CRM/CSV

Overview

Plasma provides powerful tools for managing customer relationship data through CSV files and direct CRM integrations.

Getting Started

1. Prepare Your Data

Ensure your CSV files follow the correct format:

customers.csv
contacts.csv
leads.csv
companies.csv

Example CSV structure:

customers.csv

id,name,email,phone,company,status,created_date
1,John Doe,john@example.com,+1234567890,Acme Corp,active,2024-01-15
2,Jane Smith,jane@example.com,+0987654321,Tech Ltd,inactive,2024-01-16

2. Import Data

Use Plasma’s import functionality:

import-csv.ts

import { csvImporter } from 'plasma';

const result = await csvImporter.import('customers.csv', {
  mapping: {
    'Customer Name': 'name',
    'Email Address': 'email',
    'Phone Number': 'phone',
  },
  validation: true,
  batchSize: 1000,
});

3. Validate & Process

Review imported data and handle errors:

if (result.errors.length > 0) {
  console.log('Import errors:', result.errors);
}

console.log(`Imported ${result.successful} records`);

Data Formats

Customer CSV Format

Required fields for customer data:

Field	Type	Required	Default	Description
id	string | number	✅	-	Unique customer identifier
name	string	✅	-	Customer full name
email	string	✅	-	Primary email address
phone	string	❌	-	Phone number with country code
company	string	❌	-	Associated company name
status	”active” | “inactive” | “prospect”	❌	“prospect”	Customer status

Lead CSV Format

leads.csv

id,name,email,source,score,stage,assigned_to
1,Alice Johnson,alice@test.com,website,85,qualified,john.doe
2,Bob Wilson,bob@test.com,referral,65,contacted,jane.smith

Lead scoring integration:

const leadProcessor = await csvImporter.processLeads('leads.csv', {
  scoreThreshold: 70,
  autoAssign: true,
  notifyAssignee: true,
});

Company CSV Format

companies.csv

id,name,domain,industry,size,revenue,location
1,Acme Corp,acme.com,Technology,500,10000000,New York
2,Tech Solutions,techsol.com,Consulting,50,2000000,California

Contact CSV Format

contacts.csv

id,first_name,last_name,email,title,company_id,department
1,John,Doe,john@acme.com,Manager,1,Sales
2,Jane,Smith,jane@acme.com,Developer,1,Engineering

CRM Integration

Salesforce Integration

Connect directly to Salesforce for real-time data sync:

salesforce-sync.ts

import { crmIntegration } from 'plasma';

const salesforce = await crmIntegration.connect('salesforce', {
  clientId: process.env.SALESFORCE_CLIENT_ID,
  clientSecret: process.env.SALESFORCE_CLIENT_SECRET,
  sandbox: false,
});

// Sync customers
const customers = await salesforce.getCustomers({
  lastModified: new Date('2024-01-01'),
});

// Export to CSV
await csvExporter.export(customers, 'salesforce-customers.csv');

HubSpot Integration

Connect to HubSpot for contact and deal management:

hubspot-sync.ts

const hubspot = await crmIntegration.connect('hubspot', {
  apiKey: process.env.HUBSPOT_API_KEY,
});

// Bulk import contacts
const contacts = await csvImporter.parseFile('contacts.csv');
await hubspot.bulkCreateContacts(contacts);

Custom CRM Integration

Build custom integrations for proprietary CRM systems:

custom-crm.ts

class CustomCRMIntegration {
  async importData(csvFile: string) {
    const data = await csvImporter.parse(csvFile);

    // Transform data to match CRM format
    const transformed = data.map((row) => ({
      externalId: row.id,
      fullName: `${row.first_name} ${row.last_name}`,
      emailAddress: row.email,
      // Custom mapping logic
    }));

    return this.bulkCreate(transformed);
  }
}

Data Validation

Ensure data quality with built-in validation:

validation.ts

import { validator } from 'plasma';

const schema = {
  email: validator.email(),
  phone: validator.phone(),
  name: validator.string().min(2).max(100),
  company: validator.string().optional(),
  status: validator.enum(['active', 'inactive', 'prospect']),
};

const validationResult = await csvImporter.validate('customers.csv', schema);

if (!validationResult.isValid) {
  console.log('Validation errors:', validationResult.errors);
}

Export Features

Export to multiple formats:

// Export filtered customer data
const activeCustomers = await crm.getCustomers({
  status: 'active',
  lastActivity: { since: '2024-01-01' },
});

// Export to CSV
await csvExporter.export(activeCustomers, 'active-customers.csv');

// Export to Excel
await excelExporter.export(activeCustomers, 'active-customers.xlsx');

// Export to JSON
await jsonExporter.export(activeCustomers, 'active-customers.json');

Batch Processing

Handle large datasets efficiently:

batch-processing.ts

const batchProcessor = csvImporter.createBatchProcessor({
  batchSize: 1000,
  concurrency: 5,
  retries: 3,
});

batchProcessor.onProgress((progress) => {
  console.log(`Processed ${progress.completed}/${progress.total} records`);
});

batchProcessor.onError((error, batch) => {
  console.error(`Batch ${batch.id} failed:`, error);
});

await batchProcessor.process('large-dataset.csv');

Best Practices

Performance Tip: Use batch processing for files larger than 10,000 records to maintain optimal performance.

• Validate data before import
• Use consistent field naming
• Include unique identifiers
• Implement error handling
• Monitor import progress
• Backup data before bulk operations

CRM Ready