{
  "format": "dataset-manifest/1",
  "slug": "sales-activity-network",
  "version": 3,
  "seed": "sales-activity-network:1",
  "engine": 1,
  "engineVersion": "1.0.0",
  "spec": {
    "format": "dataset/1",
    "slug": "sales-activity-network",
    "name": "Sales Activity Network",
    "tagline": "Sales activities, individuals, organizations, invoices, and lead labels from a marketing firm's operations.",
    "description": "A marketing firm transforms leads into loyal customers through meticulous tracking of sales activities. Individuals and organizations are at the heart of this operation, providing the raw data from which successful marketing strategies are crafted. Invoices track the financial transactions that result from these marketing efforts, while lead labels signify varying levels of engagement and interest from potential customers. The firm navigates through popular industry sectors, where a high volume of leads converge, while also exploring niche fields that may show sparse engagement. Each row in the activities table typically reflects a unique interaction, illustrating how relationships evolve and sales processes unfold across a diverse range of teams and goals.\n\nThe tables in this dataset interconnect seamlessly; the individuals table holds records of sales representatives and their interactions, which relate to the organizations table that identifies the businesses being targeted. Activities are linked to both individuals and organizations, providing a comprehensive view of the sales process in action. Invoices connect to organizations, detailing the revenue generated from successful engagements. Lead labels categorize leads based on their interaction levels, and industry sectors highlight the market segmentation for targeted marketing strategies.\n\nWith this dataset, developers can explore a variety of questions: How do engagement levels vary across different industries? What patterns emerge from the sales activities of individuals? How does the conversion rate correlate with lead labels? This synthetic data offers a rich landscape for practicing data analysis, machine learning models, or developing visualizations that help understand the dynamics of sales activities. By modeling real-world marketing processes, users can gain insights into effective strategies and enhance their analytical skills.",
    "domain": "marketing",
    "shape": "graph-edges",
    "useCases": [
      "sql-practice",
      "teaching"
    ],
    "keywords": [
      "sales",
      "leads",
      "activities",
      "marketing",
      "invoices",
      "engagement",
      "network",
      "data analysis"
    ],
    "locale": "en",
    "license": "CC0-1.0",
    "version": 1,
    "tables": {
      "individuals": {
        "description": "This table represents individuals involved in sales activities, linking to their respective organizations and activities.",
        "rows": 250,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each individual."
          },
          "full_name": {
            "kind": "fullName",
            "description": "The full name of the individual.",
            "nullable": true
          },
          "email": {
            "kind": "email",
            "from": "full_name",
            "description": "Contact email of the individual.",
            "nullable": true
          },
          "organization_id": {
            "kind": "fk",
            "ref": "organizations.id",
            "description": "References the organization this individual belongs to."
          },
          "phone": {
            "kind": "phone",
            "description": "Contact phone number of the individual."
          }
        }
      },
      "organizations": {
        "description": "This table contains organizations that the individuals belong to, detailing their industry and engagement level.",
        "rows": 100,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each organization."
          },
          "name": {
            "kind": "company",
            "suffixes": [
              "Solutions",
              "Group",
              "Systems",
              "Services",
              "Technologies",
              "Dynamics",
              "Innovations",
              "Partners",
              "Consulting",
              "Analytics",
              "Ventures",
              "Media",
              "Capital",
              "Design",
              "Digital"
            ],
            "description": "Name of the organization."
          },
          "industry": {
            "kind": "enum",
            "values": [
              "Technology",
              "Finance",
              "Healthcare",
              "Education",
              "Retail"
            ],
            "description": "Industry category of the organization.",
            "weights": [
              6,
              4,
              3,
              2,
              1
            ]
          },
          "engagement_level": {
            "kind": "decimal",
            "min": 0,
            "max": 1,
            "scale": 2,
            "description": "Engagement level from 0.00 to 1.00.",
            "nullable": true,
            "when": {
              "column": "industry",
              "in": [
                "Technology",
                "Finance",
                "Healthcare",
                "Education",
                "Retail"
              ]
            },
            "distribution": "high"
          }
        }
      },
      "activities": {
        "description": "This table holds details about sales activities performed by individuals, linking them to the respective leads.",
        "rows": 500,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each activity."
          },
          "individual_id": {
            "kind": "fk",
            "ref": "individuals.id",
            "description": "The individual who performed the activity."
          },
          "activity_type": {
            "kind": "enum",
            "values": [
              "Call",
              "Email",
              "Meeting",
              "Follow-up"
            ],
            "description": "Type of activity performed.",
            "weights": [
              5,
              4,
              3,
              2
            ]
          },
          "date": {
            "kind": "datetime",
            "from": "2023-01-01",
            "to": "2023-12-31",
            "description": "Date of the activity.",
            "dayProfile": "waking"
          },
          "duration_minutes": {
            "kind": "int",
            "min": 5,
            "max": 120,
            "distribution": "normal",
            "description": "Duration of the activity in minutes."
