{
  "format": "dataset-manifest/1",
  "slug": "affiliate-marketing-tracker",
  "version": 1,
  "seed": "affiliate-marketing-tracker:1",
  "spec": {
    "format": "dataset/1",
    "slug": "affiliate-marketing-tracker",
    "name": "Affiliate Performance Insights",
    "tagline": "Performance metrics, campaign details, and user feedback from various affiliate marketing campaigns.",
    "description": "Affiliate marketing connects advertisers with publishers to amplify sales and brand visibility. This dataset models the intricate ecosystem of affiliate marketing, tracking essential performance metrics like clicks, conversions, and user feedback. It provides insights into various affiliate campaigns, illustrating how clicks often peak during shopping seasons while conversions highlight the success of targeted strategies. With diverse entries reflecting user engagement patterns and financial transactions related to merchant payments, this synthetic dataset offers a valuable resource for businesses aiming to optimize their affiliate strategies. The relational structure allows for comprehensive analysis across multiple dimensions of the affiliate marketing process, making it ideal for analytics and business intelligence dashboards.",
    "domain": "marketing",
    "shape": "time-series",
    "useCases": [
      "analytics-demo",
      "bi-dashboards"
    ],
    "keywords": [
      "affiliate marketing",
      "performance metrics",
      "campaign analysis",
      "clicks",
      "conversions",
      "payments",
      "data insights"
    ],
    "locale": "en",
    "license": "CC0-1.0",
    "version": 1,
    "tables": {
      "affiliates": {
        "description": "Each row represents an affiliate with unique identifiers and engagement details, linked to various campaigns through foreign keys.",
        "rows": 200,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for the affiliate."
          },
          "name": {
            "kind": "fullName",
            "description": "Full name of the affiliate."
          },
          "email": {
            "kind": "email",
            "from": "name",
            "description": "Email address of the affiliate.",
            "nullable": true
          },
          "created_at": {
            "kind": "date",
            "from": "2023-01-01",
            "to": "2024-12-31",
            "description": "Date the affiliate was added to the system."
          }
        }
      },
      "campaigns": {
        "description": "This table captures the details of each marketing campaign, including its duration and associated affiliates.",
        "rows": 150,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for the campaign."
          },
          "affiliate_id": {
            "kind": "fk",
            "ref": "affiliates.id",
            "description": "Reference to the affiliate running the campaign."
          },
          "name": {
            "kind": "words",
            "min": 3,
            "max": 10,
            "description": "Name of the campaign."
          },
          "start_date": {
            "kind": "date",
            "from": "2023-01-01",
            "to": "2024-12-31",
            "description": "Start date of the campaign."
          },
          "end_date": {
            "kind": "date",
            "after": "start_date",
            "withinDays": 60,
            "description": "End date of the campaign.",
            "nullable": true
          }
        }
      },
      "performance_metrics": {
        "description": "Metrics related to affiliate performance for specific campaigns, detailing user interactions over time.",
        "rows": 600,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for the performance metric entry."
          },
          "campaign_id": {
            "kind": "fk",
            "ref": "campaigns.id",
            "description": "Reference to the campaign this metric relates to."
          },
          "date": {
            "kind": "date",
            "from": "2023-01-01",
            "to": "2024-12-31",
            "description": "Date of the recorded metrics."
          },
          "clicks": {
            "kind": "int",
            "min": 0,
            "max": 5000,
            "description": "Number of clicks received on the campaign."
          },
          "conversions": {
            "kind": "int",
            "min": 0,
            "max": 500,
            "description": "Number of conversions resulting from the clicks.",
            "nullable": true
          }
        }
      },
      "payments": {
        "description": "Financial transactions related to affiliate payouts, linking each payment to a specific affiliate and campaign.",
        "rows": 300,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for the payment record."
          },
          "affiliate_id": {
            "kind": "fk",
            "ref": "affiliates.id",
            "description": "Reference to the affiliate receiving the payment."
          },
          "campaign_id": {
            "kind": "fk",
            "ref": "campaigns.id",
            "description": "Reference to the campaign related to the payment."
          },
          "amount": {
            "kind": "decimal",
            "min": 10,
            "max": 1000,
            "scale": 2,
            "description": "Amount of money paid to the affiliate."
