{
  "format": "dataset-manifest/1",
  "slug": "customer-engagement-analytics",
  "version": 2,
  "seed": "customer-engagement-analytics:1",
  "spec": {
    "format": "dataset/1",
    "slug": "customer-engagement-analytics",
    "name": "Customer Engagement Analytics",
    "tagline": "Customer profiles, interaction logs, campaign responses, and agent performance metrics from a marketing call center.",
    "description": "Agents reach out to potential customers daily, promoting products and gathering insights to shape future marketing efforts. Every interaction is meticulously logged, capturing the nuances of each conversation, while detailed customer profiles provide valuable context. The business thrives on understanding customer responses to various campaigns, allowing for strategic adjustments based on their feedback and behaviors. This dynamic environment allows agents to not only promote but also learn from each engagement, ultimately driving the effectiveness of marketing strategies.\n\nThe dataset comprises several interconnected tables that delineate the operations of this marketing call center. The 'customers' table holds details on individual customers, including demographics and preferences, while the 'interactions' table tracks each engagement made by the agents. Each interaction is associated with a specific 'campaign' from the campaigns table, which outlines the marketing efforts in place. Feedback from customers is recorded in the 'responses' table, linking back to both the interactions and campaigns to provide a clear picture of effectiveness. Additionally, 'agents' table catalogues the representatives, while 'agent_performance' table evaluates their efficiency based on the outcomes of their interactions. Each row in these tables contributes to a comprehensive narrative about how customer engagement unfolds.\n\nThis dataset is invaluable for practicing data analysis, allowing developers to explore questions such as which campaigns yield the highest engagement rates, what interactions lead to positive feedback, and how agent performance varies across different campaigns. It also facilitates deeper analysis on customer behavior patterns and identifies areas for improvement in outreach efforts. Keep in mind that the data is synthetic, crafted to offer realistic scenarios for your analytical endeavors.",
    "domain": "marketing",
    "shape": "transactional",
    "useCases": [
      "sql-practice",
      "orm-seeding"
    ],
    "keywords": [
      "customer engagement",
      "call center",
      "marketing campaigns",
      "interaction logs",
      "agent performance",
      "customer profiles"
    ],
    "locale": "en",
    "license": "CC0-1.0",
    "version": 1,
    "tables": {
      "customers": {
        "description": "This table contains unique customer profiles, linking to their engagement history and preferences. Each row represents a distinct customer.",
        "rows": 500,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each customer."
          },
          "full_name": {
            "kind": "fullName",
            "description": "The full name of the customer."
          },
          "email": {
            "kind": "email",
            "from": "full_name",
            "description": "The customer's email address."
          },
          "phone": {
            "kind": "phone",
            "description": "The customer's contact number."
          },
          "age": {
            "kind": "int",
            "min": 18,
            "max": 90,
            "description": "Age of the customer."
          },
          "gender": {
            "kind": "enum",
            "values": [
              "male",
              "female",
              "non-binary"
            ],
            "description": "Gender of the customer."
          },
          "city": {
            "kind": "city",
            "description": "City where the customer resides."
          },
          "interests": {
            "kind": "words",
            "min": 1,
            "max": 3,
            "description": "Interests and preferences of the customer.",
            "pool": [
              "technology",
              "travel",
              "fitness",
              "music",
              "food",
              "fashion",
              "gaming",
              "art",
              "sports",
              "reading",
              "photography",
              "cooking",
              "movies",
              "nature",
              "wellness",
              "social",
              "education",
              "finance",
              "pets",
              "health"
            ]
          }
        }
      },
      "interactions": {
        "description": "This table logs each interaction between agents and customers, detailing the nature of the engagement. Each row corresponds to a single interaction.",
        "rows": {
          "per": "customers",
          "via": "customer_id",
          "min": 1,
          "max": 5,
          "skew": "long-tail"
        },
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each interaction."
          },
          "customer_id": {
            "kind": "fk",
            "ref": "customers.id",
            "description": "The customer involved in the interaction."
          },
          "agent_id": {
            "kind": "fk",
            "ref": "agents.id",
            "description": "The agent conducting the interaction."
          },
          "interaction_date": {
            "kind": "datetime",
            "from": "2022-01-01",
            "to": "2023-12-31",
            "description": "Date and time of the interaction.",
            "dayProfile": "waking"
          },
          "campaign_id": {
            "kind": "fk",
            "ref": "campaigns.id",
            "description": "The campaign associated with the interaction."
          },
          "feedback": {
            "kind": "sentence",
            "nullable": true,
            "description": "Feedback provided by the customer during the interaction."