          }
        }
      },
      "invoices": {
        "description": "This table captures invoices generated for engagements, linking them to organizations and activities.",
        "rows": 200,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each invoice."
          },
          "organization_id": {
            "kind": "fk",
            "ref": "organizations.id",
            "description": "The organization associated with the invoice."
          },
          "activity_id": {
            "kind": "fk",
            "ref": "activities.id",
            "description": "The activity that led to this invoice."
          },
          "amount": {
            "kind": "decimal",
            "min": 50,
            "max": 5000,
            "scale": 2,
            "description": "Total amount of the invoice.",
            "nullable": true,
            "distribution": "long-tail"
          },
          "issue_date": {
            "kind": "datetime",
            "from": "2023-01-01",
            "to": "2023-12-31",
            "description": "Date when the invoice was issued.",
            "nullable": true,
            "dayProfile": "business",
            "trend": "growing"
          },
          "due_date": {
            "kind": "datetime",
            "from": "2023-01-01",
            "to": "2023-12-31",
            "description": "Date when the invoice payment is due."
          }
        }
      },
      "lead_labels": {
        "description": "This table captures labels assigned to leads based on their engagement level and interest.",
        "rows": 150,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each lead label."
          },
          "label": {
            "kind": "words",
            "min": 1,
            "max": 3,
            "description": "Label assigned to the lead.",
            "nullable": true,
            "pool": [
              "hot",
              "warm",
              "cold",
              "new",
              "follow-up",
              "qualified",
              "unqualified",
              "interested",
              "not_interested",
              "pending",
              "converted",
              "archived",
              "active",
              "inactive",
              "potential",
              "priority",
              "low",
              "medium",
              "high",
              "urgent"
            ]
          },
          "individual_id": {
            "kind": "fk",
            "ref": "individuals.id",
            "description": "The lead associated with this label."
          }
        }
      },
      "industry_sectors": {
        "description": "This table outlines various industry sectors and their respective engagement metrics.",
        "rows": 20,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each industry sector."
          },
          "sector": {
            "kind": "words",
            "min": 1,
            "max": 3,
            "description": "Name of the industry sector.",
            "pool": [
              "technology",
              "healthcare",
              "finance",
              "education",
              "retail",
              "manufacturing",
              "real_estate",
              "telecommunications",
              "transportation",
              "energy",
              "agriculture",
              "construction",
              "hospitality",
              "media",
              "pharmaceutical",
              "automotive",
              "nonprofit",
              "government",
              "consulting",
              "insurance"
            ]
          },
          "average_engagement": {
            "kind": "decimal",
            "min": 0,
            "max": 1,
            "scale": 2,
            "description": "Average engagement level in this sector.",
            "distribution": "high"
          }
        }
      }
    },
    "structure": "multi-table",
    "traits": [
      "categorical",
      "timestamped",
      "monetary",
      "nullable"
    ],
    "analysisQuestions": [
      "What types of activities lead to higher engagement levels?",
      "Which industries have the highest average invoice amounts?",
      "How does engagement level vary across different sectors?",
      "What is the relationship between activity types and lead conversion rates?"
    ],
    "suggestedQueries": [
      {
        "title": "Join Individuals and Organizations",
        "sql": "SELECT i.full_name, o.name FROM individuals i JOIN organizations o ON i.organization_id = o.id;",
        "dialect": "postgres"
      },
      {
        "title": "Count Activities by Type",
        "sql": "SELECT activity_type, COUNT(*) FROM activities GROUP BY activity_type;",
        "dialect": "postgres"
      },
      {
        "title": "Average Invoice Amounts by Organization",
        "sql": "SELECT o.name, AVG(i.amount) FROM invoices i JOIN organizations o ON i.organization_id = o.id GROUP BY o.name;",
        "dialect": "postgres"
      },
      {
        "title": "Engagement Trends Over Time",
        "sql": "SELECT DATE_TRUNC('month', a.date) AS month, COUNT(*) FROM activities a GROUP BY month ORDER BY month;",
        "dialect": "postgres"
      }
    ],
    "usageScenarios": [
      {
        "title": "Sales Training",
        "body": "Sales trainers can utilize this dataset to simulate real-world scenarios for training purposes. By analyzing activities and their outcomes, trainees can learn about effective engagement strategies and the impact of various activity types on lead conversion."
      },
      {
        "title": "Marketing Strategy Development",
        "body": "Marketing analysts can leverage the dataset to identify trends in lead engagement across different industries. This analysis will help them develop targeted marketing strategies and allocate resources more effectively based on engagement trends."
      },
      {
        "title": "SQL Practice for Analysts",
        "body": "Data analysts looking to sharpen their SQL skills can use this dataset for practice. The diverse tables and relationships allow for complex queries, helping analysts become proficient in data manipulation and extraction techniques."
      }
    ]
  },
  "counts": {
    "industry_sectors": 20,
    "organizations": 100,
    "individuals": 250,
    "activities": 500,
    "invoices": 200,
    "lead_labels": 150
  },
  "stats": {
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