{
  "format": "dataset-manifest/1",
  "slug": "virtual-retail-experience-dataset",
  "version": 1,
  "seed": "virtual-retail-experience-dataset:1",
  "spec": {
    "format": "dataset/1",
    "slug": "virtual-retail-experience-dataset",
    "name": "Virtual Retail Experience Dataset",
    "tagline": "User interactions and product details in augmented reality shopping experiences.",
    "description": "Customers engage with products in a virtual environment, using augmented reality to try on clothes, accessories, and furniture. This dataset models user interactions with virtual try-ons, product reviews, cart contents, and wishlist items, capturing a rich landscape of consumer behavior in the AR shopping realm. The dataset allows developers and data scientists to analyze user engagement, product performance, and AR feature usage. It is particularly useful for understanding how high-volume transactions cluster around popular items, while unique user-generated reviews and less common AR features create distinct patterns. The data is synthetic, crafted to simulate real-world interactions in an AR shopping context, making it perfect for SQL practice and ORM seeding. By exploring this dataset, users can answer questions about customer preferences, the effectiveness of AR features, and the relationship between product popularity and user engagement.",
    "domain": "ecommerce",
    "shape": "transactional",
    "useCases": [
      "sql-practice",
      "orm-seeding"
    ],
    "keywords": [
      "augmented reality",
      "shopping",
      "ecommerce",
      "user interactions",
      "product reviews",
      "virtual try-ons",
      "shopping cart",
      "wishlist"
    ],
    "locale": "en",
    "license": "CC0-1.0",
    "version": 1,
    "tables": {
      "ar_shopping_experience": {
        "description": "Each row represents a user's interaction with an AR shopping experience, including their actions, product details, and reviews. The table tracks multiple aspects such as product IDs, user engagement metrics, and AR technology features used during the experience.",
        "rows": 1000,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each interaction record."
          },
          "user_id": {
            "kind": "uuid",
            "description": "Unique identifier of the user engaging with the AR experience."
          },
          "product_id": {
            "kind": "uuid",
            "description": "Unique identifier for the product being interacted with."
          },
          "interaction_type": {
            "kind": "enum",
            "values": [
              "try_on",
              "review",
              "add_to_cart",
              "wishlist"
            ],
            "description": "Type of interaction the user performed with the product."
          },
          "review_rating": {
            "kind": "int",
            "min": 1,
            "max": 5,
            "nullable": true,
            "description": "Rating given to the product, on a scale from 1 to 5."
          },
          "review_text": {
            "kind": "sentence",
            "max": 255,
            "nullable": true,
            "description": "Text content of the user's review, if applicable."
          },
          "ar_feature_used": {
            "kind": "enum",
            "values": [
              "3D_view",
              "room_scale",
              "gesture_control",
              "voice_command"
            ],
            "description": "AR feature utilized during the interaction."
          },
          "interaction_timestamp": {
            "kind": "datetime",
            "from": "2023-01-01",
            "to": "2023-12-31",
            "description": "Timestamp of when the interaction occurred.",
            "dayProfile": "waking"
          }
        }
      }
    },
    "structure": "single-table",
    "traits": [
      "timestamped",
      "categorical",
      "high-cardinality"
    ],
    "analysisQuestions": [
      "What are the most popular products based on user interactions?",
      "How do user ratings correlate with the AR features used?",
      "What types of interactions are most common among users?",
      "Which AR features have the highest engagement rates?",
      "How does the time of day affect user interactions in AR shopping?"
    ],
    "suggestedQueries": [
      {
        "title": "Count of interactions by product",
        "sql": "SELECT product_id, COUNT(*) AS interaction_count FROM ar_shopping_experience GROUP BY product_id;",
        "dialect": "postgres"
      },
      {
        "title": "Average rating for each product",
        "sql": "SELECT product_id, AVG(review_rating) AS average_rating FROM ar_shopping_experience WHERE review_rating IS NOT NULL GROUP BY product_id;",
        "dialect": "postgres"
      },
      {
        "title": "Interactions by AR feature used",
        "sql": "SELECT ar_feature_used, COUNT(*) AS feature_count FROM ar_shopping_experience GROUP BY ar_feature_used;",
        "dialect": "postgres"
      },
      {
        "title": "Daily interaction trends",
        "sql": "SELECT DATE(interaction_timestamp) AS interaction_date, COUNT(*) AS daily_interactions FROM ar_shopping_experience GROUP BY interaction_date ORDER BY interaction_date;",
        "dialect": "postgres"
      }
    ],
    "usageScenarios": [
      {
        "title": "Ecommerce Analytics",
        "body": "Data analysts in ecommerce companies can use this dataset to derive insights into user behavior in AR shopping environments. By analyzing interaction types and product ratings, they can identify trends in customer preferences and improve product offerings."
      },
      {
        "title": "AR Feature Testing",
        "body": "Developers working on augmented reality applications can utilize this dataset to test various features and their effectiveness. By simulating user interactions and analyzing engagement metrics, they can refine their AR technology for better user experiences."
      },
      {
        "title": "User Experience Research",
        "body": "UX researchers can leverage this dataset to understand how users interact with AR shopping features. By examining interaction types and user feedback, they can gather valuable insights to enhance the overall shopping experience in augmented reality."
      }
    ]
  },
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