{ "format": "dataset-manifest/1", "slug": "ai-driven-interactive-digital-marketing", "version": 4, "seed": "ai-driven-interactive-digital-marketing:1", "engine": 1, "engineVersion": "1.0.0", "spec": { "format": "dataset/1", "slug": "ai-driven-interactive-digital-marketing", "name": "Dynamic Marketing Engagement", "tagline": "Campaigns, content variants, user interactions, and performance metrics for AI-driven marketing strategies.", "description": "A digital marketing agency drives engagement through advanced AI by creating interactive and personalized campaigns. This dataset models how the agency tests various content variants across channels like email and web, while analyzing user interactions. The focus on A/B testing helps optimize strategies, providing insights into which content resonates best with audiences. The tables include campaigns, content variants, interactions, users, performance metrics, and channels. Each of these tables interconnects to give a comprehensive view of the marketing strategies employed. Users can analyze how different content variants perform across channels, identify which templates are most engaging, and evaluate the effectiveness of their campaigns. This synthetic dataset allows users to practice SQL skills, analyze marketing performance, and develop ETL pipelines to extract actionable insights.", "domain": "marketing", "shape": "event-log", "useCases": [ "analytics-demo", "etl-pipelines" ], "keywords": [ "digital marketing", "AI", "A/B testing", "user engagement", "content variants", "performance metrics", "campaign analysis" ], "locale": "en", "license": "CC0-1.0", "version": 1, "tables": { "campaigns": { "description": "Each row represents a marketing campaign launched by the agency, linking to various content variants and channels used.", "rows": 150, "columns": { "id": { "kind": "pk", "description": "Unique identifier for each campaign." }, "name": { "kind": "words", "min": 1, "max": 3, "description": "The name of the campaign.", "pool": [ "launch", "growth", "engagement", "strategy", "promotion", "awareness", "conversion", "retargeting", "branding", "influence", "impact", "visibility", "outreach", "expansion", "connect", "reach", "experience", "insight", "innovation", "success" ] }, "start_date": { "kind": "date", "from": "2023-01-01", "to": "2023-12-31", "description": "The date when the campaign started.", "trend": "growing" }, "end_date": { "kind": "date", "from": "2023-01-01", "to": "2023-12-31", "description": "The date when the campaign ended.", "nullable": true, "when": { "column": "status", "in": [ "completed" ] }, "trend": "growing" }, "status": { "kind": "enum", "values": [ "active", "inactive", "completed" ], "description": "Current status of the campaign.", "weights": [ 8, 2, 1 ] } } }, "content_variants": { "description": "Each row details a specific variant of content used in campaigns, linking to the campaign it belongs to.", "rows": { "per": "campaigns", "via": "campaign_id", "min": 2, "max": 5, "skew": "long-tail" }, "columns": { "id": { "kind": "pk", "description": "Unique identifier for each content variant." }, "campaign_id": { "kind": "fk", "ref": "campaigns.id", "description": "The campaign to which this content variant belongs." }, "template": { "kind": "words", "min": 1, "max": 3, "description": "Template used for the content variant.", "nullable": true, "when": { "column": "variant_type", "in": [ "email", "banner" ] }, "pool": [ "email", "banner", "video", "social", "landing", "ad", "post", "story", "carousel", "infographic", "article", "webinar", "case", "guide", "quiz", "survey", "report", "template", "strategy", "campaign" ] }, "variant_type": { "kind": "enum", "values": [ "email", "banner", "social_media" ], "description": "Type of content variant.", "weights": [ 5, 3, 2 ] }, "audience_targeting": { "kind": "enum", "values": [ "general", "specific", "retargeting" ], "weights": [ 50, 30, 20 ], "description": "Target audience for the content variant." }, "estimated_cost": { "kind": "decimal", "min": 100, "max": 5000, "scale": 2, "distribution": "normal", "description": "Estimated cost of producing the content variant." } } }, "interactions": { "description": "This table logs user interactions with content variants, linking back to the respective content and users.", "rows": 600, "columns": { "id": { "kind": "pk", "description": "Unique identifier for each interaction." }, "content_variant_id": { "kind": "fk", "ref": "content_variants.id", "description": "The content