Data ManagementTransform Data

Format, standardize, and transform CRM data in bulk.

Fix inconsistent names, phone numbers, addresses, job titles, and any text field across thousands of records at once, with chainable functions, conditional logic, and a preview of every change before it touches your CRM. Then automate it so the formatting holds.

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Preview before updateFull audit trailNo code, no export-reimport

Target field

Phone Number

3 functions

1
Remove non-digits
+(44) 161-868.8000 → 441618688000
2
Add international prefix
Country context: United Kingdom
3
Format as E.164
Result: +441618688000

Preview mode · no CRM changesRun preview

From one-off fix to reusable pipeline

If you can describe the cleanup step by step, you can turn it into a transformation that runs at scale.

Filter the right records, stack simple functions into the exact sequence you need, inspect every before-and-after value, and save the configuration so the next import is handled automatically.

Leave the spreadsheet loop behind

Export, wrangle, re-import, repeat.

The usual fix for inconsistent CRM data is a spreadsheet full of formulas or a one-off script one person understands. Both approaches create another risky write to the CRM, and neither leaves a reusable process behind. The next import breaks the field again, and the cleanup starts over.

How bulk CRM transformation works

First, filter the CRM records you intend to change. Next, configure one or more functions that transform the selected fields in sequence. Run Preview to generate a CSV with every Before and After value, then update only after the result is correct. Finally, save and automate the template so new records follow the same formatting process.

60+ functions · 12 families

A function for every fix.

Find the recognizable operation first, then combine it with filters and other functions for the exact transformation your field requires.

Format

Proper-case names and companies, six phone formats including E.164, dates, separators, and custom patterns.

john doe → John Doe

Map

Replace by exact value, term, word, substring, regex, or numeric range, with defaults for unmapped values.

NY | NYC | new york → NY

Standardize

Use maintained reference data for states, provinces, countries, ISO codes, ZIPs, cities, and area codes.

DE → Delaware

Split

Split by a space or any delimiter, keep the Nth term, or separate one field into two.

John Doe → John | Doe

Extract

Pull a domain, substring, initials, regex match, or moved regex value from existing text.

john@seas.upenn.edu → upenn.edu

Remove

Remove terms, titles, parentheses, subdomains, TLDs, whitespace variants, symbols, or non-digits.

Acme, Inc. → Acme

Copy

Copy, fill only when empty, move, or swap values between fields, the workhorses for retiring legacy fields.

Legacy Industry → Industry

Append

Concatenate fields with separators or add values to multi-select picklists without discarding what is already there.

Region + Segment → Sales Area

Find & Replace

Replace any occurrence using plain text or regular expressions.

North-East → Northeast

Prefix / Suffix

Add, remove, or replace values at either end of a field.

10492 → CUST-10492

Dates

Add fixed or field-driven intervals, calculate days between dates, or calculate days from now.

Renewal date − today → Days remaining

Math & Compare

Run arithmetic, rounding, and absolute values, or compare two fields and write the verdict to a third.

Actual ARR ≥ Target ARR → On target

Add a function

FormatMapStandardizeSplitExtractRemoveCopyAppendFind & ReplacePrefix / SuffixDatesMath & Compare

Compose

Chain functions when one is not enough.

Functions execute from top to bottom. Each one consumes the previous function's output, creating a point-and-click transformation pipeline.

Example 01

Populate country from an email address.

1
Split email by “.”
Keep the last term: de
2
Standardize Code2
de → Germany
3
Copy to Country
Email stays unchanged

Example 02

Create a standard deal name from four fields.

1
Concatenate Deal + Close Date
Join source fields
2
Split off timestamp
Keep the readable date
3
Append Amount + Next Step
Add separators and proper case

Each function is simple. Chained, they express transformations you would otherwise write code for.

Precision

Filter first. Change only what matched.

Build filters on any standard or custom field, including regex conditions, before configuring a single update. The Record Viewer shows exactly which records are in scope so you can confirm the audience before proceeding.

Country is empty
Email exists
Domain ends in a two-letter country TLD
Exclude .com, .net, and .io

Use the simplest module that fits. For a flat “if X, set Y” change, Bulk Operations is faster. To inventory the variants in a field before deciding how to fix them, start with Cleanse Data.

Step 1 · Filter

Countryis emptyAND

Emailmatches regex.[a-z]{2}$

Record Viewer1,946 matched

Email	Country
lena@firma.de	,
marc@atelier.fr	,
eva@empresa.es	,

Preview, verify, update

See the before and after, before.

Preview mode simulates the full run without touching the CRM. Develop the template against the CSV, inspect the Before and After values for every record, and run Update only when the result is exactly right. Start with a small chunk, verify it in the CRM, then process the rest.

