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Overview

GatePass provides a powerful analytics dashboard for event organizers to track sales performance, attendee demographics, check-in patterns, and revenue metrics in real-time.

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Accessing Analytics

Navigate to the analytics dashboard:
API Endpoint
GET /api/analytics/organizer/stats      # Overall organizer stats
GET /api/analytics/:eventId             # Specific event analytics
Access analytics from your organizer dashboard:
  1. Click Analytics in the main navigation
  2. Select an event from the dropdown
  3. Choose a time range (24h, 7d, 30d, 90d)
  4. Explore different analytics tabs

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Key Metrics

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Revenue Metrics

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totalRevenue
float
required
Total revenue generated from ticket sales (including platform fees)
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ticketsSold
integer
required
Total number of tickets sold across all tiers
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averageOrderValue
float
Average revenue per order (totalRevenue / number of orders)
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revenueGrowth
float
Percentage growth compared to previous period

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Example Response

Analytics Response
{
  "totalRevenue": 4500000.00,
  "ticketsSold": 487,
  "totalTickets": 550,
  "salesByDay": [
    { "date": "2026-03-01", "sales": 45 },
    { "date": "2026-03-02", "sales": 67 },
    { "date": "2026-03-03", "sales": 89 }
  ],
  "salesByTier": [
    { "name": "Early Bird", "sold": 100, "total": 100 },
    { "name": "General Admission", "sold": 337, "total": 400 },
    { "name": "VIP Pass", "sold": 50, "total": 50 }
  ],
  "attendance": {
    "checkIns": 412,
    "noShows": 75,
    "checkInRate": 84.6,
    "hourlyBreakdown": [
      { "hour": "09:00", "checkIns": 45 },
      { "hour": "10:00", "checkIns": 89 },
      { "hour": "11:00", "checkIns": 123 }
    ],
    "peakHours": [
      { "hour": "11:00", "count": 123 },
      { "hour": "10:00", "count": 89 }
    ]
  },
  "geography": {
    "cities": [
      { "city": "Lagos", "count": 287, "percentage": 58.9 },
      { "city": "Abuja", "count": 103, "percentage": 21.1 },
      { "city": "Ibadan", "count": 45, "percentage": 9.2 }
    ],
    "countries": [
      { "country": "Nigeria", "count": 452, "percentage": 92.8 },
      { "country": "Ghana", "count": 25, "percentage": 5.1 },
      { "country": "Kenya", "count": 10, "percentage": 2.1 }
    ]
  }
}

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Analytics Tabs

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Sales Analytics

Track ticket sales performance:
Visualizations:
  • Area chart showing daily ticket sales
  • Line chart for revenue trends
  • Cumulative sales progress
Insights:
  • Identify peak sales periods
  • Track early bird effectiveness
  • Monitor sales velocity
Visualizations:
  • Pie chart of sales by tier
  • Bar chart comparing tier performance
Metrics:
  • Sold vs. available per tier
  • Revenue contribution by tier
  • Popular tier identification
Tier Performance
{
  "name": "VIP Pass",
  "sold": 50,
  "total": 50,
  "revenue": 1250000,  // 50 × ₦25,000
  "percentageSold": 100,
  "revenueContribution": 27.8
}
Breakdown:
  • Crypto vs. Fiat payment distribution
  • Payment gateway performance (Paystack, Flutterwave, M-Pesa)
  • Currency distribution (NGN, GHS, KES, etc.)
Example:
Payment Distribution
{
  "cryptocurrency": 35.2,  // 35.2% of payments
  "creditCard": 64.8,      // 64.8% via fiat
  "gateways": {
    "paystack": 342,
    "flutterwave": 89,
    "mpesa": 12
  }
}

