Field Transformations

Transformations modify data as it moves between systems. They help normalize data formats, clean up values, and handle the differences between how platforms store information.

Why Use Transformations?

Different systems store data in different formats:

Phone numbers: (555) 123-4567 vs +15551234567 vs 555-123-4567
Names: John Doe vs John + Doe (separate fields)
Dates: 2024-01-15 vs 01/15/2024 vs January 15, 2024
Addresses: Single line vs structured fields

Transformations ensure data is converted to the format each system expects.

Available Transformations

Text Transformations

Transform	Description	Example
`uppercase`	Convert to uppercase	`john doe` → `JOHN DOE`
`lowercase`	Convert to lowercase	`JOHN@EXAMPLE.COM` → `john@example.com`
`titlecase`	Capitalize first letter of each word	`john doe` → `John Doe`
`trim`	Remove leading/trailing whitespace	`John` → `John`

Phone Number Transformations

Transform	Description	Example
`phone_e164`	Format to E.164 international standard	`(555) 123-4567` → `+15551234567`
`phone_national`	Format for national display	`+15551234567` → `(555) 123-4567`
`phone_digits`	Extract digits only	`(555) 123-4567` → `5551234567`

Date Transformations

Transform	Description	Example
`date_iso`	Convert to ISO 8601 format	`01/15/2024` → `2024-01-15`
`date_us`	Convert to US format (MM/DD/YYYY)	`2024-01-15` → `01/15/2024`
`datetime_iso`	Full ISO 8601 with time	`2024-01-15T14:30:00Z`
`timestamp_unix`	Convert to Unix timestamp	`2024-01-15` → `1705276800`

Name Transformations

Transform	Description	Example
`split_first_name`	Extract first name from full name	`John Doe` → `John`
`split_last_name`	Extract last name from full name	`John Doe` → `Doe`
`combine_name`	Combine first + last into full name	`John` + `Doe` → `John Doe`

Address Transformations

Transform	Description
`address_single_line`	Combine address components into one line
`address_parse_street`	Extract street from full address
`address_parse_city`	Extract city from full address
`address_parse_state`	Extract state/province from full address
`address_parse_zip`	Extract postal code from full address

Value Mapping

Map specific values to different values:

Transform	Description
`value_map`	Map source values to target values (e.g., lifecycle stages)
`default_value`	Use a default if source is empty
`boolean_to_string`	Convert true/false to Yes/No or custom strings

Applying Transformations

In the Mapping Editor

When creating or editing a mapping:

Select a field pair
Click the Transform dropdown
Choose a transformation
Configure any options (e.g., default country for phone formatting)

Chaining Transformations

You can apply multiple transformations in sequence:

Source: "  john doe  "
  ↓ trim
  ↓ titlecase
Result: "John Doe"

Transformations are applied in order from top to bottom.

Common Transformation Patterns

HubSpot to Jobber Phone Numbers

HubSpot stores phones in various formats. Jobber prefers E.164:

Transform: phone_e164
Default Country: US

Input:  (555) 123-4567
Output: +15551234567

Splitting Full Names

Some systems use a single name field, others use first/last:

Source Field: fullname ("John Doe")

Field Pair 1:
  Target: first_name
  Transform: split_first_name
  Result: "John"

Field Pair 2:
  Target: last_name
  Transform: split_last_name
  Result: "Doe"

Pipeline Stage Mapping

Map deal stages between CRMs:

Transform: value_map

Mappings:
  "Appointment Scheduled" → "Lead"
  "Qualified to Buy" → "Qualified"
  "Closed Won" → "Won"
  "Closed Lost" → "Lost"

Default: "Other"

Email Normalization

Ensure consistent email formatting:

Transform: lowercase, trim

Input:  "  John.Doe@EXAMPLE.COM  "
Output: "john.doe@example.com"

Handling Transformation Errors

When a transformation fails:

Log the error – The sync log shows which record and field failed
Skip the field – Other fields still sync
Retry options – Configure whether to retry or skip

Common causes:

Invalid phone number format
Date parsing failures
Missing required source data

Next Steps

Bidirectional Sync – Handle two-way data flow
Conflict Resolution – When the same record is updated in both systems
Creating Mappings – Back to mapping basics