Building Automated Workflows with TailURL API

// Create a new short URL
const response = await fetch('https://api.tailurl.com/v1/urls', {
  method: 'POST',
  headers: {
    'Authorization': 'Bearer YOUR_API_KEY',
    'Content-Type': 'application/json'
  },
  body: JSON.stringify({
    url: 'https://example.com/long-url',
    title: 'Product Launch Page',
    custom_path: 'launch2025',
    tags: ['product', 'marketing'],
    utm_source: 'email',
    utm_campaign: 'q4_launch'
  })
});

const shortUrl = await response.json();
console.log('Short URL:', shortUrl.short_url);

// Configure webhook for click events
const webhook = await fetch('https://api.tailurl.com/v1/webhooks', {
  method: 'POST',
  headers: {
    'Authorization': 'Bearer YOUR_API_KEY',
    'Content-Type': 'application/json'
  },
  body: JSON.stringify({
    url: 'https://your-app.com/webhook/handler',
    events: ['click', 'conversion'],
    secret: 'your_webhook_secret',
    active: true
  })
});

// Webhook payload example:
// {
//   "event": "click",
//   "short_url": "https://tl.ur/abc123",
//   "original_url": "https://example.com/product",
//   "timestamp": "2025-09-29T10:30:00Z",
//   "user_agent": "Mozilla/5.0...",
//   "ip_address": "192.168.1.1",
//   "referrer": "https://twitter.com",
//   "location": {"country": "NL", "city": "Amsterdam"}
// }

import requests
import pandas as pd
from datetime import datetime, timedelta

# Fetch analytics data
def get_campaign_analytics(campaign_id, days=30):
    end_date = datetime.now()
    start_date = end_date - timedelta(days=days)
    
    response = requests.get(
        f'https://api.tailurl.com/v1/analytics/campaign/{campaign_id}',
        headers={'Authorization': f'Bearer {API_KEY}'},
        params={
            'start_date': start_date.isoformat(),
            'end_date': end_date.isoformat(),
            'group_by': 'day'
        }
    )
    
    data = response.json()
    df = pd.DataFrame(data['analytics'])
    
    # Calculate key metrics
    total_clicks = df['clicks'].sum()
    conversion_rate = df['conversions'].sum() / total_clicks * 100
    top_referrers = df.groupby('referrer')['clicks'].sum().head(5)
    
    return {
        'total_clicks': total_clicks,
        'conversion_rate': conversion_rate,
        'top_referrers': top_referrers.to_dict()
    }