CRM migration

Migrate from Mautic to Freshsales

Field-level mapping, validation, and rollback between Mautic and Freshsales. We move data and schema; workflows are rebuilt natively in Freshsales.

Mautic logo

Mautic

Source

Freshsales

Destination

Freshsales logo

Compatibility

70%

7 of 10

objects map 1:1 between Mautic and Freshsales.

Complexity

BStandard

Timeline

2-4 weeks

Rollback included Accuracy guarantee Field-level validation

Overview

What this migration involves

Moving from Mautic to Freshsales is a platform-type migration: Mautic is a marketing automation system where contacts are the primary record, while Freshsales is a sales CRM with separate Lead and Contact objects, Deal pipelines, and built-in Freddy AI for lead scoring. The structural gap that shapes every migration is the Mautic Lifecycle Stage (or points-based scoring) versus the Freshsales Lead-Contact-Account model. We resolve that split during scoping, preserve company-to-account relationships, and use direct database reads from Mautic's MySQL/MariaDB to bypass the broken v6 CSV export queue. Campaign definitions, segments, forms, and landing pages do not migrate as functional records; we deliver a written inventory of each campaign's trigger logic, filter conditions, and action sequence for your admin to rebuild using Freshsales Workflows and automation tools. Asset files and custom objects migrate as data, not as configured objects.

Field-level fidelity

Every standard and custom field arrives verified.

Schema-aware mapping

AI proposes the map; you confirm before any record moves.

Relationships preserved

Parent–child, lookups, and ownership stay linked.

Full activity history

Calls, emails, meetings — with original timestamps.

Attachments & notes

Documents, uploads, and inline notes move with the record.

Why teams make this switch

Two sides of the same decision

Leaving

Mautic logo

Mautic

What's pushing teams away

  • Upgrading between major versions (4→5→6) is not a simple in-place update; plugins and custom code frequently break silently with no errors logged.
  • MySQL/MariaDB column-size limits and index-per-table restrictions throttle performance at contact counts above 500K, forcing expensive database workarounds.
  • Self-hosting requires dedicated server management, security patching, and cron job maintenance that marketing teams are not equipped to handle.
  • The UI and documentation lag behind feature development, making routine tasks like bulk contact exports unreliable in newer versions (v6.0.0 CSV exports queue but do not reliably email).
  • Enterprise support tiers at $20K–$30K/year are priced similarly to SaaS alternatives, removing the cost advantage for organizations that need professional SLA coverage.

Choosing

Freshsales logo

Freshsales

What's pulling them in

  • Lowest barrier to entry among major CRMs — the free tier supports up to 3 users and includes core CRM functionality before committing to per-seat pricing.
  • Built-in chat, email, and phone reduce reliance on third-party integrations for basic sales communication and contact management.
  • Freddy AI contact scoring and deal insights are included on Pro plans at a lower price than comparable HubSpot tiers.
  • Kanban pipeline views across Contacts, Accounts, and Deals provide visual deal management without requiring custom configuration.
  • Integration with the broader Freshworks ecosystem (Freshdesk, Freshchat, Freshservice) reduces tool sprawl for teams already using Freshworks.

Object mapping

How Mautic objects map to Freshsales

Each row shows how a Mautic object lands in Freshsales, including any object-level transformations, lookup resolution, or schema-design dependencies.

Typical mapping — final map is confirmed during the sample migration step.

Mautic

Contact

maps to

Freshsales

Lead or Contact (lifecycle split required)

1:many
Fully supported

Mautic Contacts map to Freshsales Lead or Contact based on the contact's lifecycle stage and activity profile. Contacts with Mautic lifecycle stages below marketing qualified lead (subscriber, lead, marketing qualified) map to Freshsales Lead. Active, sales-qualified, customer, or evangelist stages map to Freshsales Contact attached to an Account. The original Mautic lifecycle stage and point score migrate as custom fields on both Lead and Contact for audit. This split is the single most important design decision in the migration and must be confirmed with the customer's RevOps lead before any records are written.

Mautic

Company

maps to

Freshsales

Account

1:1
Fully supported

Mautic Company records map directly to Freshsales Account. Address, industry, annual revenue, website, and phone fields map to their Freshsales equivalents. Mautic's many-to-one Contact-to-Company relationship (one contact can associate with multiple companies) requires resolution: the primary company becomes the AccountId on the Contact record, and secondary company associations are preserved in a custom text field listing additional account IDs or names for reference.

Mautic

Campaign

maps to

Freshsales

Campaign (reference data only)

1:1
Fully supported

Mautic Campaigns are automation workflows with trigger events, conditions, and action sequences. Freshsales Campaigns are grouping labels for marketing attribution with no automation logic. We export campaign names, statuses, dates, and contact membership counts as a Campaign reference record in Freshsales. The automation logic is captured in a written campaign inventory document describing each campaign's trigger (contact action or time interval), filter conditions, and action sequence for the customer's admin to rebuild in Freshsales Workflows. This document is the primary handoff artifact for the automation rebuild scope.

