Event Analytics

Get comprehensive analytics for a specific event

GET /api/analytics/:eventId

cURL

curl -X GET https://api.gatepass.io/api/analytics/clx1234567890 \
  -H "Authorization: Bearer YOUR_TOKEN"

Success Response

{
  "totalRevenue": 125500.00,
  "ticketsSold": 1247,
  "totalTickets": 1500,
  "salesByDay": [
    {
      "date": "2024-05-01",
      "sales": 45
    },
    {
      "date": "2024-05-02",
      "sales": 78
    },
    {
      "date": "2024-05-03",
      "sales": 120
    },
    {
      "date": "2024-05-04",
      "sales": 156
    }
  ],
  "salesByTier": [
    {
      "name": "VIP",
      "sold": 98,
      "total": 100
    },
    {
      "name": "Premium",
      "sold": 234,
      "total": 300
    },
    {
      "name": "General",
      "sold": 915,
      "total": 1100
    }
  ],
  "attendance": {
    "checkIns": 1089,
    "noShows": 158,
    "checkInRate": 87.3,
    "hourlyBreakdown": [
      {
        "hour": "10:00",
        "checkIns": 45
      },
      {
        "hour": "11:00",
        "checkIns": 123
      },
      {
        "hour": "12:00",
        "checkIns": 234
      },
      {
        "hour": "13:00",
        "checkIns": 189
      },
      {
        "hour": "14:00",
        "checkIns": 267
      },
      {
        "hour": "15:00",
        "checkIns": 156
      },
      {
        "hour": "16:00",
        "checkIns": 75
      }
    ],
    "peakHours": [
      {
        "hour": "14:00",
        "count": 267
      },
      {
        "hour": "12:00",
        "count": 234
      },
      {
        "hour": "13:00",
        "count": 189
      },
      {
        "hour": "15:00",
        "count": 156
      }
    ]
  },
  "geography": {
    "cities": [
      {
        "city": "Lagos",
        "count": 567,
        "percentage": 45.5
      },
      {
        "city": "Abuja",
        "count": 234,
        "percentage": 18.8
      },
      {
        "city": "Port Harcourt",
        "count": 156,
        "percentage": 12.5
      },
      {
        "city": "Ibadan",
        "count": 123,
        "percentage": 9.9
      },
      {
        "city": "Kano",
        "count": 89,
        "percentage": 7.1
      }
    ],
    "countries": [
      {
        "country": "Nigeria",
        "count": 1098,
        "percentage": 88.0
      },
      {
        "country": "Ghana",
        "count": 78,
        "percentage": 6.3
      },
      {
        "country": "Kenya",
        "count": 45,
        "percentage": 3.6
      },
      {
        "country": "South Africa",
        "count": 26,
        "percentage": 2.1
      }
    ]
  }
}

Retrieve detailed analytics for an event, including sales data, attendance metrics, geographic distribution, and time-based patterns. Available to event organizers only.

Authentication

Authorization · string · required

Bearer token for authentication (organizer role required)

Path Parameters

eventId · string · required

The unique identifier of the event

Response

totalRevenue · number

Total revenue from ticket sales

ticketsSold · number

Total number of tickets sold

totalTickets · number

Total event capacity

salesByDay · array

Daily sales breakdown

Show Sales Data

date · string

Date (YYYY-MM-DD)

sales · number

Number of tickets sold on this date

salesByTier · array

Ticket sales by tier

Show Tier Data

name · string

Tier name

sold · number

Number of tickets sold

total · number

Total tier capacity

attendance · object

Attendance and check-in metrics

Show Attendance Object

checkIns · number

Total number of check-ins

noShows · number

Number of tickets not used

checkInRate · number

Check-in rate as a percentage

hourlyBreakdown · array

Check-ins by hour

Show Hourly Data

hour · string

Hour in 24h format (e.g., “14:00”)

checkIns · number

Number of check-ins during this hour

peakHours · array

Top 4 busiest hours

Show Peak Hour Data

hour · string

Hour in 24h format

count · number

Number of check-ins

geography · object

Geographic distribution of ticket buyers

Show Geography Object

cities · array

Top cities by ticket sales

Show City Data

city · string

City name

count · number

Number of tickets sold

percentage · number

Percentage of total sales

countries · array

Top countries by ticket sales

Show Country Data

country · string

Country name or code

count · number

Number of tickets sold

percentage · number

Percentage of total sales

cURL

curl -X GET https://api.gatepass.io/api/analytics/clx1234567890 \
  -H "Authorization: Bearer YOUR_TOKEN"

