CRM Complete Batch - Full Personality Analysis

Process up to 10 profiles concurrently with complete personality analysis including OCEAN, MBTI, and buyer playbooks.

When to Use

See the consolidated guidance: When to Use Each Batch Endpoint

Endpoint Details

Property	Value
Endpoint	POST /crm_batch
Cost	2 credits × number of profiles
Max Profiles	10 per request
Tier Required	Complete
Processing Time	~8-10 seconds for 5 profiles

Overview

The CRM Complete Batch endpoint provides comprehensive personality analysis for multiple profiles simultaneously, including OCEAN scores, MBTI types, and detailed buyer playbooks. You only pay for successfully analyzed profiles.

Key Features

• ✅ Concurrent Processing: Up to 10 profiles analyzed simultaneously
• ✅ Pay-per-Success: Only charged for successfully analyzed profiles
• ✅ Time Efficient: ~65% faster than sequential single requests
• ✅ Complete Analysis: OCEAN, MBTI, buyer manual, impact language, and more

Request

Headers

Header	Required	Value
Content-Type	Yes	application/json
x-api-key	Yes	Your API key

Request Body

{
  "profiles": [
    {
      "primeiro_nome": "Satya",
      "ultimo_nome": "Nadella",
      "empresa": "Microsoft",
      "contexto_adicional": "Optional context"
    },
    {
      "primeiro_nome": "Sundar",
      "ultimo_nome": "Pichai",
      "empresa": "Google"
    }
  ]
}

Parameters

Parameter	Type	Required	Description
profiles	array	Yes	Array of profile objects (max 10)
profiles[].primeiro_nome	string	Yes	First name
profiles[].ultimo_nome	string	Yes	Last name
profiles[].empresa	string	Yes	Company name
profiles[].contexto_adicional	string	No	Additional context

Example Request

cURL

curl -X POST "https://api.fluenceinsights.com/crm_batch" \
  -H "Content-Type: application/json" \
  -H "x-api-key: your-api-key-here" \
  -d '{
    "profiles": [
      {"primeiro_nome": "Satya", "ultimo_nome": "Nadella", "empresa": "Microsoft"},
      {"primeiro_nome": "Sundar", "ultimo_nome": "Pichai", "empresa": "Google"}
    ]
  }'

Python

import requests

API_URL = "https://api.fluenceinsights.com/crm_batch"
API_KEY = "your-api-key-here"

profiles = [
    {"primeiro_nome": "Satya", "ultimo_nome": "Nadella", "empresa": "Microsoft"},
    {"primeiro_nome": "Sundar", "ultimo_nome": "Pichai", "empresa": "Google"},
    {"primeiro_nome": "Tim", "ultimo_nome": "Cook", "empresa": "Apple"}
]

response = requests.post(
    API_URL,
    headers={
        "Content-Type": "application/json",
        "x-api-key": API_KEY
    },
    json={"profiles": profiles}
)

if response.status_code == 200:
    data = response.json()
    summary = data['batch_summary']
    print(f"✅ Processed {summary['successful']}/{summary['total_profiles']} profiles")
    print(f"⚡ Time: {summary['processing_time_ms']/1000:.1f}s")
    print(f"💰 Credits: {data['billing_info']['creditos_utilizados']}")

    # Access personality insights
    for result in data['results']:
        if result['success']:
            analise = result['analise']
            print(f"\n{result['input']['primeiro_nome']} {result['input']['ultimo_nome']}:")
            print(f"  MBTI: {analise['mbti']['tipo']}")
            print(f"  Openness: {analise['ocean']['openness']:.1f}")
else:
    print(f"❌ Error: {response.json()}")

JavaScript/Node.js

const axios = require('axios');

const API_URL = 'https://api.fluenceinsights.com/crm_batch';
const API_KEY = 'your-api-key-here';

async function analyzeBatch() {
  const profiles = [
    { primeiro_nome: 'Satya', ultimo_nome: 'Nadella', empresa: 'Microsoft' },
    { primeiro_nome: 'Sundar', ultimo_nome: 'Pichai', empresa: 'Google' }
  ];

  try {
    const response = await axios.post(
      API_URL,
      { profiles },
      {
        headers: {
          'Content-Type': 'application/json',
          'x-api-key': API_KEY
        }
      }
    );

    const { batch_summary, results, billing_info } = response.data;
    console.log(`✅ ${batch_summary.successful}/${batch_summary.total_profiles} profiles`);
    console.log(`💰 Credits: ${billing_info.creditos_utilizados}`);

    results.forEach(result => {
      if (result.success) {
        const { analise, input } = result;
        console.log(`\n${input.primeiro_nome}: ${analise.mbti.tipo}`);
      }
    });
  } catch (error) {
    console.error('❌ Error:', error.response?.data || error.message);
  }
}

analyzeBatch();