variant that was interacted with." }, "user_id": { "kind": "fk", "ref": "users.id", "description": "The user who interacted with the content." }, "interaction_type": { "kind": "enum", "values": [ "click", "view", "share" ], "description": "Type of interaction performed by the user.", "weights": [ 6, 3, 1 ] }, "interaction_date": { "kind": "datetime", "from": "2023-01-01", "to": "2023-12-31", "description": "The date and time when the interaction occurred.", "dayProfile": "waking" }, "device_type": { "kind": "enum", "values": [ "desktop", "mobile", "tablet" ], "weights": [ 40, 50, 10 ], "description": "Type of device used for the interaction." }, "interaction_duration_seconds": { "kind": "int", "min": 1, "max": 300, "distribution": "normal", "description": "Duration of the interaction in seconds." } } }, "users": { "description": "This table contains information about users who interact with the content, with unique identifiers for each user.", "rows": 300, "columns": { "id": { "kind": "pk", "description": "Unique identifier for each user." }, "full_name": { "kind": "fullName", "description": "The full name of the user." }, "email": { "kind": "email", "from": "full_name", "description": "The email address of the user." }, "registration_date": { "kind": "date", "from": "2022-01-01", "to": "2023-12-31", "description": "The date the user registered.", "trend": "growing" } } }, "performance_metrics": { "description": "This table holds performance metrics for each campaign and content variant, summarizing their effectiveness.", "rows": { "per": "content_variants", "via": "content_variant_id", "min": 1, "max": 1, "skew": "long-tail" }, "columns": { "id": { "kind": "pk", "description": "Unique identifier for each performance metric entry." }, "content_variant_id": { "kind": "fk", "ref": "content_variants.id", "description": "The content variant for which metrics are recorded." }, "click_through_rate": { "kind": "decimal", "min": 0, "max": 1, "scale": 2, "description": "Click-through rate of the content variant.", "distribution": "high" }, "conversion_rate": { "kind": "decimal", "min": 0, "max": 1, "scale": 2, "description": "Conversion rate based on interactions with the content variant.", "distribution": "high" }, "engagement_rate": { "kind": "decimal", "min": 0.01, "max": 0.75, "scale": 2, "distribution": "normal", "description": "Rate of user engagement with the content variant." }, "impressions": { "kind": "int", "min": 100, "max": 10000, "distribution": "long-tail", "description": "Number of times the content variant was displayed." } } }, "channels": { "description": "This table lists different marketing channels used in campaigns, linking to respective campaigns.", "rows": 50, "columns": { "id": { "kind": "pk", "description": "Unique identifier for each channel." }, "name": { "kind": "words", "min": 1, "max": 3, "description": "Name of the marketing channel.", "pool": [ "email", "social", "search", "display", "video", "affiliate", "mobile", "influencer", "content", "native", "event", "direct", "print", "radio", "tv", "outdoor", "podcast", "web", "app", "community", "referral" ] }, "campaign_id": { "kind": "fk", "ref": "campaigns.id", "description": "The campaign that utilizes this channel." }, "reach": { "kind": "int", "min": 50, "max": 50000, "distribution": "long-tail", "description": "Estimated number of users reached by the channel." }, "channel_type": { "kind": "enum", "values": [ "paid", "organic", "earned" ], "weights": [ 40, 40, 20 ], "description": "Type of marketing channel." } } } }, "structure": "multi-table", "traits": [ "timestamped", "categorical", "nullable", "self-referencing" ], "analysisQuestions": [ "Which content variants had the highest click-through rates?", "What are the most effective marketing channels for user engagement?", "How do different user demographics affect interaction patterns?", "Which campaigns yielded the best conversion rates overall?" ], "suggestedQueries": [ { "title": "Get all campaigns and their statuses", "sql": "SELECT name, status FROM campaigns;", "dialect": "postgres" }, { "title": "Count interactions by content variant", "sql": "SELECT content_variant_id, COUNT(*) AS interaction_count FROM interactions GROUP BY content_variant_id;", "dialect": "postgres" }, { "title": "Calculate average conversion rates by campaign", "sql": "SELECT cv.campaign_id, AVG(pm.conversion_rate) AS avg_conversion_rate FROM performance_metrics pm JOIN content_variants cv ON pm.content_variant_id = cv.id GROUP BY