{
  "format": "dataset-manifest/1",
  "slug": "engagement-insights",
  "version": 2,
  "seed": "engagement-insights:1",
  "spec": {
    "format": "dataset/1",
    "slug": "engagement-insights",
    "name": "Engagement Insights",
    "tagline": "Viewer participation, chat activity, follower growth, and subscription trends from live streaming platforms.",
    "description": "Content creators engage with their audience on live streaming platforms, driving real-time interaction and building vibrant communities. As creators host their streams, they leverage various engagement metrics to assess viewer participation, chat activity, follower growth, subscription trends, and the frequency of reported issues. By analyzing these metrics, creators can refine their strategies to enhance viewer experiences and foster loyalty.\n\nThe engagement_metrics table serves as the cornerstone of this dataset, containing rows that represent individual streams along with their corresponding engagement statistics. Each row details the date and time of the stream, total viewers, chat messages sent, new followers gained, subscriptions, and any reported issues, such as banned users. This structure allows for meaningful joins to analyze trends over time, compare different streams, and identify factors that contribute to successful interactions.\n\nPracticing with this data enables developers to explore questions around user engagement, such as how chat volume correlates with viewer retention or the impact of subscriber growth on content success. Additionally, the dataset provides opportunities to investigate community dynamics—specifically, how the presence of reported issues affects viewer sentiment and participation. Since the data is synthetic, users can confidently create queries and models without concerns about real-world privacy implications, allowing for an experimental and educational approach to data analytics in the media domain.",
    "domain": "media",
    "shape": "time-series",
    "useCases": [
      "analytics-demo",
      "bi-dashboards"
    ],
    "keywords": [
      "live streaming",
      "viewer engagement",
      "chat activity",
      "follower growth",
      "subscription trends",
      "metrics"
    ],
    "locale": "en",
    "license": "CC0-1.0",
    "version": 1,
    "tables": {
      "engagement_metrics": {
        "description": "This table contains metrics for individual live streams, with each row representing a unique stream event. It links to no other tables, focusing solely on the metrics from that specific stream.",
        "rows": 500,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each stream event."
          },
          "stream_date": {
            "kind": "datetime",
            "from": "2023-01-01",
            "to": "2023-12-31",
            "description": "The date and time when the stream took place.",
            "dayProfile": "waking"
          },
          "viewer_count": {
            "kind": "int",
            "min": 1,
            "max": 10000,
            "description": "Number of viewers watching the stream.",
            "distribution": "high"
          },
          "chat_messages": {
            "kind": "int",
            "min": 0,
            "max": 5000,
            "description": "Total number of chat messages sent during the stream.",
            "nullable": true,
            "distribution": "long-tail"
          },
          "follower_growth": {
            "kind": "int",
            "min": 0,
            "max": 1000,
            "description": "Number of new followers gained during the stream.",
            "distribution": "long-tail"
          },
          "subscriptions": {
            "kind": "int",
            "min": 0,
            "max": 500,
            "description": "Number of new subscriptions received during the stream.",
            "distribution": "long-tail"
          },
          "reported_issues": {
            "kind": "int",
            "min": 0,
            "max": 50,
            "description": "Number of issues reported by viewers during the stream.",
            "nullable": true,
            "distribution": "long-tail"
          },
          "banned_users": {
            "kind": "int",
            "min": 0,
            "max": 20,
            "description": "Number of users banned during the stream.",
            "nullable": true,
            "distribution": "long-tail"
          }
        }
      }
    },
    "structure": "single-table",
    "traits": [
      "timestamped",
      "high-cardinality",
      "monetary"
    ],
    "analysisQuestions": [
      "What is the average number of viewers across all streams?",
      "How does chat activity correlate with follower growth?",
      "Which streams had the highest number of reported issues and why?",
      "Are there trends in subscriptions based on the time of day streams are held?",
      "What percentage of streams resulted in banned users?"
    ],
    "suggestedQueries": [
      {
        "title": "Basic Stream Metrics",
        "sql": "SELECT stream_date, viewer_count, chat_messages FROM engagement_metrics;",
        "dialect": "postgres"
      },
      {
        "title": "Average Viewer Count",
        "sql": "SELECT AVG(viewer_count) AS average_viewers FROM engagement_metrics;",
        "dialect": "postgres"
      },
      {
        "title": "Chat Activity and Follower Growth",
        "sql": "SELECT chat_messages, follower_growth FROM engagement_metrics WHERE viewer_count > 1000;",
        "dialect": "postgres"
      },
      {
        "title": "Streams with Reported Issues",
        "sql": "SELECT stream_date, reported_issues FROM engagement_metrics WHERE reported_issues > 0 ORDER BY reported_issues DESC;",
        "dialect": "postgres"
      },
      {
        "title": "Trend Analysis of Subscriptions",
        "sql": "SELECT EXTRACT(HOUR FROM stream_date) AS hour, SUM(subscriptions) AS total_subscriptions FROM engagement_metrics GROUP BY hour ORDER BY hour;",
        "dialect": "postgres"
      }
    ],
    "usageScenarios": [
      {
        "title": "Content Creator Analysis",
        "body": "Content creators can use this dataset to analyze their own stream performance, identifying patterns in viewer engagement and chat activity. By examining the metrics, they can tailor their content to maximize viewer interaction and increase follower growth."
      },
      {
        "title": "Platform Performance Monitoring",
        "body": "Media analysts at streaming platforms can leverage this data to monitor overall platform performance. They can identify peak viewing times, assess the impact of chat engagement on subscriptions, and address any issues leading to user bans."
      },
      {
        "title": "Marketing Strategy Development",
        "body": "Marketing teams can utilize the insights from this dataset to develop targeted strategies aimed at increasing viewer engagement and subscriptions. By understanding trends in viewer behavior, they can optimize promotional efforts for future streams."
      },
      {
        "title": "Data-Driven Decision Making",
        "body": "Data scientists and analysts can use this dataset in BI tools to create dashboards that visualize key metrics over time, enabling data-driven decision-making for both content creators and platform operators. This will help in strategic planning and improving engagement tactics."
      }
    ]
  },
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    }
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