          }
        }
      },
      "campaigns": {
        "description": "This table outlines the marketing campaigns run by the call center, linking to interactions and responses. Each campaign has specific goals and metrics.",
        "rows": 30,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each campaign."
          },
          "name": {
            "kind": "productName",
            "description": "The name of the marketing campaign."
          },
          "start_date": {
            "kind": "date",
            "from": "2022-01-01",
            "to": "2023-12-31",
            "description": "The start date of the campaign.",
            "trend": "growing"
          },
          "end_date": {
            "kind": "date",
            "from": "2022-01-01",
            "to": "2023-12-31",
            "description": "The end date of the campaign.",
            "trend": "growing"
          },
          "goal": {
            "kind": "words",
            "min": 1,
            "max": 3,
            "description": "The primary goal of the campaign.",
            "pool": [
              "increase",
              "engagement",
              "awareness",
              "sales",
              "loyalty",
              "traffic",
              "conversion",
              "retention",
              "visibility",
              "growth",
              "reach",
              "impact",
              "response",
              "participation",
              "feedback",
              "interaction",
              "promotion",
              "branding",
              "education",
              "activation"
            ]
          },
          "budget": {
            "kind": "decimal",
            "min": 1000,
            "max": 50000,
            "scale": 2,
            "description": "Budget allocated for the campaign.",
            "distribution": "long-tail"
          }
        }
      },
      "responses": {
        "description": "This table captures customer responses to various campaigns, detailing interest levels and outcomes. Each row represents a customer response.",
        "rows": {
          "per": "campaigns",
          "via": "campaign_id",
          "min": 1,
          "max": 10,
          "skew": "long-tail"
        },
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each response."
          },
          "customer_id": {
            "kind": "fk",
            "ref": "customers.id",
            "description": "The customer providing the response."
          },
          "campaign_id": {
            "kind": "fk",
            "ref": "campaigns.id",
            "description": "The campaign related to the response."
          },
          "interest_level": {
            "kind": "enum",
            "values": [
              "low",
              "medium",
              "high"
            ],
            "description": "The level of interest expressed by the customer."
          },
          "purchase_intent": {
            "kind": "bool",
            "trueRate": 0.3,
            "description": "Indicates if the customer intends to purchase."
          }
        }
      },
      "agents": {
        "description": "This table includes details about the agents working at the call center, linking their performance metrics to interactions. Each row corresponds to one agent.",
        "rows": 100,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each agent."
          },
          "full_name": {
            "kind": "fullName",
            "description": "The full name of the agent."
          },
          "email": {
            "kind": "email",
            "from": "full_name",
            "description": "The agent's email address."
          },
          "hire_date": {
            "kind": "date",
            "from": "2020-01-01",
            "to": "2023-12-31",
            "description": "The date when the agent was hired.",
            "trend": "growing"
          },
          "performance_rating": {
            "kind": "decimal",
            "min": 1,
            "max": 5,
            "scale": 1,
            "description": "Performance rating of the agent.",
            "distribution": "high"
          }
        }
      },
      "agent_performance": {
        "description": "This table tracks the performance metrics of agents over time, linking to their interactions and customer feedback. Each row represents a performance record.",
        "rows": {
          "per": "agents",
          "via": "agent_id",
          "min": 1,
          "max": 12,
          "skew": "long-tail"
        },
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each performance record."
          },
          "agent_id": {
            "kind": "fk",
            "ref": "agents.id",
            "description": "The agent whose performance is being recorded."
          },
          "interaction_count": {
            "kind": "int",
            "min": 1,
            "max": 100,
            "description": "Total interactions handled by the agent.",
            "distribution": "long-tail"
          },
          "success_rate": {
            "kind": "decimal",
            "min": 0,
            "max": 1,
            "scale": 2,
            "description": "Success rate of the agent in converting leads."
          },
          "feedback_score": {
            "kind": "decimal",
            "min": 1,
            "max": 10,
            "scale": 1,
            "description": "Average feedback score received by the agent.",
            "distribution": "high"
          }
        }
      }
    },
    "structure": "multi-table",
    "traits": [
      "nullable",
      "timestamped",
      "categorical",
      "high-cardinality"
    ],
    "analysisQuestions": [
      "Which campaigns have the highest customer engagement rates?",
      "How do different customer demographics affect campaign success?",
      "What factors contribute to an agent's performance metrics?",
      "What is the average feedback score across different campaigns?",
      "How does customer interest level correlate with purchase intent?"