cv.campaign_id;", "dialect": "postgres" }, { "title": "Find users who interacted with a specific content variant", "sql": 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By analyzing click-through and conversion rates, they can identify which strategies yield the best results, allowing for data-driven decision-making in future campaigns." }, { "title": "Data Engineers", "body": "Data engineers can leverage this dataset to build ETL pipelines that extract performance metrics from raw interaction logs. By transforming and loading this data into a data warehouse, they can enable deeper analytics and reporting for the marketing team." }, { "title": "Campaign Managers", "body": "Campaign managers can use this dataset to monitor ongoing campaigns in real-time. By analyzing user interactions across different content variants, they can make informed adjustments to maximize engagement and effectiveness." }, { "title": "Data Scientists", "body": "Data scientists can apply machine learning models to this dataset to predict user behavior and optimize content strategies. By understanding how users interact with different templates, they can enhance personalization and improve campaign outcomes." } ] }, "counts": { "campaigns": 150, "channels": 50, "content_variants": 451, "performance_metrics": 451, "users": 300, "interactions": 600 }, "stats": { "campaigns": { "rows": 150, "columns": { "id": { "nulls": 0, "distinct": 150, "min": 1, "max": 150 }, "name": { "nulls": 0, "distinct": 117 }, "start_date": { "nulls": 0, "distinct": 127, "min": "2023-01-05", "max": "2023-12-31" }, "end_date": { "nulls": 139, "distinct": 11, "min": "2023-03-03", "max": "2023-11-19" }, "status": { "nulls": 0, "distinct": 3, "min": "active", "max": "inactive" } } }, "content_variants": { "rows": 451, "columns": { "id": { "nulls": 0, "distinct": 451, "min": 1, "max": 451 }, "campaign_id": { "nulls": 0, "distinct": 150, "min": 1, "max": 150 }, "template": { "nulls": 83, "distinct": 253 }, "variant_type": { "nulls": 0, "distinct": 3, "min": "banner", "max": "social_media" }, "audience_targeting": { "nulls": 0, "distinct": 3, "min": "general", "max": "specific" }, "estimated_cost": { "nulls": 0, "distinct": 450, "min": 709.41, "max": 4505.77 } } }, "interactions": { "rows": 600, "columns": { "id": { "nulls": 0, "distinct": 600, "min": 1, "max": 600 }, "content_variant_id": { "nulls": 0, "distinct": 323, "min": 3, "max": 448 }, "user_id": { "nulls": 0, "distinct": 255, "min": 1, "max": 300 }, "interaction_type": { "nulls": 0, "distinct": 3, "min": "click", "max": "view" }, "interaction_date": { "nulls": 0, "distinct": 600, "min": "2023-01-01 13:36:13", "max": "2023-12-31 19:03:07" }, "device_type": { "nulls": 0, "distinct": 3, "min": "desktop", "max": "tablet" }, "interaction_duration_seconds": { "nulls": 0, "distinct": 168, "min": 35, "max": 255 } } }, "users": { "rows": 300, "columns": { "id": { "nulls": 0, "distinct": 300, "min": 1, "max": 300 }, "full_name": { "nulls": 0, "distinct": 290, "min": "Alice Kowalski", "max": "Zoe Yilmaz" }, "email": { "nulls": 0, "distinct": 298 }, "registration_date": { "nulls": 0, "distinct": 252, "min": "2022-01-12", "max": "2023-12-31" } } }, "performance_metrics": { "rows": 451, "columns": { "id": { "nulls": 0, "distinct": 451, "min": 1, "max": 451 }, "content_variant_id": { "nulls": 0, "distinct": 451, "min": 1, "max": 451 }, "click_through_rate": { "nulls": 0, "distinct": 95, "min": 0.01, "max": 1 }, "conversion_rate": { "nulls": 0, "distinct": 96, "min": 0.01, "max": 1 }, "engagement_rate": { "nulls": 0, "distinct": 50, "min": 0.07, "max": 0.68 }, "impressions": { "nulls": 0, "distinct": 426, "min": 100, "max": 9991 } } }, "channels": { "rows": 50, "columns": { "id": { "nulls": 0, "distinct": 50, "min": 1, "max": 50 }, "name": { "nulls": 0, "distinct": 44 }, "campaign_id": { "nulls": 0, "distinct": 41, "min": 1, "max": 148 }, "reach": { "nulls": 0, "distinct": 50, "min": 170, "max": 47092 }, "channel_type": { "nulls": 0, "distinct": 3, "min": "earned", "max": "paid" } } } }, "artifacts": [ { "file": "csv/campaigns.csv", "format": "csv", "table": "campaigns", "key": "datasets/ai-driven-interactive-digital-marketing/v4/csv/campaigns.csv", "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/ai-driven-interactive-digital-marketing/v4/csv/campaigns.csv", "bytes": 6606, "sha256": "fc3b4fa5188192bb36608140b4a280b3a1589ea0b1ad803f467c738c85b5c962", "rows": 150, "contentType": "text/csv; 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