Success Response

{
  "totalRevenue": 125500.00,
  "ticketsSold": 1247,
  "totalTickets": 1500,
  "salesByDay": [
    {
      "date": "2024-05-01",
      "sales": 45
    },
    {
      "date": "2024-05-02",
      "sales": 78
    },
    {
      "date": "2024-05-03",
      "sales": 120
    },
    {
      "date": "2024-05-04",
      "sales": 156
    }
  ],
  "salesByTier": [
    {
      "name": "VIP",
      "sold": 98,
      "total": 100
    },
    {
      "name": "Premium",
      "sold": 234,
      "total": 300
    },
    {
      "name": "General",
      "sold": 915,
      "total": 1100
    }
  ],
  "attendance": {
    "checkIns": 1089,
    "noShows": 158,
    "checkInRate": 87.3,
    "hourlyBreakdown": [
      {
        "hour": "10:00",
        "checkIns": 45
      },
      {
        "hour": "11:00",
        "checkIns": 123
      },
      {
        "hour": "12:00",
        "checkIns": 234
      },
      {
        "hour": "13:00",
        "checkIns": 189
      },
      {
        "hour": "14:00",
        "checkIns": 267
      },
      {
        "hour": "15:00",
        "checkIns": 156
      },
      {
        "hour": "16:00",
        "checkIns": 75
      }
    ],
    "peakHours": [
      {
        "hour": "14:00",
        "count": 267
      },
      {
        "hour": "12:00",
        "count": 234
      },
      {
        "hour": "13:00",
        "count": 189
      },
      {
        "hour": "15:00",
        "count": 156
      }
    ]
  },
  "geography": {
    "cities": [
      {
        "city": "Lagos",
        "count": 567,
        "percentage": 45.5
      },
      {
        "city": "Abuja",
        "count": 234,
        "percentage": 18.8
      },
      {
        "city": "Port Harcourt",
        "count": 156,
        "percentage": 12.5
      },
      {
        "city": "Ibadan",
        "count": 123,
        "percentage": 9.9
      },
      {
        "city": "Kano",
        "count": 89,
        "percentage": 7.1
      }
    ],
    "countries": [
      {
        "country": "Nigeria",
        "count": 1098,
        "percentage": 88.0
      },
      {
        "country": "Ghana",
        "count": 78,
        "percentage": 6.3
      },
      {
        "country": "Kenya",
        "count": 45,
        "percentage": 3.6
      },
      {
        "country": "South Africa",
        "count": 26,
        "percentage": 2.1
      }
    ]
  }
}

Access Control

Only the event organizer (creator) can access analytics for their events. Attempting to view analytics for events created by other users will result in a 403 Forbidden error.

Use Cases

Sales Performance

Track daily sales trends to identify:

• Peak sales periods
• Impact of marketing campaigns
• Last-minute ticket purchases
• Tier popularity

Attendance Optimization

Use attendance data to:

• Plan staff allocation during peak hours
• Identify optimal check-in start times
• Reduce wait times at entry
• Predict no-show rates

Geographic Insights

Understand your audience to:

• Target marketing in high-performing regions
• Plan future event locations
• Arrange transportation and accommodations
• Tailor content to regional preferences

Analytics are updated in real-time as tickets are sold and attendees check in. Refresh the data during your event to monitor live attendance.

EventAnalytics Model

Analytics data is also stored in the EventAnalytics table with daily snapshots:

• ticketsSold: Cumulative tickets sold
• revenue: Total revenue
• uniqueBuyers: Number of unique customers
• checkIns: Total check-ins
• checkInRate: Attendance percentage
• noShows: Tickets purchased but not used
• topCountries: JSON array of country data
• topCities: JSON array of city data
• hourlyBreakdown: JSON array of time-based patterns

Chart-Ready Data

All response arrays are formatted for direct use with charting libraries:

// Line chart: Sales over time
const salesChart = {
  labels: analytics.salesByDay.map(d => d.date),
  data: analytics.salesByDay.map(d => d.sales)
};

// Bar chart: Check-ins by hour
const attendanceChart = {
  labels: analytics.attendance.hourlyBreakdown.map(h => h.hour),
  data: analytics.attendance.hourlyBreakdown.map(h => h.checkIns)
};

// Pie chart: Sales by tier
const tierChart = {
  labels: analytics.salesByTier.map(t => t.name),
  data: analytics.salesByTier.map(t => t.sold)
};