    ],
    "suggestedQueries": [
      {
        "title": "Join Customers with Interactions",
        "sql": "SELECT c.full_name, i.interaction_date FROM customers c JOIN interactions i ON c.id = i.customer_id;",
        "dialect": "postgres"
      },
      {
        "title": "Count Interactions per Campaign",
        "sql": "SELECT campaign_id, COUNT(*) as interaction_count FROM interactions GROUP BY campaign_id;",
        "dialect": "postgres"
      },
      {
        "title": "Average Success Rate by Agent",
        "sql": "SELECT agent_id, AVG(success_rate) as average_success FROM agent_performance GROUP BY agent_id;",
        "dialect": "postgres"
      },
      {
        "title": "Daily Interactions Over Time",
        "sql": "SELECT DATE(interaction_date) as interaction_day, COUNT(*) as total_interactions FROM interactions GROUP BY interaction_day ORDER BY interaction_day;",
        "dialect": "postgres"
      },
      {
        "title": "Feedback Score Distribution by Campaign",
        "sql": "SELECT campaign_id, AVG(feedback_score) as average_feedback FROM agent_performance INNER JOIN interactions ON interactions.agent_id = agent_performance.agent_id GROUP BY campaign_id;",
        "dialect": "postgres"
      }
    ],
    "usageScenarios": [
      {
        "title": "Marketing Analyst",
        "body": "A marketing analyst can utilize this dataset to assess the effectiveness of various campaigns by analyzing customer engagement and feedback. They can identify high-performing campaigns, understand customer demographics, and refine marketing strategies based on the insights derived from this data."
      },
      {
        "title": "Data Scientist",
        "body": "A data scientist may leverage the dataset to build predictive models that forecast customer interest and conversion rates. By analyzing past interactions and agent performance metrics, they can develop algorithms that optimize future marketing efforts and improve overall campaign success."
      },
      {
        "title": "Call Center Manager",
        "body": "A call center manager can use the dataset to evaluate the performance of agents and identify areas for improvement. By examining interaction logs and customer feedback, they can provide targeted training and support to agents, ultimately enhancing the customer experience."
      },
      {
        "title": "Business Intelligence Developer",
        "body": "A business intelligence developer can utilize the dataset to create interactive dashboards that visualize key metrics related to customer engagement and campaign effectiveness. By presenting data in a meaningful way, they can help stakeholders make informed decisions and drive marketing initiatives."
      }
    ]
  },
  "counts": {
    "agents": 100,
    "agent_performance": 506,
    "campaigns": 30,
    "customers": 500,
    "interactions": 1119,
    "responses": 98
  },
  "stats": {
    "customers": {
      "rows": 500,
      "columns": {
        "id": {
          "nulls": 0,
          "distinct": 500,
          "min": 1,
          "max": 500
        },
        "full_name": {
          "nulls": 0,
          "distinct": 483,
          "min": "Alice Chen",
          "max": "Zoe Wagner"
        },
        "email": {
          "nulls": 0,
          "distinct": 497
        },
        "phone": {
          "nulls": 0,
          "distinct": 449
        },
        "age": {
          "nulls": 0,
          "distinct": 73,
          "min": 18,
          "max": 90
        },
        "gender": {
          "nulls": 0,
          "distinct": 3,
          "min": "female",
          "max": "non-binary"
        },
        "city": {
          "nulls": 0,
          "distinct": 63,
          "min": "Amsterdam",
          "max": "Zurich"
        },
        "interests": {
          "nulls": 0,
          "distinct": 328
        }
      }
    },
    "interactions": {
      "rows": 1119,
      "columns": {
        "id": {
          "nulls": 0,
          "distinct": 1119,
          "min": 1,
          "max": 1119
        },
        "customer_id": {
          "nulls": 0,
          "distinct": 500,
          "min": 1,
          "max": 500
        },
        "agent_id": {
          "nulls": 0,
          "distinct": 100,
          "min": 1,
          "max": 100
        },
        "interaction_date": {
          "nulls": 0,
          "distinct": 1119,
          "min": "2022-01-01 09:51:02",
          "max": "2023-12-31 18:43:18"
        },
        "campaign_id": {
          "nulls": 0,
          "distinct": 30,
          "min": 1,
