{
  "format": "dataset-manifest/1",
  "slug": "ai-powered-home-decor-stylist",
  "version": 1,
  "seed": "ai-powered-home-decor-stylist:1",
  "spec": {
    "format": "dataset/1",
    "slug": "ai-powered-home-decor-stylist",
    "name": "Home Decor Recommendations",
    "tagline": "Personalized decor suggestions for every unique style.",
    "description": "Customers engage with personalized home decor experiences by taking quizzes that reveal their style preferences. These quizzes gather insights on their tastes, desired aesthetics, and specific needs for different spaces in their homes. Based on quiz results, tailored recommendations are generated, presenting curated product selections that align with individual styles. Stylists interact with customers, offering additional guidance and advice, helping them refine their choices and enhance their decor journey.\n\nThe dataset includes several tables that capture the dynamics of this business operation. The customers table holds details about each customer, including demographics and preferences. The quizzes table records the questions posed to customers and their responses, while the products table lists all available items, from furniture to decorative accessories. Recommendations link customers to specific products based on their quiz results, highlighting the most relevant options for each individual. Interactions track the communications between customers and stylists, capturing the various ways they seek advice or express their preferences. Each interaction can provide valuable context for understanding customer behaviors and decision-making.\n\nBy examining this data, developers can explore questions such as: What styles are most popular among different demographics? How do customer preferences shift over time based on quiz responses? What is the impact of stylist interactions on customer purchasing decisions? Additionally, the dataset offers insights into emerging trends in home decor, helping to identify which product categories and styles are gaining traction, and which may be considered niche. As a synthetic dataset created for practice, it provides an engaging opportunity for developers to hone their skills in data analysis, machine learning, and customer behavior interpretation.",
    "domain": "ecommerce",
    "shape": "transactional",
    "useCases": [
      "sql-practice",
      "orm-seeding"
    ],
    "keywords": [
      "home decor",
      "recommendations",
      "stylist",
      "ecommerce",
      "customer preferences",
      "products",
      "quizzes"
    ],
    "locale": "en",
    "license": "CC0-1.0",
    "version": 1,
    "tables": {
      "customers": {
        "description": "This table contains unique customers who interact with the home decor platform, storing personal details and preferences.",
        "rows": 200,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each customer."
          },
          "full_name": {
            "kind": "fullName",
            "description": "The customer's full name."
          },
          "email": {
            "kind": "email",
            "from": "full_name",
            "description": "The customer's email address."
          },
          "preferred_style": {
            "kind": "enum",
            "values": [
              "modern",
              "rustic",
              "bohemian",
              "minimalist",
              "industrial"
            ],
            "description": "The customer's preferred decor style."
          },
          "created_at": {
            "kind": "datetime",
            "from": "2023-01-01",
            "to": "2023-10-31",
            "description": "The timestamp of when the customer registered.",
            "dayProfile": "business"
          }
        }
      },
      "quizzes": {
        "description": "This table records the quizzes taken by customers to discover their decor preferences.",
        "rows": 250,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each quiz."
          },
          "customer_id": {
            "kind": "fk",
            "ref": "customers.id",
            "description": "Reference to the customer taking the quiz."
          },
          "style_preferences": {
            "kind": "json",
            "template": {
              "preferred_colors": {
                "kind": "words",
                "description": "Preferred colors as expressed by the customer."
              },
              "room_dimensions": {
                "kind": "words",
                "description": "Dimensions of the customer's rooms."
              }
            },
            "description": "The preferences expressed by the customer in the quiz."
          },
          "completed_at": {
            "kind": "datetime",
            "from": "2023-01-01",
            "to": "2023-10-31",
            "description": "The date when the quiz was completed.",
            "dayProfile": "waking"
          }
        }
      },
      "products": {
        "description": "Details of decor products available for recommendation, including types and styles.",
        "rows": 300,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each product."
          },
          "name": {
            "kind": "productName",
            "description": "The name of the product."
          },
          "style": {
            "kind": "enum",
            "values": [
              "modern",
              "rustic",
              "bohemian",
              "minimalist",
              "industrial"
            ],
            "description": "The style category of the product."
          },
          "price": {
            "kind": "decimal",
            "min": 10,
            "max": 5000,
            "scale": 2,
            "description": "The price of the product."
          },
          "stock_quantity": {
            "kind": "int",
            "min": 0,
            "max": 100,
            "description": "The available stock quantity of the product."
          }
        }
      },
      "recommendations": {
        "description": "This table links customers to the products recommended based on quiz results and stylist input.",
        "rows": 400,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each recommendation."
