data mapping – ActiveProspect

Data Mapping and Transformation

Master the art of transforming lead data between sources and recipients. Learn field mapping, data transformation, and standardization techniques for seamless data flow.

📍 You Are Here

You're learning how to map and transform data as it flows through LeadConduit. Proper mapping ensures data arrives at destinations in the correct format, with the right field names, and properly validated.

🎯 What You'll Master

Field mapping fundamentals
Data transformation techniques
Value standardization
Complex mapping scenarios
Troubleshooting mapping issues

🗺️ Understanding Mappings

What Are Mappings?

Mappings connect data between systems:

Source Fields → LeadConduit Fields → Recipient Fields
Transform formats (phone: 555-1234 → 5551234)
Standardize values (California → CA)
Combine/split fields
Apply business logic

Why Mappings Matter

Without Proper Mapping:

❌ Data arrives in wrong format
❌ Required fields missing
❌ Integration failures
❌ Manual data cleanup

With Proper Mapping:

✅ Automatic format conversion
✅ Consistent data quality
✅ Seamless integrations
✅ Zero manual intervention

🔧 Basic Mapping Concepts

Direct Mapping

Simple one-to-one field connections:

Source Field    →    Destination Field
─────────────────────────────────────
email           →    email_address
phone           →    contact_phone
first_name      →    fname
last_name       →    lname

Field Naming Strategies

Source Variations:

Common Email Field Names:
- email
- email_address  
- emailAddress
- Email
- contact_email
- primary_email

Standardize in LeadConduit:

All map to → email

Data Types

Ensure compatible types:

String → String
Number → Number or String
Boolean → Boolean, 1/0, yes/no
Date → Various formats

🔄 Transformation Techniques

Format Transformations

Phone Formatting:

Input:  (555) 123-4567
Output: 5551234567

Input:  555.123.4567
Output: 5551234567

Input:  +1-555-123-4567
Output: 5551234567

Date Formatting:

Input:  2024-01-15T10:30:00Z
Output: 01/15/2024

Input:  January 15, 2024
Output: 2024-01-15

Value Standardization

State Codes:

Input:  California, CALIFORNIA, Calif., CA
Output: CA

Input:  New York, NEW YORK, N.Y., NY
Output: NY

Boolean Values:

Input:  true, True, TRUE, 1, yes, Yes
Output: true

Input:  false, False, FALSE, 0, no, No
Output: false

String Manipulation

Concatenation:

first_name: John
last_name: Doe
full_name: {first_name} {last_name} → "John Doe"

Extraction:

full_email: john.doe@example.com
username: {full_email.split('@')[0]} → "john.doe"
domain: {full_email.split('@')[1]} → "example.com"

Case Conversion:

Input:  JOHN DOE
Output: John Doe (Title Case)

Input:  john doe  
Output: JOHN DOE (Upper Case)

📝 Advanced Mapping Patterns

Conditional Mapping

Map based on conditions:

// Map lead type based on source
if (source == "Facebook") {
  lead_type = "Social"
} else if (source == "Google") {
  lead_type = "Search"  
} else {
  lead_type = "Direct"
}

// Map priority based on state
if (state in ["CA", "TX", "FL"]) {
  priority = "High"
} else {
  priority = "Normal"
}

Calculated Fields

Create new fields from existing data:

// Calculate age from birthdate
age = current_year - birth_year

// Score calculation
lead_score = (email_valid * 25) + 
             (phone_valid * 25) + 
             (state_matches * 50)

// Revenue estimation
estimated_value = base_value * conversion_rate * quality_score

Multi-Field Mapping

Combine multiple fields:

// Address compilation
full_address = {street} + ", " + {city} + ", " + {state} + " " + {zip}

// Phone preference
best_phone = {mobile_phone} || {work_phone} || {home_phone}

// Name formatting
display_name = {prefix} + " " + {first_name} + " " + {last_name} + " " + {suffix}