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Attendance Analytics

Monitor event check-ins and attendance patterns:
Hourly Breakdown:
  • Bar chart showing check-ins by hour
  • Identify rush hours and bottlenecks
  • Optimize staff allocation
Hourly Check-Ins
{
  "hourlyBreakdown": [
    { "hour": "09:00", "checkIns": 45 },
    { "hour": "10:00", "checkIns": 89 },
    { "hour": "11:00", "checkIns": 123 },  // Peak hour
    { "hour": "12:00", "checkIns": 98 },
    { "hour": "13:00", "checkIns": 57 }
  ]
}
Key Metrics:
  • Total checked in vs. tickets sold
  • No-show rate
  • Check-in rate percentage
Calculations:
Attendance Metrics
const checkIns = 412;
const ticketsSold = 487;
const noShows = ticketsSold - checkIns; // 75
const checkInRate = (checkIns / ticketsSold) * 100; // 84.6%
Top Check-In Hours:
  • Ranked list of busiest hours
  • People count per time slot
  • Helps plan future events
Peak Hours
[
  { "hour": "11:00", "count": 123 },
  { "hour": "10:00", "count": 89 },
  { "hour": "12:00", "count": 98 },
  { "hour": "09:00", "count": 45 }
]

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Geographic Analytics

Understand where your attendees come from:
Metrics:
  • City-wise attendee distribution
  • Percentage breakdown
  • Count of attendees per city
City Distribution
{
  "cities": [
    {
      "city": "Lagos",
      "count": 287,
      "percentage": 58.9
    },
    {
      "city": "Abuja",
      "count": 103,
      "percentage": 21.1
    },
    {
      "city": "Ibadan",
      "count": 45,
      "percentage": 9.2
    },
    {
      "city": "Port Harcourt",
      "count": 32,
      "percentage": 6.6
    }
  ]
}
Geographic data is extracted from billing addresses and user profiles.
Country Distribution:
  • Primary vs. international audience
  • Country-wise breakdown with percentages
Country Stats
{
  "countries": [
    { "country": "Nigeria", "count": 452, "percentage": 92.8 },
    { "country": "Ghana", "count": 25, "percentage": 5.1 },
    { "country": "Kenya", "count": 10, "percentage": 2.1 }
  ]
}

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Real-Time Analytics

Monitor live event activity:
Recent Transactions:
  • Real-time ticket purchases
  • Check-in notifications
  • Payment confirmations
Display Format:
Live Activity
interface Transaction {
  id: string;
  type: 'crypto' | 'fiat';
  buyer: string;
  amount: number;
  tickets: number;
  timestamp: string;
}
Live Metrics:
  • Active browsing sessions
  • Tickets in cart (pending checkout)
  • Pending payments
  • Sales in last hour
  • Revenue in last hour
Real-Time Stats
{
  "activeSessions": 47,
  "ticketsInCart": 23,
  "pendingPayments": 8,
  "salesLastHour": 12,
  "revenueLastHour": 96000
}
Gateway Status:
  • Paystack: Configured ✅
  • Flutterwave: Configured ✅
  • M-Pesa: Not configured ⚠️
  • Stripe: Not configured ⚠️
Gateway Health
{
  "gateways": {
    "paystack": { "configured": true, "status": "operational" },
    "flutterwave": { "configured": true, "status": "operational" },
    "mpesa": { "configured": false, "status": "unavailable" },
    "stripe": { "configured": false, "status": "unavailable" }
  }
}

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EventAnalytics Model

Analytics data is stored in the database:
EventAnalytics Schema
model EventAnalytics {
  id        String   @id @default(cuid())
  date      DateTime @default(now())
  
  // Sales metrics
  ticketsSold    Int @default(0)
  revenue        Float @default(0)
  uniqueBuyers   Int @default(0)
  
  // Check-in metrics
  checkIns       Int @default(0)
  checkInRate    Float @default(0)  // Percentage
  noShows        Int @default(0)
  
  // Geographic data (JSON strings)
  topCountries   String?  // [{country: "NG", count: 10}, ...]
  topCities      String?  // [{city: "Lagos", count: 5}, ...]
  
  // Time-based data
  hourlyBreakdown String? // Check-in patterns by hour

  // Relations
  event   Event  @relation(fields: [eventId], references: [id])
  eventId String

  @@unique([eventId, date])
  @@map("event_analytics")
}

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API Routes

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Organizer Stats

Get overall statistics across all events: 
curl https://api.gatepass.com/analytics/organizer/stats \
  -H "Authorization: Bearer YOUR_TOKEN"
Response:
Organizer Stats
{
  "totalRevenue": 12500000,
  "ticketsSold": 1847,
  "activeEvents": 3,
  "totalEvents": 8,
  "revenueGrowth": 23.5,
  "ticketsGrowth": 18.2,
  "recentSales": [
    {
      "id": "order_001",
      "buyer": "[email protected]",
      "amount": 16000,
      "tickets": 2,
      "timestamp": "2026-03-06T14:23:45.000Z",
      "eventName": "Tech Conference 2024"
    }
  ]
}

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Event-Specific Analytics

Get detailed analytics for a specific event:
Get Event Analytics
curl https://api.gatepass.com/analytics/clxyz123abc \
  -H "Authorization: Bearer YOUR_TOKEN"
Returns the comprehensive analytics object shown earlier in this guide.