          },
          "payment_date": {
            "kind": "date",
            "from": "2023-01-01",
            "to": "2024-12-31",
            "description": "Date when the payment was made.",
            "nullable": true
          }
        }
      },
      "user_feedback": {
        "description": "Feedback from users regarding their experience with the affiliate campaigns, providing qualitative insights.",
        "rows": 400,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for the feedback entry."
          },
          "campaign_id": {
            "kind": "fk",
            "ref": "campaigns.id",
            "description": "Reference to the campaign this feedback pertains to."
          },
          "user_id": {
            "kind": "uuid",
            "description": "Unique identifier for the user providing feedback."
          },
          "feedback": {
            "kind": "sentence",
            "min": 5,
            "max": 200,
            "description": "User's feedback on the campaign.",
            "nullable": true
          },
          "submitted_at": {
            "kind": "datetime",
            "from": "2023-01-01",
            "to": "2024-12-31",
            "description": "Date and time the feedback was submitted.",
            "dayProfile": "waking"
          }
        }
      }
    },
    "structure": "multi-table",
    "traits": [
      "timestamped",
      "monetary",
      "free-text",
      "high-cardinality"
    ],
    "analysisQuestions": [
      "What are the peak times for clicks and conversions in affiliate campaigns?",
      "How does user feedback correlate with conversion rates?",
      "Which affiliates generate the highest revenue through their campaigns?",
      "What is the average payout per campaign for affiliates?",
      "Are there specific campaigns that consistently perform better than others?"
    ],
    "suggestedQueries": [
      {
        "title": "Join Campaigns and Affiliates",
        "sql": "SELECT campaigns.name, affiliates.name FROM campaigns JOIN affiliates ON campaigns.affiliate_id = affiliates.id;",
        "dialect": "postgres"
      },
      {
        "title": "Aggregate Clicks by Campaign",
        "sql": "SELECT campaigns.name, SUM(performance_metrics.clicks) AS total_clicks FROM performance_metrics JOIN campaigns ON performance_metrics.campaign_id = campaigns.id GROUP BY campaigns.name;",
        "dialect": "postgres"
      },
      {
        "title": "Conversion Rates by Date",
        "sql": "SELECT date, SUM(conversions) / NULLIF(SUM(clicks), 0) AS conversion_rate FROM performance_metrics GROUP BY date ORDER BY date;",
        "dialect": "postgres"
      },
      {
        "title": "Payments Analysis",
        "sql": "SELECT affiliates.name, SUM(payments.amount) AS total_paid FROM payments JOIN affiliates ON payments.affiliate_id = affiliates.id GROUP BY affiliates.name;",
        "dialect": "postgres"
      },
      {
        "title": "Feedback Trends Over Time",
        "sql": "SELECT campaigns.name, COUNT(user_feedback.id) AS feedback_count FROM user_feedback JOIN campaigns ON user_feedback.campaign_id = campaigns.id GROUP BY campaigns.name ORDER BY feedback_count DESC;",
        "dialect": "postgres"
      }
    ],
    "usageScenarios": [
      {
        "title": "Marketing Analyst",
        "body": "A marketing analyst uses the dataset to monitor affiliate performance across various campaigns. By analyzing clicks and conversions over time, they identify trends and peak periods, optimizing future campaigns accordingly."
      },
      {
        "title": "Business Intelligence Developer",
        "body": "A BI developer integrates the data into dashboards, allowing stakeholders to visualize affiliate performance metrics. This helps in making informed decisions regarding budget allocations and campaign strategies."
      },
      {
        "title": "Affiliate Manager",
        "body": "An affiliate manager leverages the dataset to evaluate the effectiveness of individual affiliates. By reviewing performance metrics and payments, they can identify high-performing affiliates and tailor incentives to boost performance."
      },
      {
        "title": "Data Scientist",
        "body": "A data scientist employs machine learning techniques on the dataset to predict future trends in affiliate marketing. By analyzing historical data, they develop models that forecast clicks and conversions based on various factors."
      }
    ]
  },
  "counts": {
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    "campaigns": 150,
    "payments": 300,
    "performance_metrics": 600,
    "user_feedback": 400
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