🎯 Mapping Configuration

Source Mapping

Configure how incoming data maps to LeadConduit:

Source Configuration:
  Field Mappings:
    - Source: "EmailAddress"
      LeadConduit: "email"
      Required: true
    
    - Source: "PhoneNumber"  
      LeadConduit: "phone_1"
      Transform: "digits_only"
      
    - Source: "FirstName"
      LeadConduit: "first_name"
      Transform: "title_case"

Recipient Mapping

Configure outbound field mapping:

Recipient Configuration:
  Field Mappings:
    - LeadConduit: "email"
      Destination: "contact_email"
      
    - LeadConduit: "phone_1"
      Destination: "primary_phone"
      Format: "+1{value}"
      
    - LeadConduit: "state"
      Destination: "mailing_state_code"
      Transform: "uppercase"

🔍 Common Mapping Scenarios

E-commerce Lead Mapping

// Source Fields → LeadConduit → Shopify
email → email → customer_email
phone → phone_1 → customer_phone
cart_value → purchase_amount → total_spent
items → product_interest → tags

Real Estate Lead Mapping

// Zillow → LeadConduit → CRM
property_interest → property_type → lead_property_type
price_range → budget → estimated_budget
timeline → purchase_timeline → urgency_score
area → target_location → search_area

Insurance Lead Mapping

// Form → LeadConduit → Carrier
dob → birth_date → applicant_age (calculated)
coverage_amount → requested_coverage → policy_amount
health_conditions → medical_history → risk_category
smoker → tobacco_use → rate_class

🚧 Troubleshooting Mappings

"Field Not Mapped"

Symptoms: Data missing at destination

Check:

Source field name exact match
Mapping configuration saved
Field contains data
No typos in field names

"Invalid Format"

Symptoms: Data rejected by recipient

Solutions:

Add format transformation
Verify expected format
Check data type compatibility
Test with sample data

"Mapping Not Working"

Debug Steps:

Check Events for raw data
Verify field appears in flow
Test transformation logic
Review recipient requirements

💡 Best Practices

Naming Conventions

Use Clear Names:

Good:
- email_address
- mobile_phone
- purchase_date

Bad:
- field1
- data
- misc

Documentation

Document Complex Mappings:

// Map timezone based on state
// Uses ET for eastern states, CT for central, etc.
// Default to PT if state unknown
timezone = state_to_timezone_map[state] || "PT"

Validation

Validate Before Mapping:

Check data exists
Verify format
Ensure type compatibility
Handle edge cases

Performance

Optimize Transformations:

Cache repeated calculations
Minimize complex operations
Use built-in functions
Batch similar transformations

📊 Mapping Patterns Library

Phone Number Patterns

// Remove all non-digits
phone.replace(/\D/g, '')

// Format as (XXX) XXX-XXXX
phone.replace(/(\d{3})(\d{3})(\d{4})/, '($1) $2-$3')

// Add country code
'+1' + phone

Email Patterns

// Lowercase all emails
email.toLowerCase()

// Extract domain
email.split('@')[1]

// Validate format
/^[^\s@]+@[^\s@]+\.[^\s@]+$/.test(email)

Name Patterns

// Title case
name.replace(/\w\S*/g, txt => 
  txt.charAt(0).toUpperCase() + txt.substr(1).toLowerCase()
)

// Remove extra spaces
name.trim().replace(/\s+/g, ' ')

// Extract initials
name.split(' ').map(n => n[0]).join('')

📋 Mapping Checklist

Before activating flow:

All required fields mapped
Transformations tested
Edge cases handled
Documentation added
Sample data validates
Recipient format confirmed
Error handling configured
Performance acceptable

Field Configuration - Field setup and validation
Types Reference - Data type details
Mappings Reference - Technical specifications

🔄 Mapping Mastery: Proper data mapping is the bridge between systems. Take time to understand requirements, test thoroughly, and document complex transformations for maintainable integrations!