          "max": 30
        },
        "feedback": {
          "nulls": 117,
          "distinct": 988
        }
      }
    },
    "campaigns": {
      "rows": 30,
      "columns": {
        "id": {
          "nulls": 0,
          "distinct": 30,
          "min": 1,
          "max": 30
        },
        "name": {
          "nulls": 0,
          "distinct": 28,
          "min": "Classic Jacket",
          "max": "Wireless Jacket"
        },
        "start_date": {
          "nulls": 0,
          "distinct": 30,
          "min": "2022-01-30",
          "max": "2023-12-17"
        },
        "end_date": {
          "nulls": 0,
          "distinct": 29,
          "min": "2022-03-23",
          "max": "2023-12-27"
        },
        "goal": {
          "nulls": 0,
          "distinct": 29
        },
        "budget": {
          "nulls": 0,
          "distinct": 30,
          "min": 1298.93,
          "max": 46740.4
        }
      }
    },
    "responses": {
      "rows": 98,
      "columns": {
        "id": {
          "nulls": 0,
          "distinct": 98,
          "min": 1,
          "max": 98
        },
        "customer_id": {
          "nulls": 0,
          "distinct": 91,
          "min": 18,
          "max": 499
        },
        "campaign_id": {
          "nulls": 0,
          "distinct": 30,
          "min": 1,
          "max": 30
        },
        "interest_level": {
          "nulls": 0,
          "distinct": 3,
          "min": "high",
          "max": "medium"
        },
        "purchase_intent": {
          "nulls": 0,
          "distinct": 2,
          "min": "false",
          "max": "true"
        }
      }
    },
    "agents": {
      "rows": 100,
      "columns": {
        "id": {
          "nulls": 0,
          "distinct": 100,
          "min": 1,
          "max": 100
        },
        "full_name": {
          "nulls": 0,
          "distinct": 100,
          "min": "Alice Khan",
          "max": "Zoe Reyes"
        },
        "email": {
          "nulls": 0,
          "distinct": 100
        },
        "hire_date": {
          "nulls": 0,
          "distinct": 98,
          "min": "2020-02-04",
          "max": "2023-12-30"
        },
        "performance_rating": {
          "nulls": 0,
          "distinct": 34,
          "min": 1,
          "max": 5
        }
      }
    },
    "agent_performance": {
      "rows": 506,
      "columns": {
        "id": {
          "nulls": 0,
          "distinct": 506,
          "min": 1,
          "max": 506
        },
        "agent_id": {
          "nulls": 0,
          "distinct": 100,
          "min": 1,
          "max": 100
        },
        "interaction_count": {
          "nulls": 0,
          "distinct": 96,
          "min": 1,
          "max": 100
        },
        "success_rate": {
          "nulls": 0,
          "distinct": 100,
          "min": 0.01,
          "max": 1
        },
        "feedback_score": {
          "nulls": 0,
          "distinct": 89,
          "min": 1.1,
          "max": 10
        }
      }
    }
  },
  "artifacts": [
    {
      "file": "csv/agents.csv",
      "format": "csv",
      "table": "agents",
      "key": "datasets/customer-engagement-analytics/v2/csv/agents.csv",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/customer-engagement-analytics/v2/csv/agents.csv",
      "bytes": 5551,
      "sha256": "f82fb0c969dbea2d34f4d34a8da3827ab6ee256e54787c3af299ce77b55e517b",
      "rows": 100,
      "contentType": "text/csv; charset=utf-8"
    },
    {
      "file": "csv/agent_performance.csv",
      "format": "csv",
      "table": "agent_performance",
      "key": "datasets/customer-engagement-analytics/v2/csv/agent_performance.csv",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/customer-engagement-analytics/v2/csv/agent_performance.csv",
      "bytes": 9941,
      "sha256": "9355fe18cc8d51056dc9bf1a9dbe29d9230cbb105aeee3a6ceb7ab1f4496c7c9",
      "rows": 506,
      "contentType": "text/csv; charset=utf-8"
    },
    {
      "file": "csv/campaigns.csv",
      "format": "csv",
      "table": "campaigns",
      "key": "datasets/customer-engagement-analytics/v2/csv/campaigns.csv",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/customer-engagement-analytics/v2/csv/campaigns.csv",
      "bytes": 2068,
      "sha256": "c8f6e4a2b91de8b30f0f8260265e48c41b39f40dc4125a1e87057156580b7a23",
      "rows": 30,
      "contentType": "text/csv; charset=utf-8"
    },
    {
      "file": "csv/customers.csv",
      "format": "csv",
      "table": "customers",