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Exporting Data

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CSV Export

Export analytics as CSV for further analysis:
Export CSV
function exportAnalytics() {
  const rows = [
    ['metric', 'value'],
    ['totalRevenue', totalRevenue],
    ['ticketsSold', ticketsSold],
    ['checkInRate', checkInRate]
  ];
  
  // Add daily sales
  salesByDay.forEach(point => {
    rows.push([`salesByDay_${point.date}`, point.sales]);
  });
  
  const csv = rows.map(r => r.join(',')).join('\n');
  
  // Download
  const blob = new Blob([csv], { type: 'text/csv' });
  const url = URL.createObjectURL(blob);
  const a = document.createElement('a');
  a.href = url;
  a.download = 'gatepass_analytics.csv';
  a.click();
}

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JSON Export

Download raw JSON data:
Export JSON
const data = await fetch(`/api/analytics/${eventId}`).then(r => r.json());
const blob = new Blob([JSON.stringify(data, null, 2)], {
  type: 'application/json'
});
const url = URL.createObjectURL(blob);
const a = document.createElement('a');
a.href = url;
a.download = 'analytics.json';
a.click();

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Visualizations

The dashboard uses Recharts for data visualization:

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Sales Chart (Area)

Sales Area Chart
<ResponsiveContainer width="100%" height={300}>
  <AreaChart data={salesByDay}>
    <CartesianGrid strokeDasharray="3 3" />
    <XAxis dataKey="date" />
    <YAxis />
    <Tooltip />
    <Area 
      type="monotone" 
      dataKey="sales" 
      stroke="#7C3AED" 
      fill="#7C3AED" 
      fillOpacity={0.6} 
    />
  </AreaChart>
</ResponsiveContainer>

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Tier Distribution (Pie)

Pie Chart
<PieChart>
  <Pie
    data={salesByTier}
    cx="50%"
    cy="50%"
    outerRadius={80}
    fill="#A78BFA"
    dataKey="sold"
    label={({ name, percent }) => `${name} ${Math.round(percent * 100)}%`}
  >
    {salesByTier.map((entry, index) => (
      <Cell key={`cell-${index}`} fill={COLORS[index % COLORS.length]} />
    ))}
  </Pie>
</PieChart>

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Check-In Pattern (Bar)

Bar Chart
<BarChart data={attendance.hourlyBreakdown}>
  <CartesianGrid strokeDasharray="3 3" />
  <XAxis dataKey="hour" />
  <YAxis />
  <Tooltip />
  <Bar dataKey="checkIns" fill="#5B21B6" />
</BarChart>

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Brand Colors

Use these colors in custom analytics visualizations:
GatePass Brand Colors
--primary: #7C3AED;    /* Purple */
--secondary: #A78BFA;  /* Light Purple */
--accent: #5B21B6;     /* Dark Purple */
--success: #10B981;    /* Green */
--warning: #F59E0B;    /* Orange */
--error: #EF4444;      /* Red */

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Best Practices

Regular Monitoring

Check analytics daily during sale periods to identify and address issues quickly.

Trend Analysis

Compare analytics across events to identify successful strategies.

Data-Driven Decisions

Use tier performance data to optimize pricing for future events.

Export Regularly

Export analytics data for long-term record keeping and reporting.

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Troubleshooting

Analytics refresh every 5 minutes. To force a refresh:
  • Click the Refresh button
  • Clear browser cache
  • Check if there are recent orders/check-ins
Geographic data requires:
  • Billing address during fiat payments
  • User profile location (optional)
Crypto purchases may lack geographic info unless user profile is complete.
Common reasons:
  • Event hasn’t happened yet (0% check-in rate)
  • Some attendees haven’t arrived
  • Technical issues at check-in (verify scanner working)

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Next Steps

Creating Events

Create your next event using insights from analytics

Check-In Management

Learn how to efficiently check in attendees

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