8"
    },
    {
      "file": "csv/products.csv",
      "format": "csv",
      "table": "products",
      "key": "datasets/support-engagement-metrics/v3/csv/products.csv",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/support-engagement-metrics/v3/csv/products.csv",
      "bytes": 656,
      "sha256": "56cd8e5581e2a826fb478ff46bc6d65435ba5e55ff5d486eda758a8d9c1da4d2",
      "rows": 20,
      "contentType": "text/csv; charset=utf-8"
    },
    {
      "file": "csv/support_requests.csv",
      "format": "csv",
      "table": "support_requests",
      "key": "datasets/support-engagement-metrics/v3/csv/support_requests.csv",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/support-engagement-metrics/v3/csv/support_requests.csv",
      "bytes": 18555,
      "sha256": "551573251fcbd36bee2b8aa2b5fe472444ee5f77ffbab0dc0f25085a38149518",
      "rows": 200,
      "contentType": "text/csv; charset=utf-8"
    },
    {
      "file": "csv/responses.csv",
      "format": "csv",
      "table": "responses",
      "key": "datasets/support-engagement-metrics/v3/csv/responses.csv",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/support-engagement-metrics/v3/csv/responses.csv",
      "bytes": 9492,
      "sha256": "8f8bcc2ea51c4ab7bb0ccf31240391db04fdcfc968c1c2234eca761d290c4bc7",
      "rows": 200,
      "contentType": "text/csv; charset=utf-8"
    },
    {
      "file": "support-engagement-metrics.json",
      "format": "json",
      "table": null,
      "key": "datasets/support-engagement-metrics/v3/support-engagement-metrics.json",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/support-engagement-metrics/v3/support-engagement-metrics.json",
      "bytes": 124169,
      "sha256": "8f9d15f1661ea402f8dd99d053e6c4e0b6760530b81b6ab2fede5897c5177985",
      "rows": null,
      "contentType": "application/json; charset=utf-8"
    },
    {
      "file": "sql/postgres.sql",
      "format": "sql-postgres",
      "table": null,
      "key": "datasets/support-engagement-metrics/v3/sql/postgres.sql",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/support-engagement-metrics/v3/sql/postgres.sql",
      "bytes": 48836,
      "sha256": "4e401700f728bede8bb5f620218f3881e212358738b6f156917514df05671f4f",
      "rows": null,
      "contentType": "application/sql; charset=utf-8"
    },
    {
      "file": "sql/mysql.sql",
      "format": "sql-mysql",
      "table": null,
      "key": "datasets/support-engagement-metrics/v3/sql/mysql.sql",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/support-engagement-metrics/v3/sql/mysql.sql",
      "bytes": 48260,
      "sha256": "2b36c1583e8fdeddd3c4dd811283840068134b8aa70547e382af15c19b668a19",
      "rows": null,
      "contentType": "application/sql; charset=utf-8"
    },
    {
      "file": "sql/mssql.sql",
      "format": "sql-mssql",
      "table": null,
      "key": "datasets/support-engagement-metrics/v3/sql/mssql.sql",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/support-engagement-metrics/v3/sql/mssql.sql",
      "bytes": 50157,
      "sha256": "40365a0a41a4affed0d07b99b9277c8b4f60b95a762e34a74bfb9c64d4e7a331",
      "rows": null,
      "contentType": "application/sql; charset=utf-8"
    },
    {
      "file": "sql/sqlite.sql",
      "format": "sql-sqlite",
      "table": null,
      "key": "datasets/support-engagement-metrics/v3/sql/sqlite.sql",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/support-engagement-metrics/v3/sql/sqlite.sql",
      "bytes": 48028,
      "sha256": "76434b1ced493728882bdd07659c58043af91ae89bb7ef5dc156016af83d6055",
      "rows": null,
      "contentType": "application/sql; charset=utf-8"
    },
    {
      "file": "prisma/seed.ts",
      "format": "prisma-seed",
      "table": null,
      "key": "datasets/support-engagement-metrics/v3/prisma/seed.ts",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/support-engagement-metrics/v3/prisma/seed.ts",
      "bytes": 101556,
      "sha256": "bce35dc97be464d808ec620ed2e1755e7a9a33ae2e75a663b94e07d90c5de5ec",
      "rows": null,
      "contentType": "application/typescript; charset=utf-8"
    },
    {
      "file": "django/fixtures.json",
      "format": "django-fixture",
      "table": null,
      "key": "datasets/support-engagement-metrics/v3/django/fixtures.json",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/support-engagement-metrics/v3/django/fixtures.json",
      "bytes": 150563,
      "sha256": "4a9880fe4be839924875a7f1b739ea435692a9a22e0bce627425ef9d2b553f66",
      "rows": null,
      "contentType": "application/json; charset=utf-8"
    }
  ],
  "publishedAt": 1788768523515,
  "updatedAt": 1789096073220
}