Response

Success Response (200)

{
  "success": true,
  "batch_summary": {
    "total_profiles": 2,
    "successful": 2,
    "failed": 0,
    "processing_time_ms": 5234.67
  },
  "results": [
    {
      "success": true,
      "profile_index": 0,
      "input": {
        "primeiro_nome": "Satya",
        "ultimo_nome": "Nadella",
        "empresa": "Microsoft"
      },
      "analise": {
        "cargo": "Chairman and CEO",
        "tipo_stakeholder": "C-Level Executive",
        "ocean": {
          "openness": 8.5,
          "conscientiousness": 9.0,
          "extraversion": 7.5,
          "agreeableness": 8.0,
          "neuroticism": 3.0
        },
        "mbti": {
          "tipo": "ENTJ",
          "descricao": "The Commander - Strategic, ambitious, natural leader",
          "pontos_fortes": [
            "Strategic thinking",
            "Decisive leadership",
            "Innovation focus"
          ],
          "comunicacao_preferida": "Direct, goal-oriented, data-driven"
        },
        "manual_comprador": {
          "dores": [
            "Digital transformation challenges",
            "Cloud migration complexity",
            "Competitive market pressure"
          ],
          "ganhos_procurados": [
            "Innovation acceleration",
            "Market leadership",
            "Business growth"
          ],
          "prioridades": [
            "Technology innovation",
            "Customer success",
            "Organizational culture"
          ]
        },
        "linguagem_impacto": [
          "Innovation drives growth",
          "Empowering transformation",
          "Customer-first approach"
        ]
      }
    },
    {
      "success": true,
      "profile_index": 1,
      "input": {
        "primeiro_nome": "Sundar",
        "ultimo_nome": "Pichai",
        "empresa": "Google"
      },
      "analise": {
        "cargo": "CEO",
        "tipo_stakeholder": "C-Level Executive",
        "ocean": {...},
        "mbti": {...},
        "manual_comprador": {...},
        "linguagem_impacto": [...]
      }
    }
  ],
  "billing_info": {
    "creditos_por_perfil": 2,
    "perfis_processados": 2,
    "creditos_utilizados": 4,
    "creditos_restantes": 196,
    "tipo_plano": "complete"
  }
}

Complete Tier Analysis Fields

Field	Type	Description
cargo	string	Current position/title
tipo_stakeholder	string	Stakeholder classification
ocean	object	OCEAN personality scores (0-10)
ocean.openness	float	Openness to experience
ocean.conscientiousness	float	Conscientiousness level
ocean.extraversion	float	Extraversion level
ocean.agreeableness	float	Agreeableness level
ocean.neuroticism	float	Neuroticism level
mbti	object	MBTI personality type
mbti.tipo	string	4-letter MBTI type (e.g., ENTJ)
mbti.descricao	string	Personality description
mbti.pontos_fortes	array	Key strengths
mbti.comunicacao_preferida	string	Preferred communication style
manual_comprador	object	Buyer playbook
manual_comprador.dores	array	Pain points
manual_comprador.ganhos_procurados	array	Desired gains
manual_comprador.prioridades	array	Top priorities
linguagem_impacto	array	Impact language phrases

Error Responses

Insufficient Credits (402)

{
  "error": "Insufficient credits",
  "message": "Batch requires 20 credits but you have 15",
  "profiles_requested": 10,
  "credits_per_profile": 2,
  "credits_available": 15
}

Batch Size Exceeded (400)

{
  "error": "Batch size exceeds maximum",
  "message": "Maximum 10 profiles per batch, received 15",
  "max_batch_size": 10,
  "profiles_received": 15
}

Partial Batch Failure (200)

