{
  "format": "dataset-manifest/1",
  "slug": "fashion-e-commerce-insights",
  "version": 3,
  "seed": "fashion-e-commerce-insights:1",
  "spec": {
    "format": "dataset/1",
    "slug": "fashion-e-commerce-insights",
    "name": "Fashion E-Commerce Insights",
    "tagline": "Product offerings, customer interactions, and supply logistics from an online clothing and shoe shop.",
    "description": "A clothing and shoe shop thrives on delivering the latest fashion trends to customers online, providing a wide array of apparel and footwear options. Each product in the catalog features distinct attributes such as style, color, material, and pricing, which directly influences customer interactions, wishlists, and reviews. The shop’s dynamic inventory is shaped by seasonal trends, leading to fluctuations in product popularity as certain styles or categories experience spikes in demand while niche items may remain in smaller quantities. By analyzing these patterns, the shop can effectively optimize its sales strategies and enhance the shopping experience.\n\nIn this dataset, the single table, 'products', serves as the core component of this e-commerce environment. Each row within the products table corresponds to a specific item available for sale, detailing its unique characteristics. Attributes such as product ID, name, description, category, price, stock quantity, and other relevant information are included, enabling a comprehensive view of the inventory. While additional tables that capture customer interactions, trends, or logistical factors are not present, the 'products' table alone allows for significant exploration of product popularity, pricing strategies, and inventory management.\n\nDevelopers can leverage this data to address numerous questions, such as: What trends are evident in consumer preferences? How do specific attributes of products correlate with higher sales? What items are most frequently added to wishlists or reviewed positively? The insights gained from this synthetic dataset can provide valuable practice in data analysis, statistical modeling, and understanding customer behavior in an online retail context.",
    "domain": "ecommerce",
    "shape": "star-schema",
    "useCases": [
      "bi-dashboards",
      "analytics-demo"
    ],
    "keywords": [
      "e-commerce",
      "fashion",
      "clothing",
      "shoes",
      "inventory",
      "customer engagement",
      "synthetic data"
    ],
    "locale": "en",
    "license": "CC0-1.0",
    "version": 1,
    "tables": {
      "products": {
        "description": "This table contains detailed information about each product offered by the shop, linking product features with customer interactions. Each row represents a unique product, detailing its category, color, material, inventory, and customer engagement metrics such as wishlist counts and reviews.",
        "rows": 500,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each product."
          },
          "category": {
            "kind": "enum",
            "values": [
              "clothing",
              "shoes",
              "accessories"
            ],
            "description": "The category to which the product belongs.",
            "weights": [
              6,
              2,
              1
            ]
          },
          "color": {
            "kind": "enum",
            "values": [
              "red",
              "blue",
              "green",
              "black",
              "white",
              "yellow",
              "purple"
            ],
            "description": "The primary color of the product.",
            "weights": [
              5,
              4,
              3,
              2,
              1,
              1,
              1
            ]
          },
          "material": {
            "kind": "enum",
            "values": [
              "cotton",
              "leather",
              "polyester",
              "denim",
              "wool"
            ],
            "description": "The material used to make the product.",
            "weights": [
              7,
              3,
              2,
              1,
              1
            ]
          },
          "inventory_level": {
            "kind": "int",
            "min": 0,
            "max": 1000,
            "description": "The current stock level of the product.",
            "nullable": true
          },
          "wishlist_count": {
            "kind": "int",
            "min": 0,
            "max": 500,
            "description": "The number of times this product has been added to wishlists.",
            "nullable": true,
            "distribution": "long-tail"
          },
          "average_review_score": {
            "kind": "decimal",
            "min": 1,
            "max": 5,
            "scale": 1,
            "description": "The average rating given by customers, on a scale from 1 to 5.",
            "nullable": true,
            "distribution": "high"
          },
          "seasonal_trend": {
            "kind": "enum",
            "values": [
              "spring",
              "summer",
              "fall",
              "winter",
              "none"
            ],
            "description": "The seasonal trend associated with the product, if applicable.",
            "nullable": true,
            "when": {
              "column": "category",
              "in": [
                "clothing",
                "shoes"
              ]
            },
            "weights": [
              5,
              3,
              1,
              1,
              2
            ]
          }
        }
      }
    },
    "structure": "single-table",
    "traits": [
      "categorical",
      "nullable",
      "high-cardinality"
    ],
    "analysisQuestions": [
      "What are the most popular product categories among customers?",
      "How does inventory level impact customer wishlist counts?",
      "Which colors and materials are trending this season?",
      "What is the average review score for products in each category?",
      "How do seasonal trends affect product sales?"
    ],
    "suggestedQueries": [
      {
        "title": "Get all products with high wishlist counts",
        "sql": "SELECT * FROM products WHERE wishlist_count > 100;",
        "dialect": "postgres"
      },
      {
        "title": "Count of products by category",
        "sql": "SELECT category, COUNT(*) FROM products GROUP BY category;",
        "dialect": "postgres"
      },
      {
        "title": "Average review score by color",
        "sql": "SELECT color, AVG(average_review_score) FROM products GROUP BY color;",
        "dialect": "postgres"
      },
      {
        "title": "Top 5 products by inventory level",
        "sql": "SELECT * FROM products ORDER BY inventory_level DESC LIMIT 5;",
        "dialect": "postgres"
      }
    ],
    "usageScenarios": [
      {
        "title": "E-commerce Data Analysis",
        "body": "Data analysts at a fashion e-commerce company can use this dataset to explore customer preferences based on product categories, colors, and materials. By analyzing wishlist counts and review scores, they can identify which products are most appealing to customers and optimize their inventory accordingly."
      },
      {
        "title": "Business Intelligence Dashboard Creation",
        "body": "Business intelligence professionals can leverage this synthetic dataset to create dynamic dashboards that visualize trends in product popularity and customer engagement. By incorporating metrics such as average review scores and inventory levels, they can provide actionable insights to the marketing team."
      },
      {
        "title": "Testing E-commerce Algorithms",
        "body": "Developers working on recommendation algorithms for e-commerce platforms can use this synthetic dataset to test and refine their models. By simulating customer interactions with products, they can assess the effectiveness of different recommendation strategies based on wishlist counts and seasonal trends."
      }
    ]
  },
  "counts": {
    "products": 500
  },
  "stats": {
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          "distinct": 500,
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        "category": {
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          "distinct": 3,
          "min": "accessories",
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        "color": {
          "nulls": 0,
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          "min": "black",
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        "material": {
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          "distinct": 5,
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        "inventory_level": {
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        "wishlist_count": {
          "nulls": 56,
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        "average_review_score": {
          "nulls": 47,
          "distinct": 40,
          "min": 1.1,
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        "seasonal_trend": {
          "nulls": 49,
          "distinct": 5,
          "min": "fall",
          "max": "winter"
        }
      }
    }
  },
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