      "key": "datasets/customer-engagement-analytics/v2/csv/customers.csv",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/customer-engagement-analytics/v2/csv/customers.csv",
      "bytes": 45476,
      "sha256": "ab89c557ec6bbae12ee95555a4701f5f398cf4ae0404a7f844dacda73780482f",
      "rows": 500,
      "contentType": "text/csv; charset=utf-8"
    },
    {
      "file": "csv/interactions.csv",
      "format": "csv",
      "table": "interactions",
      "key": "datasets/customer-engagement-analytics/v2/csv/interactions.csv",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/customer-engagement-analytics/v2/csv/interactions.csv",
      "bytes": 95508,
      "sha256": "f3e3ba5112b7e4a4aed2ca295c4b10fe41e6401f6886204581e888b684aa2718",
      "rows": 1119,
      "contentType": "text/csv; charset=utf-8"
    },
    {
      "file": "csv/responses.csv",
      "format": "csv",
      "table": "responses",
      "key": "datasets/customer-engagement-analytics/v2/csv/responses.csv",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/customer-engagement-analytics/v2/csv/responses.csv",
      "bytes": 2166,
      "sha256": "e1715edc23f14b97024a23b7e1c73638fe14e6afe1e62b45ce3b1680f67b74ff",
      "rows": 98,
      "contentType": "text/csv; charset=utf-8"
    },
    {
      "file": "customer-engagement-analytics.json",
      "format": "json",
      "table": null,
      "key": "datasets/customer-engagement-analytics/v2/customer-engagement-analytics.json",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/customer-engagement-analytics/v2/customer-engagement-analytics.json",
      "bytes": 477480,
      "sha256": "b54b9ed4be813a52725b858a000a5f40ed7c0a6e6c76329ee0df3ebd18483ba0",
      "rows": null,
      "contentType": "application/json; charset=utf-8"
    },
    {
      "file": "sql/postgres.sql",
      "format": "sql-postgres",
      "table": null,
      "key": "datasets/customer-engagement-analytics/v2/sql/postgres.sql",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/customer-engagement-analytics/v2/sql/postgres.sql",
      "bytes": 202781,
      "sha256": "c9d0ed9d4a82848ff009b61b7747cd416ca2c40914e0b4a25b842c852da7c5fd",
      "rows": null,
      "contentType": "application/sql; charset=utf-8"
    },
    {
      "file": "sql/mysql.sql",
      "format": "sql-mysql",
      "table": null,
      "key": "datasets/customer-engagement-analytics/v2/sql/mysql.sql",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/customer-engagement-analytics/v2/sql/mysql.sql",
      "bytes": 201724,
      "sha256": "8d9b96a03a86a74b59594c89c390a579d5d30d5193bd21880c9913ed1b15e916",
      "rows": null,
      "contentType": "application/sql; charset=utf-8"
    },
    {
      "file": "sql/mssql.sql",
      "format": "sql-mssql",
      "table": null,
      "key": "datasets/customer-engagement-analytics/v2/sql/mssql.sql",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/customer-engagement-analytics/v2/sql/mssql.sql",
      "bytes": 207771,
      "sha256": "e10ec29ed3a1fe3def775ac6dfe919493b87a774d08e832bc62c83ad689ae6f8",
      "rows": null,
      "contentType": "application/sql; charset=utf-8"
    },
    {
      "file": "sql/sqlite.sql",
      "format": "sql-sqlite",
      "table": null,
      "key": "datasets/customer-engagement-analytics/v2/sql/sqlite.sql",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/customer-engagement-analytics/v2/sql/sqlite.sql",
      "bytes": 201401,
      "sha256": "2bf0e2b4607e863e9b132536eeae74f83f4b78498dc1c3df9f547f674d2b25f8",
      "rows": null,
      "contentType": "application/sql; charset=utf-8"
    },
    {
      "file": "prisma/seed.ts",
      "format": "prisma-seed",
      "table": null,
      "key": "datasets/customer-engagement-analytics/v2/prisma/seed.ts",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/customer-engagement-analytics/v2/prisma/seed.ts",
      "bytes": 387668,
      "sha256": "8ad87ee352230908625ae0d854acaf224a87063044637041a02d58a46d68111b",
      "rows": null,
      "contentType": "application/typescript; charset=utf-8"
    },
    {
      "file": "django/fixtures.json",
      "format": "django-fixture",
      "table": null,
      "key": "datasets/customer-engagement-analytics/v2/django/fixtures.json",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/customer-engagement-analytics/v2/django/fixtures.json",
      "bytes": 581725,
      "sha256": "f33376658dff2e906c097ddced305da22044a00a51cdbb55ebb244dbc548b6ac",
      "rows": null,
      "contentType": "application/json; charset=utf-8"
    }
  ],
  "publishedAt": 1788767477336,
  "updatedAt": 1788778179989
}