{
  "success": true,
  "batch_summary": {
    "total_profiles": 3,
    "successful": 2,
    "failed": 1
  },
  "results": [
    {
      "success": true,
      "profile_index": 0,
      "analise": {...}
    },
    {
      "success": false,
      "profile_index": 1,
      "input": {"primeiro_nome": "John", "empresa": "Unknown"},
      "error": "Profile not found",
      "error_code": "PROFILE_NOT_FOUND"
    },
    {
      "success": true,
      "profile_index": 2,
      "analise": {...}
    }
  ],
  "billing_info": {
    "creditos_utilizados": 4
  }
}

Credit Calculation

Scenario	Calculation	Total Credits
5 profiles, all successful	2 × 5	10 credits
10 profiles, all successful	2 × 10	20 credits
10 profiles, 8 successful	2 × 8	16 credits

Note: You are only charged for successfully analyzed profiles. Failed analyses do not consume credits.

Performance Benchmarks

Profiles	Single Requests	Batch Request	Time Saved
5 profiles	~20-25 sec	~8-10 sec	60% faster
10 profiles	~40-50 sec	~15-20 sec	65% faster

Use Cases

Sales Intelligence

def build_sales_intelligence(prospects):
    """Analyze prospects for sales team"""
    batch_size = 10
    enriched_prospects = []

    for i in range(0, len(prospects), batch_size):
        batch = prospects[i:i + batch_size]
        response = requests.post(API_URL, json={"profiles": batch}, headers=headers)

        if response.status_code == 200:
            for result in response.json()['results']:
                if result['success']:
                    enriched_prospects.append({
                        'name': f"{result['input']['primeiro_nome']} {result['input']['ultimo_nome']}",
                        'company': result['input']['empresa'],
                        'mbti': result['analise']['mbti']['tipo'],
                        'communication_style': result['analise']['mbti']['comunicacao_preferida'],
                        'pain_points': result['analise']['manual_comprador']['dores']
                    })

    return enriched_prospects

Personality-Based Segmentation

def segment_by_personality(batch_results):
    """Segment contacts by MBTI type"""
    segments = {}

    for result in batch_results['results']:
        if result['success']:
            mbti = result['analise']['mbti']['tipo']
            if mbti not in segments:
                segments[mbti] = []
            segments[mbti].append(result)

    return segments

Best Practices

1. Batch Size Optimization

• Recommended: 10 profiles per batch (maximum)
• For large datasets: Process in sequential batches of 10
• Add delays: 0.5-1s between batches for large-scale processing

2. Credit Management

# Verify sufficient credits before batch processing
credits_needed = len(profiles) * 2  # 2 credits per profile
current_credits = check_credits(API_KEY)

if current_credits < credits_needed:
    print(f"⚠️ Insufficient credits: {current_credits} available, {credits_needed} needed")
else:
    process_batch(profiles)

3. Processing Large Datasets

def process_large_dataset(profiles, batch_size=10):
    """Process large datasets in optimized batches"""
    results = []
    total_credits = 0

    for i in range(0, len(profiles), batch_size):
        batch = profiles[i:i + batch_size]
        response = requests.post(API_URL, json={"profiles": batch}, headers=headers)

        if response.status_code == 200:
            data = response.json()
            results.extend(data['results'])
            total_credits += data['billing_info']['creditos_utilizados']

            print(f"Progress: {min(i + batch_size, len(profiles))}/{len(profiles)}")
            print(f"Credits used: {total_credits}")

        time.sleep(0.5)  # Rate limiting

    return results, total_credits

When to Use This Endpoint

✅ Use CRM Complete Batch When:

• Need full personality insights for multiple profiles
• Building personality-based sales strategies
• Segmenting contacts by communication preferences
• Analyzing team dynamics at scale
• Processing 2-10 profiles at once

❌ Use Single Endpoint When:

• Analyzing only 1 profile
• Real-time interactive analysis needed
• Immediate feedback required

Comparison with Other Tiers

Feature	Basic Batch	Complete Batch	Plus Batch
Credits per profile	1	2	3
OCEAN scores	❌	✅	✅
MBTI type	❌	✅	✅
Buyer playbook	Basic	Complete	Complete
AI insights	❌	❌	✅
Processing time	~8-10s	~8-10s	~15-25s

Next Steps

• View CRM Basic Batch (1 credit) →
• View CRM Plus Batch (3 credits) →
• Learn about single profile analysis →
• Explore credit system →