Mautic

Segment

maps to

Freshsales

Static List or Dynamic View (lookup)

1:1
Fully supported

Mautic Segments are dynamic contact lists filtered by field values, tags, and behavioral conditions. Freshsales does not replicate dynamic segment logic natively. We export segment definitions (filter criteria, tag membership, date ranges) as a written segment inventory. Where segments are small and stable, we can pre-filter and migrate the resulting contact list as a Freshsales Static List. Complex or dynamic segments require rebuilding as Freshsales Filters or Automation Rules post-migration.

Mautic

Custom Object

maps to

Freshsales

Custom Object

1:1
Fully supported

Mautic Custom Objects extend the data model with user-defined schemas and relationship records. Freshsales supports custom fields on standard objects but Custom Object API coverage and bulk import support varies by Freshsales plan tier. We profile the source Custom Object schemas and their relationship junction tables (accessed directly from MySQL/MariaDB because the Mautic Relationships API is documented as non-functional) and map them to Freshsales custom field structures. If the destination Freshsales plan limits custom object support, we restructure the data as custom fields on the Contact or Account object and flag the constraint during scoping.

Mautic

Stage

maps to

Freshsales

Lifecycle Stage (custom field)

lossy
Fully supported

Mautic Stages define contact lifecycle positions (e.g., Lead, MQL, Customer, Evangelist). Freshsales does not have a native equivalent across all tiers. We create a custom Lifecycle_Stage__c picklist field on Contact and Lead with the same stage values, and migrate the original Mautic stage name for each record. If the customer uses Freddy AI, the custom stage field supplements the built-in predictive scoring model.

Mautic

Points

maps to

Freshsales

Custom field (numeric)

lossy
Fully supported

Mautic's points system assigns numeric scores to contacts based on actions (page visits, form submissions, email opens). Freshsales does not replicate Mautic's points engine natively. We migrate the point value as a custom numeric field lead_score__c on the Contact and Lead records. The customer can use this field in Freshsales Filters to replicate segment logic, or map it to Freddy AI scoring inputs if they configure a custom scoring model.

Mautic

Tag

maps to

Freshsales

Tag (on Contact or Lead)

1:1
Fully supported

Mautic tags are flat string labels applied to contacts and other objects. We export all tags and reapply them during migration as tags on the Freshsales Contact or Lead record. Tag-based segmentation logic is preserved as a tag reference, with the segment inventory document capturing which tags were used in segment filter rules for rebuild in Freshsales Filters.

Mautic

Asset

maps to

Freshsales

Document (as blob reference)

1:1
Fully supported

Mautic Assets are downloadable files (PDFs, guides, media) managed within the platform. We export asset metadata (filename, description, upload date, download count) as a reference record in Freshsales. The actual file binary is exported as a ZIP archive and delivered alongside the migration for manual re-upload to Freshsales Documents, Dropbox, or a CDN-based storage solution of the customer's choice.

Mautic

Engagement (calls, emails, meetings, tasks, notes)

maps to

Freshsales

Activity history on Contact or Lead

1:1
Fully supported

Mautic engagement records (calls, emails, meetings, tasks, notes) are stored in activity tables linked to contact IDs. Freshsales surfaces these as activity timeline entries on the Contact or Lead record. We export engagement type, subject, body, timestamp, and disposition (for calls) and write them as Freshsales Activity records. The engagement history timeline is preserved by setting the activity date to the original Mautic timestamp. Note that Freshsales has a limit on how far back activity history is displayed in the UI timeline; very large activity histories are written as records and are queryable via reports even if UI display is paginated.

Gotchas + challenges

What specifically takes care here

Platform-specific issues from each side, plus the pair-specific challenges that don't show up on either platform's page on its own.

Mautic logo

Mautic gotchas

High

Mautic v6 CSV export silently fails to deliver files

High

Mautic 4 to 5 upgrade breaks plugins without warning

Medium

MySQL/MariaDB index limits throttle large contact databases

Medium

Custom Object Relationships API is non-functional

Medium

Mautic 5 to 6 migration logs no errors on failure

Freshsales logo

Freshsales gotchas

Medium

Freddy AI is Pro-tier only despite heavy marketing

High

Post-migration emails and sequences are disabled

Medium

Bot session credits are a one-time 500-session allocation

Medium

Phone credits charged per minute with no cap

Low

File storage limits scale with plan tier

Pair-specific challenges

  • Mautic v6 CSV export queues but never delivers the file

    Mautic 6.0.0's CSV export function queues the file for background processing but never sends it via email and writes no errors to Apache logs or Mautic's internal log. We handle this by bypassing the built-in export entirely: we connect directly to the Mautic MySQL/MariaDB database with authenticated read access and extract contact, company, custom field, and engagement data using batched SQL queries. This is the only reliable export path for Mautic v6 instances. For Mautic v4 and v5 instances, the REST API is available but batch limits require chunked requests; we use the database-read approach consistently for reliability across versions.

