Sales Leads REST API

const API_TOKEN = 'YOUR_API_TOKEN';

async function getSalesLeads() {
  const response = await fetch(
    'https://api.cernion.de/api/business-intelligence/sales-leads',
    {
      method: 'POST',
      headers: {
        'Authorization': `Bearer ${API_TOKEN}`,
        'Content-Type': 'application/json'
      },
      body: JSON.stringify({
        region: 'Bayern',
        installationType: 'solar',
        daysBack: 30,
        limit: 20,
        minScore: 70
      })
    }
  );
  
  const data = await response.json();
  
  console.log(`Total Leads: ${data.data.totalLeads}`);
  console.log(`Qualified: ${data.data.qualifiedLeads}`);
  console.log(`Total LTV: ${data.data.statistics.totalEstimatedLTV}\n`);
  
  // Top 5 Leads
  data.data.leads.slice(0, 5).forEach(lead => {
    console.log(`Lead ${lead.leadId}: Score ${lead.score}`);
    console.log(`  Address: ${lead.installation.address}`);
    console.log(`  Capacity: ${lead.installation.capacityKWp} kWp`);
    console.log(`  ARPU: ${lead.customerProfile.estimatedARPU}€`);
    console.log(`  Offer: ${lead.recommendations.primaryOffer}\n`);
  });
  
  return data;
}

getSalesLeads();

import requests
import pandas as pd

API_TOKEN = 'YOUR_API_TOKEN'

def get_sales_leads(region='Bayern', installation_type='solar', min_score=70):
    """Get qualified sales leads"""
    response = requests.post(
        'https://api.cernion.de/api/business-intelligence/sales-leads',
        headers={
            'Authorization': f'Bearer {API_TOKEN}',
            'Content-Type': 'application/json'
        },
        json={
            'region': region,
            'installationType': installation_type,
            'daysBack': 30,
            'limit': 50,
            'minScore': min_score
        }
    )
    
    data = response.json()['data']
    
    print(f'\n=== Sales Leads Report ===')
    print(f'Region: {data["region"]}')
    print(f'Total Leads: {data["totalLeads"]}')
    print(f'Qualified (Score ≥{min_score}): {data["qualifiedLeads"]}')
    print(f'Total LTV: {data["statistics"]["totalEstimatedLTV"]}\n')
    
    # Convert to DataFrame
    leads_df = pd.DataFrame(data['leads'])
    
    # Analysis
    print('Score Distribution:')
    print(f'High Quality (≥80): {len(leads_df[leads_df["score"] >= 80])}')
    print(f'Medium Quality (70-79): {len(leads_df[(leads_df["score"] >= 70) & (leads_df["score"] < 80)])}')
    print(f'Lower Quality (60-69): {len(leads_df[(leads_df["score"] >= 60) & (leads_df["score"] < 70)])}')
    
    # Top 10 Leads
    print(f'\n=== Top 10 Leads ===')
    top_leads = leads_df.nlargest(10, 'score')
    for idx, lead in top_leads.iterrows():
        print(f'{lead["leadId"]}: Score {lead["score"]} - {lead["installation"]["capacityKWp"]} kWp')
        print(f'  LTV: {lead["customerProfile"]["estimatedLTV"]}€')
        print(f'  Offer: {lead["recommendations"]["primaryOffer"]}\n')
    
    return data

if __name__ == '__main__':
    leads = get_sales_leads(region='Bayern', installation_type='solar', min_score=70)