          },
          "customer_id": {
            "kind": "fk",
            "ref": "customers.id",
            "description": "Reference to the customer receiving the recommendation."
          },
          "product_id": {
            "kind": "fk",
            "ref": "products.id",
            "description": "Reference to the recommended product."
          },
          "recommended_at": {
            "kind": "datetime",
            "from": "2023-01-01",
            "to": "2023-10-31",
            "description": "The date when the product was recommended.",
            "dayProfile": "waking"
          }
        }
      },
      "stylists": {
        "description": "Information about the stylists who assist customers with recommendations and inquiries.",
        "rows": 100,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each stylist."
          },
          "full_name": {
            "kind": "fullName",
            "description": "The stylist's full name."
          },
          "email": {
            "kind": "email",
            "from": "full_name",
            "description": "The stylist's email address."
          },
          "specialty": {
            "kind": "enum",
            "values": [
              "color consultant",
              "furniture expert",
              "accessories specialist"
            ],
            "description": "The stylist's area of expertise."
          }
        }
      },
      "interactions": {
        "description": "Tracks communications between customers and stylists, providing insights into customer service quality.",
        "rows": 150,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each interaction."
          },
          "customer_id": {
            "kind": "fk",
            "ref": "customers.id",
            "description": "Reference to the customer involved in the interaction."
          },
          "stylist_id": {
            "kind": "fk",
            "ref": "stylists.id",
            "description": "Reference to the stylist involved in the interaction."
          },
          "interaction_date": {
            "kind": "datetime",
            "from": "2023-01-01",
            "to": "2023-10-31",
            "description": "The date of the interaction.",
            "trend": "growing"
          },
          "notes": {
            "kind": "words",
            "description": "Notes from the interaction."
          }
        }
      }
    },
    "structure": "multi-table",
    "traits": [
      "categorical",
      "timestamped",
      "nullable"
    ],
    "analysisQuestions": [
      "What styles are the most popular among customers?",
      "How do quiz results correlate with product recommendations?",
      "Which stylists have the highest customer satisfaction ratings?",
      "What are the trends in room dimensions across different customer demographics?",
      "How often do customers return to take new quizzes after their initial purchase?"
    ],
    "suggestedQueries": [
      {
        "title": "Join customers and their recommendations",
        "sql": "SELECT customers.full_name, products.name FROM customers JOIN recommendations ON customers.id = recommendations.customer_id JOIN products ON recommendations.product_id = products.id;",
        "dialect": "postgres"
      },
      {
        "title": "Count recommendations per preferred style",
        "sql": "SELECT products.style, COUNT(recommendations.id) FROM recommendations JOIN products ON recommendations.product_id = products.id GROUP BY products.style;",
        "dialect": "postgres"
      },
      {
        "title": "Find the latest interactions with stylists",
        "sql": "SELECT customers.full_name, stylists.full_name, interactions.interaction_date FROM interactions JOIN customers ON interactions.customer_id = customers.id JOIN stylists ON interactions.stylist_id = stylists.id ORDER BY interactions.interaction_date DESC;",
        "dialect": "postgres"
      },
      {
        "title": "Average product price by customer style preference",
        "sql": "SELECT customers.preferred_style, AVG(products.price) FROM recommendations JOIN customers ON recommendations.customer_id = customers.id JOIN products ON recommendations.product_id = products.id GROUP BY customers.preferred_style;",
        "dialect": "postgres"
      }
    ],
    "usageScenarios": [
      {
        "title": "E-commerce Data Analysis",
        "body": "Data analysts can use this dataset to evaluate trends in home decor preferences based on customer interactions and quiz results. By analyzing the recommendations given to customers, they can identify which styles are gaining popularity and tailor marketing strategies accordingly."
      },
      {
        "title": "Stylist Performance Tracking",
        "body": "Stylist managers can utilize this data to assess the effectiveness of their teams. By examining customer satisfaction ratings and interaction notes, they can provide targeted training to improve service levels and enhance customer experience."
      },
      {
        "title": "Product Development Insights",
        "body": "Product development teams can leverage insights from customer quizzes and recommendations to guide new product lines. Understanding customer preferences and trends allows them to create offerings that align with market demands."
      },
      {
        "title": "Customer Experience Enhancement",
        "body": "Customer service representatives can analyze interactions between customers and stylists to identify areas for improvement. By reviewing communication logs and customer feedback, they can implement changes to enhance the overall experience."
      }
    ]
  },
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    "customers": 200,
    "products": 300,
    "quizzes": 250,
    "recommendations": 400,
    "stylists": 100,
    "interactions": 150
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