  • Mautic Company-to-Contact is many-to-one; Freshsales Contact-to-Account is many-to-one

    In Mautic, a single contact can be associated with multiple companies, and vice versa. In Freshsales, a Contact is always attached to one primary Account; additional account associations must be stored as custom fields or text references. During scoping, we identify contacts with multiple Mautic company associations, designate the primary company as the AccountId on the Freshsales Contact record, and write secondary company names or IDs into a custom field company_associations__c. This prevents data loss but requires the customer to decide which company is primary for each contact.

  • Mautic Custom Object Relationships API is non-functional

    Community reports from 2022 document that Mautic's REST API endpoint for creating custom object relationship records returns errors or does not persist data. The relationship data exists in Mautic's junction tables in MySQL/MariaDB. We handle this by reading relationship records directly from the database schema and reconstructing them in Freshsales as custom lookup fields or multi-select text fields depending on the destination field type support. Any Mautic plugin that extended the custom object relationship model requires manual documentation from the customer to map to Freshsales equivalents.

  • Freshsales CSV import silently rejects records with unmapped required fields

    Freshsales CSV import documentation explicitly notes that records fail when required fields (e.g., Deal value, email address) are missing or mapped with incorrect column headers. Unlike the Freshsales Salesforce migration tool (which reports errors per record), a manual CSV import requires pre-creating custom fields in Freshsales Admin Settings with exact column header names matching the CSV. We handle this by pre-creating the destination schema in Freshsales before any import, validating column header names against the target field API names, and running a test import of 100 records before the full migration. Any field type mismatches (e.g., date format differences between Mautic MySQL and Freshsales expected formats) are corrected in the transform layer before import.

  • Campaign automation logic has no functional equivalent in Freshsales without rebuild

    Mautic Campaign definitions (triggers, conditions, delays, actions) are stored as PHP-serialized objects and database records in Mautic's campaign table. Freshsales does not import campaign automation logic. We export campaign definitions as a written inventory document that describes each campaign's name, status, trigger type (contact action or time-based), filter conditions, step sequence, and action outputs. The customer's admin uses this document to rebuild campaigns in Freshsales Workflows. Workflows, bots, and automation rules are outside migration scope per FlitStack AI standard terms; they are a separate rebuild engagement or internal admin task.

Migration approach

Six steps for a successful Mautic to Freshsales data migration

  1. Discovery and source audit

    We audit the Mautic instance across version (v4, v5, or v6), custom field count, company-contact relationship cardinality, custom object schemas, campaign definitions, segment logic, and engagement volume. We connect to the Mautic MySQL/MariaDB database read-only and profile the schema: table sizes, index usage, custom field tables, and campaign junction tables. This database profiling step is required for v6 instances where the export API is broken, and it surfaces any tables approaching MySQL index limits before migration begins. The discovery output is a written migration scope covering record counts, schema map, and the Lead-Contact split rule for the customer's RevOps lead to confirm.

  2. Schema design and Freshsales field provisioning

    We pre-create the destination schema in Freshsales: custom fields on Contact, Lead, and Account matching the Mautic field names and types; lifecycle stage picklist values; point score numeric fields; tag fields; and any lookup structures for custom objects. We configure the Lead-Contact split rule based on the customer's confirmed lifecycle stage matrix. We create Freshsales users matching the Mautic owner records (matched by email) so that OwnerId references are satisfied during import. Schema is built in the customer's Freshsales production environment during a pre-agreed configuration window.

  3. Data extraction from Mautic database

    We extract data directly from the Mautic MySQL/MariaDB database in batched queries rather than relying on the Mautic REST API or CSV export. This bypasses the broken v6 export queue entirely. We extract contacts with all custom field values, companies, campaign membership records, segment filter definitions, custom object records, and engagement history (calls, emails, meetings, tasks, notes). Each extraction query chunks by contact ID ranges to avoid locking the production database during read. Extracted data is staged in a migration workspace, validated for completeness, and deduplicated against any records already migrated in a prior test run.

