CRM migration
Field-level mapping, validation, and rollback between Spark CRM and Salesforce Sales Cloud. We move data and schema; workflows are rebuilt natively in Salesforce Sales Cloud.
Spark CRM
Source
Salesforce Sales Cloud
Destination
Compatibility
14 of 14
objects map 1:1 between Spark CRM and Salesforce Sales Cloud.
Complexity
BStandard
Timeline
48–72 hours
Overview
Spark CRM positions itself as an email-first CRM with lightweight contact and pipeline management, storing deals in simple pipeline stages without the complexity of record-type scoping or business-unit-specific page layouts. Salesforce Sales Cloud uses a rigid object model where Accounts must exist before Contacts can link via AccountId, Opportunities require Contact Roles for many-to-many contact-deal relationships, and pipeline stages are scoped to Sales Processes tied to RecordTypeId. The migration carries Spark's contacts, companies, deals, and custom fields into Salesforce's hierarchy while resolving foreign-key dependencies: Accounts first, then Contacts with AccountId lookups, then Opportunities with stage-to-record-type mapping. Activity history—calls, emails, meetings, and notes—migrates as Salesforce Tasks and Events with original timestamps and owner assignments preserved. Workflows, sequences, and automation logic do not migrate and must be rebuilt in Salesforce Flow or Apex. We export workflow definitions as a reference document for your Salesforce admin. Prior to migration, we perform a data quality assessment to identify duplicate contacts, missing company associations, and stage name inconsistencies. We generate a field-level mapping document that your Salesforce admin reviews and approves. During migration, we use Bulk API for high-volume objects and REST API for activity records, maintaining an audit log of every inserted record. After migration, a reconciliation report compares record counts and field values between Spark and Salesforce to confirm completeness.
Every standard and custom field arrives verified.
AI proposes the map; you confirm before any record moves.
Parent–child, lookups, and ownership stay linked.
Calls, emails, meetings — with original timestamps.
Documents, uploads, and inline notes move with the record.
Why teams make this switch
Leaving
What's pushing teams away
Choosing
What's pulling them in
Object mapping
Each row shows how a Spark CRM object lands in Salesforce Sales Cloud, including any object-level transformations, lookup resolution, or schema-design dependencies.
Typical mapping — final map is confirmed during the sample migration step.
Spark CRM
Contact
Salesforce Sales Cloud
Contact
1:1Spark contacts map 1:1 to Salesforce Contacts. The AccountId field is required on Salesforce Contacts, so Spark contacts without a primary company link to a designated 'Unassigned' Account record. Email, phone, title, and address fields migrate directly with field names adapted to Salesforce PascalCase conventions.
Spark CRM
Company
Salesforce Sales Cloud
Account
1:1Spark companies map to Salesforce Accounts. Company name becomes Account.Name, domain becomes Website, industry values are mapped via value-mapping against Salesforce's standard industry pick-list. Parent-company hierarchies in Spark map to the Account.ParentId field; circular references are flagged before migration commits.
Spark CRM
Deal
Salesforce Sales Cloud
Opportunity
1:1Spark deals map to Salesforce Opportunities. The deal name becomes Opportunity.Name, amount maps directly, close date maps to CloseDate, and owner resolves via email match to Salesforce User records. Pipeline assignment requires mapping to a Salesforce Sales Process tied to a RecordTypeId.
Spark CRM
Pipeline
Salesforce Sales Cloud
Sales Process + Record Type
1:1Each Spark pipeline becomes a Salesforce Record Type with an associated Sales Process. The record type defines which stage pick-list values are available per deal category. We deliver a record-type setup plan specifying the page layout assignments, profile access, and stage pick-list values for each pipeline before data migration runs.
Spark CRM
Pipeline Stage
Salesforce Sales Cloud
Opportunity StageName
1:1Stage names are mapped value-by-value from Spark to Salesforce. Each stage gets a probability value and forecast category assigned based on Salesforce's stage-history model. Stage-entered timestamps are preserved as custom datetime fields (Stage_Entered_Date__c) for reporting continuity on the Opportunity record.
Spark CRM
Activity (Call)
Salesforce Sales Cloud
Task
1:1Spark call logs migrate as Salesforce Tasks with Task.Type = 'Call'. Original timestamps (CreatedDate equivalent), subject, and owner links are preserved. The Task.WhatId links to the parent Account or Opportunity record from the Spark activity association. Call duration and outcome fields map to CallDurationInSeconds and CallType respectively. If Spark recorded a disposition code, it is stored in a custom field on the Task.