Every preview and update appears in the Activity Tracker with a downloadable per-record report. Stamp the Insycle Run ID onto changed records so the CRM itself points back to the operation that changed them.

Before/after preview CSVChunked rolloutActivity Tracker audit trailRun ID on records

Transform-preview.csv

Records evaluated

1,946

Updated

1,902

Unmodified / failed

Before	After	Result
john doe	John Doe	Updated
+(44) 161-868.8000	+441618688000	Updated
,	,	Unmodified

Preview complete · no CRM values changed

Automate

Fix it once. Keep it fixed automatically.

Every form fill, import, and integration writes new variants. Save your configuration as a template, schedule it hourly to monthly, chain templates into Recipes, or trigger a Recipe from a HubSpot Workflow when a record is created or changed.

The manual cleanup becomes a standing process that nobody has to remember.

New or changed record

Form · import · integration

Saved filter + functions

Transform only what matched

Consistent CRM values

Scheduled · Recipe · Workflow triggered

When a mapping becomes a standard

A mapping you keep re-running is a standard you have not declared yet.

Export any Map function as a Blueprint CSV with one click. Pipe-delimited variants become separate rows automatically, ready for Data Logic to apply continuously, version, and measure for coverage and drift.

When a Transform template graduates from cleanup to policy, let Insycle govern the field from then on.

See how teams make the last cleanup the last one

TRANSFORM MAP

Industry normalization

BLUEPRINT CSV

Variants → Standard

SaaS | Software as a Service → Software

FinServ | Financial svcs → Financial Services

The spreadsheet and script alternative

Build the transformation once, and leave a process behind.

Capability	Spreadsheet + re-import	One-off script	Insycle Transform Data
Setup	Every time, from scratch	Engineering time for each change	Point-and-click, with reusable templates
Capability	Formula-level	Anything, for whoever wrote it	60+ chainable functions, including regex
Risk	Re-import overwrites and duplicates	Untested writes to live CRM	Preview CSV and chunked rollout
Traceability	None	Whatever was logged	Activity Tracker + Run ID on each record
Recurrence	Manual again next month	A scheduled job someone owns	Schedules, Recipes, and Workflow triggers
When logic changes	Redo the spreadsheet	Find the person who wrote it	Edit the template, or export it to a Blueprint

Common transformations

Start with the field that is blocking the work.

Format names, phones, and addresses

Normalize casing, punctuation, international prefixes, and address values at scale.

Explore formatting

Standardize job titles and industries

Map uncontrolled variants into consistent values used by segmentation and reporting.

Explore standardization

Run conditional bulk updates

Filter on one value, then update another field only for the records that qualify.

Explore conditional updates

Standardize deal names

Compose a naming convention from deal, date, amount, and next-step fields.

Explore deal naming

Populate country from email or phone

Extract a domain or country code, standardize it, and fill missing geographic fields.

Explore country enrichment

Map values field to field

Translate source values into the destination format your process expects.

Explore field mapping

Consolidate legacy fields

Copy, fill, move, and verify values before retiring fields left behind by migrations.

Explore field consolidation

Prepare data for BI

Standardize categories, dates, identifiers, and numeric fields before downstream analysis.

Explore BI preparation

Proven in live CRM operations

Replace recurring spreadsheet work with a transformation the whole team can inspect.

Use one approved customer quote about time saved and one about eliminating export-reimport or scaling field cleanup. Keep both in the task register.

Approved customer proof

Time saved transforming records at scale

Server-rendered G2 rating

Rating, review count, and source link

Frequently asked questions

Can Transform Data handle your specific transformation?

Can I format phone numbers in bulk in my CRM?

Yes. Transform Data includes multiple phone-formatting options, including E.164 and custom separators. Filter the target records, configure the format, and preview every result before updating your CRM.

Can I preview changes before updating CRM records?

Yes. Preview mode generates a CSV with the Before value, After value, and result for every record without changing the CRM.

Can I use regular expressions on CRM fields?

Yes. Regex can be used in filters and in functions including mapping, extracting, moving, and find-and-replace operations.

Can transformations run automatically?

Yes. Save the configuration as a template, schedule it, chain it into a Recipe, or trigger HubSpot Recipes from Workflows when records are created or changed.

Start with one field

Connect your CRM and run your first transformation, with a preview, in minutes.

See the exact before-and-after values before deciding what to update.

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Repeating the same cleanup every month?

Turn the Transform template into a standing standard.

Export the mapping as a Blueprint and move from recurring cleanup to continuous enforcement and measurement.

See how the upgrade path works

Platform Overview

HubSpot + Salesforce

CRM Data Management Glossary