  4. Transform and Lead-Contact split

    We apply the customer-confirmed Lead-Contact split rule to every Mautic contact record before writing to Freshsales. Contacts above the qualification threshold (lifecycle stage, point score, or behavioral criteria set during scoping) become Freshsales Contacts attached to Accounts; contacts below the threshold become Freshsales Leads. We write the original Mautic lifecycle stage and point score into custom fields on both Lead and Contact. We resolve the Mautic many-to-one company-contact relationship by selecting the primary company per contact (based on most recent association timestamp) and writing it as the AccountId on the Contact. Secondary associations are preserved in a custom text field.

  5. Import and bulk API write

    We write data to Freshsales in dependency order: Accounts (from Companies), Contacts (with AccountId resolved), Leads (with the lifecycle split applied), then engagement history. For large engagement migrations, we use Freshsales' bulk import endpoint with chunking and batch-size tuning. Each import phase produces a row-count reconciliation report comparing source record counts to destination inserted counts, with error logs for any rejected records. We correct rejected records (typically missing required fields or format mismatches) in the transform layer and rerun the affected batch before proceeding.

  6. Campaign inventory delivery and handoff

    We deliver a written campaign automation inventory that documents every Mautic campaign's trigger, conditions, step sequence, and action outputs based on the campaign table extraction from the Mautic database. This document is the primary reference for the customer's admin to rebuild campaigns in Freshsales Workflows and automation rules. We do not rebuild Mautic campaigns as Freshsales Workflows inside the migration scope. We also deliver a segment inventory mapping each Mautic segment's filter logic to Freshsales Filter equivalents. We run a final validation comparing record counts and spot-checking 25-50 records against the source, then hand off with a one-week reconciliation window for the customer's team to verify data quality.

Platform deep dives

Context on both ends of the pair

Mautic logo

Mautic

Source

Strengths

  • Zero per-contact licensing cost with the self-hosted open-source core product.
  • Full source code access enables unlimited customization without vendor dependency.
  • Complete data ownership and infrastructure control for compliance-sensitive organizations.
  • Multi-channel support including email, SMS, mobile push, and web notifications in one platform.
  • Active community and partner ecosystem with implementation support available.

Weaknesses

  • Self-hosted deployment requires ongoing server administration, security updates, and infrastructure costs.
  • MySQL/MariaDB column-size and index-per-table limits constrain large-scale contact databases.
  • Documentation and feature parity across versions is inconsistent, especially for new major releases.
  • CSV export reliability degrades in Mautic v6, and queued export jobs can fail silently without logging errors.
  • Upgrade paths between major versions frequently break third-party plugins and custom code.
Freshsales logo

Freshsales

Destination

Strengths

  • Generous free tier for small teams with core CRM functionality without per-seat costs.
  • All-in-one sales CRM with built-in telephony, chat, and email reducing third-party tool dependency.
  • Freddy AI contact scoring and deal predictions available on Pro tier.
  • Multiple pipeline views with Kanban and list options across all plans.

Weaknesses

  • Reports lack depth compared to competitors like HubSpot, with limited customization options.
  • Integration setup is poorly documented with no clear guides for connecting third-party tools.
  • AI features gated behind $39/user/month Pro tier despite marketing emphasis on Freddy AI.
  • Bot sessions limited to 500 one-time allocation with no monthly refresh.

Complexity grading

How hard is this migration?

Standard CRM migration. 2 of 8 objects need a mapping; the rest are 1:1.

B

Overall complexity

Standard migration

Derived from compatibility, mapping clarity, API constraints, and data volume across Mautic and Freshsales.

  • Object compatibility

    B

    2 of 8 objects need a mapping; the rest are 1:1.

  • Field mapping clarity

    C

    Field mapping is derived from defaults — final spec confirmed during the sample migration.

  • Timeline complexity

    B

    8-object category — typical timelines run 2–7 days end-to-end.

  • API constraints

    B

    Mautic: Not publicly documented — enforced at the server level, not within Mautic software.

  • Data volume sensitivity

    B

    Mautic doesn't expose a bulk API — REST + parallelization used for high-volume runs.

Estimator

Estimate your Mautic to Freshsales migration cost

Rule-based pricing — no per-record fees, no manual quotes. Migrations over 2M records are scoped individually.

Step 1

What are you migrating?

Pick a category, then your source and destination platforms.

Category

FAQ

Frequently asked questions about Mautic to Freshsales data migrations

Answers to the questions buyers ask most during Mautic to Freshsales migration scoping. Not seeing yours? Book a call.

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Most migrations land between two and four weeks for accounts under 15,000 contacts, 2,000 companies, and no custom objects. Migrations with active custom objects, large engagement histories (over 200,000 activity records), or Mautic instances on v6 with database access constraints move to five to nine weeks because of database profiling, custom field restructuring, and bulk import testing. The Mautic v6 export workaround (direct database read) adds one to two days to discovery but is faster and more reliable than debugging the broken export queue.

Adjacent paths

Related migrations to explore

Ready when you are

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