Spark CRM
Activity (Email)
Salesforce Sales Cloud
Task
1:1Spark email activities migrate as Salesforce Tasks with Task.Type = 'Email'. Subject line and body content transfer as Task.Subject and Task.Description. Owner resolution follows the same email-match logic used for all owner fields. Inline attachments download and re-upload as Salesforce Files linked to the Task.
Spark CRM
Activity (Meeting)
Salesforce Sales Cloud
Event
1:1Spark meetings migrate as Salesforce Events. Start time, end time, subject, and location map to Event.StartDateTime, EndDateTime, Subject, and Location respectively. The Event.WhatId links to the parent Account or Opportunity record. Owner assignment follows email-based user resolution. Meeting agenda and attendee information are stored in custom fields if present in Spark. Recurring meetings are expanded into individual Event records.
Spark CRM
Note
Salesforce Sales Cloud
Note
1:1Spark notes migrate to Salesforce Notes (not the legacy Note object). Title, body, and created date transfer. Rich-text formatting is preserved where the source format is compatible. Notes are linked to their parent Contact, Account, or Opportunity record via the ParentId field.
Spark CRM
Attachment / File
Salesforce Sales Cloud
ContentDocument / Salesforce Files
1:1Spark file attachments re-upload to Salesforce Files. Each file gets a ContentDocument record with a ContentVersion. Files are linked to their parent record via ContentDocumentLink. Salesforce's 25MB per-file limit applies; files exceeding this are flagged for manual handling before the migration runs.
Spark CRM
Custom Field (Contact)
Salesforce Sales Cloud
Custom Field on Contact (__c)
1:1Spark contact custom fields become Salesforce custom fields on the Contact object with the __c suffix. Field type mapping follows type-aware conversion: text fields become Text(255), number fields become Number, pick-list fields become Picklist with value-mapping. All custom fields are created in the target org before the migration phase begins.
Spark CRM
Custom Field (Deal)
Salesforce Sales Cloud
Custom Field on Opportunity (__c)
1:1Spark deal custom fields map to custom fields on the Opportunity object. Fields that hold monetary values use Currency data type with appropriate decimal precision. Priority or status fields become custom Picklist fields with value-mapping against the destination pick-list values defined in the Salesforce org.
Spark CRM
Contact Role (on Deal)
Salesforce Sales Cloud
OpportunityContactRole
1:1Spark contact-deal associations map to Salesforce OpportunityContactRole junction records. Each association creates an OpportunityContactRole entry with the ContactId, OpportunityId, and Role fields. If Spark stored a role label that has no Salesforce standard Role value, we map it to 'Other' and preserve the original label in a custom field on the junction record.
Spark CRM
Owner / User
Salesforce Sales Cloud
User
1:1Spark owner records are resolved against Salesforce Users by email address. Records where no matching Salesforce User exists are flagged before migration; the team assigns them to a fallback owner or invites the user to Salesforce first. No record lands without a resolved OwnerId.
| Spark CRM | Salesforce Sales Cloud | Compatibility | |
|---|---|---|---|
| Contact | Contact1:1 | Fully supported | |
| Company | Account1:1 | Fully supported | |
| Deal | Opportunity1:1 | Fully supported | |
| Pipeline | Sales Process + Record Type1:1 | Fully supported | |
| Pipeline Stage | Opportunity StageName1:1 | Fully supported | |
| Activity (Call) | Task1:1 | Fully supported | |
| Activity (Email) | Task1:1 | Fully supported | |
| Activity (Meeting) | Event1:1 | Fully supported | |
| Note | Note1:1 | Fully supported | |
| Attachment / File | ContentDocument / Salesforce Files1:1 | Fully supported | |
| Custom Field (Contact) | Custom Field on Contact (__c)1:1 | Fully supported | |
| Custom Field (Deal) | Custom Field on Opportunity (__c)1:1 | Fully supported | |
| Contact Role (on Deal) | OpportunityContactRole1:1 | Fully supported | |
| Owner / User | User1:1 | Fully supported |
Gotchas + challenges
Platform-specific issues from each side, plus the pair-specific challenges that don't show up on either platform's page on its own.
Spark CRM gotchas
Multiple unrelated 'Spark CRM' products exist
Platform fee on top of monthly subscription affects long-term TCO
Payment-orchestration data is tightly coupled to Spark's runtime
Limited public review footprint for due diligence
Salesforce Sales Cloud gotchas
Workflow Rules and Process Builder are retired
Bulk API batch quota exhaustion during large imports
Storage overage billing is non-obvious
Account-Contact many-to-many relationship mapping
Territory and team member import ordering dependencies
Pair-specific challenges
Migration approach
Inventory Spark data and design Salesforce schema
FlitStack AI inventories all Spark objects, custom fields, and relationship types. We identify every pipeline, stage name, custom field, and activity type. We then design the Salesforce schema plan: Record Type names and assignments per pipeline, custom field creation checklist with exact API names (__c suffix), owner resolution rules, and the load-order sequence required to satisfy Salesforce foreign-key constraints. This plan is delivered for your Salesforce admin to implement before the migration phase begins.
Resolve owners by email match against Salesforce users
Spark owner records are matched to Salesforce User records by email address. We generate an owner resolution report listing all Spark owners, their email addresses, and the matched Salesforce UserId. Any Spark owner with no matching Salesforce user is flagged as an exception. Your team either invites the user to Salesforce first or assigns their records to a designated fallback owner. No record is loaded into Salesforce without a resolved OwnerId.
Migrate accounts, then contacts, then opportunities with correct sequencing
We load data in strict hierarchical order: Accounts first (since Contact.AccountId is required), then Contacts with AccountId lookups resolved, then Opportunities with stage-to-record-type mapping and OpportunityContactRole junction records. This ordering satisfies Salesforce's foreign-key constraints and prevents the 'INVALID_FIELD_FOR_INSERT_UPDATE' error that occurs when a contact references an AccountId that hasn't been created yet. We use Bulk API for Accounts and Contacts, and REST API for Opportunities. Each batch is validated for required fields and foreign-key existence before commit.
Run a sample migration with field-level diff before full commit
A representative slice of records—typically 100–500 spanning contacts, companies, deals, and activities—migrates first. We generate a field-level diff between the Spark source and the Salesforce destination for each record, showing the exact value in Spark and the mapped value in Salesforce. You verify pipeline-to-record-type mapping, stage value mapping, owner resolution, and custom field population before the full run commits. This step ensures that mapping logic is correct and data appears as expected in Salesforce before committing to the full volume.
Execute full migration with delta-pickup window and audit log
The full migration runs against Salesforce using Bulk API for high-volume objects (Contacts, Accounts) and REST API for activity records. A delta-pickup window—typically 24–48 hours—captures any records created or modified in Spark during the cutover period. Every operation is logged in an audit trail. One-click rollback reverts the target org to its pre-migration state if reconciliation fails. After rollback verification, the migration is re-run with corrected mapping logic.
Platform deep dives
Spark CRM
Source
Strengths
Weaknesses
Salesforce Sales Cloud
Destination
Strengths
Weaknesses
Complexity grading
Standard CRM migration. 2 of 8 objects need a mapping; the rest are 1:1.
Overall complexity
Standard migration
Derived from compatibility, mapping clarity, API constraints, and data volume across Spark CRM and Salesforce Sales Cloud.
Object compatibility
2 of 8 objects need a mapping; the rest are 1:1.
Field mapping clarity
Field mapping is derived from defaults — final spec confirmed during the sample migration.
Timeline complexity
8-object category — typical timelines run 2–7 days end-to-end.
API constraints
Spark CRM: Not publicly documented on sparkcrm.io.
Data volume sensitivity
Spark CRM doesn't expose a bulk API — REST + parallelization used for high-volume runs.
Estimator
Rule-based pricing — no per-record fees, no manual quotes. Migrations over 2M records are scoped individually.
Step 1
Pick a category, then your source and destination platforms.
Category
FAQ
Answers to the questions buyers ask most during Spark CRM to Salesforce Sales Cloud migration scoping. Not seeing yours? Book a call.
Walk through your Spark CRM to Salesforce Sales Cloud migration with a real engineer — 30 minutes, free, written quote within 24 hours.
Book a free 30 minute consultationAdjacent paths
Other ways to leave Spark CRM
Other ways to arrive at Salesforce Sales Cloud
Ready when you are
Tell us record counts and timeline. We'll come back with a written quote inside 1 business day — no commitment, no sales pitch.