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Overview
CRM data synchronization remains one of the most critical yet challenging aspects of modern go-to-market operations. When your sales team closes a deal in Salesforce but marketing automation still treats that contact as a prospect, you have a sync problem. When lead scores update in HubSpot but your outreach sequences run on stale data, you have a sync problem. These disconnects cost revenue and erode trust in your systems.
Make (formerly Integromat) has emerged as a powerful solution for building CRM sync workflows that keep data flowing between systems in real time. Unlike rigid native integrations, Make gives GTM Engineers the flexibility to define exactly what syncs, when it syncs, and how conflicts get resolved. This guide covers the practical implementation of bi-directional CRM sync using Make, from basic setup to advanced error handling patterns that prevent data loss.
Whether you're syncing HubSpot with Salesforce, pushing enriched data from Clay to your CRM, or maintaining consistency across a dozen tools, the patterns here will help you build sync workflows that actually work in production. For teams looking to automate their entire GTM stack, a context engine like Octave can provide the intelligence layer that determines what data should sync and why.
Why CRM Sync Matters for GTM Teams
The average B2B company uses 12-15 different tools in their GTM stack. Each tool maintains its own version of the truth about leads, contacts, and accounts. Without proper synchronization, teams operate on conflicting data, leading to embarrassing outreach mistakes, missed opportunities, and unreliable reporting.
Consider the typical data flow: A lead comes in through your website, gets enriched in Clay, scored by your qualification engine, and routed to the appropriate rep. That lead's data now exists in your form tool, Clay, your scoring system, and your CRM. Update the lead's company size in one system and the others remain stale. Worse, if the rep manually corrects data in the CRM, that correction never flows back to upstream systems.
This is where coordinating Clay, CRM, and sequencer in one flow becomes essential. Make enables you to build these multi-system workflows with visual logic that non-engineers can understand and maintain.
Research shows that sales reps spend an average of 5.5 hours per week on manual data entry due to sync failures. That's nearly 300 hours per year per rep that could be spent selling. Poor data quality from sync issues also leads to a 12% average decrease in revenue according to Gartner.
Common Sync Challenges
Before diving into Make implementation, understand the core challenges any CRM sync solution must address:
- Bi-directional conflicts: When the same field gets updated in multiple systems, which value wins?
- Rate limiting: CRM APIs throttle requests, causing backlogs during high-volume operations
- Field mapping complexity: Different systems use different data structures for the same information
- Deduplication: Creating duplicate records when matching fails
- Error recovery: Handling partial failures without losing data
Make provides tools to address each of these challenges, but success depends on architecting your scenarios correctly from the start. For a deeper dive into field mapping specifically, see our guide on field mapping 101 for CRM, sequencer, and analytics.
Make Fundamentals for CRM Integration
Make operates on a scenario-based model where triggers initiate workflows that transform and route data through a series of modules. For CRM sync, you'll primarily work with three types of modules: watchers (triggers), actions, and routers.
Understanding Scenarios
A Make scenario is a visual workflow that connects your applications. For CRM sync, a typical scenario might watch for new HubSpot contacts, enrich them with company data, and create corresponding records in Salesforce. Each step in the scenario is a module that performs a specific action.
| Module Type | Purpose | CRM Sync Example |
|---|---|---|
| Instant Trigger | Responds to webhooks immediately | HubSpot contact created webhook |
| Polling Trigger | Checks for changes on schedule | Watch Salesforce for updated leads |
| Action | Creates, updates, or deletes records | Create or update HubSpot contact |
| Search | Finds existing records | Search Salesforce for matching lead |
| Router | Branches logic based on conditions | Route new vs. existing contacts differently |
Webhook vs. Polling Triggers
The choice between webhook (instant) and polling triggers significantly impacts your sync architecture. Webhooks provide near-real-time synchronization but require the source system to support them. Polling triggers work with any system but introduce latency and can miss rapid changes.
For most CRM sync scenarios, use webhooks when available and fall back to polling with appropriate intervals. HubSpot, Salesforce, and most modern CRMs support webhooks for record changes. When designing hands-off outbound workflows, webhook-based sync ensures your sequences always have current data.
When using polling triggers, set your interval based on data velocity. High-volume lead systems might need 5-minute polling, while slower-changing account data can use 30-minute intervals. Longer intervals reduce API usage and costs.
Building Bi-Directional CRM Sync
Bi-directional sync means changes flow both ways between systems. Update a contact in HubSpot and it reflects in Salesforce. Update it in Salesforce and it flows back to HubSpot. This requires careful handling to prevent infinite loops and data conflicts.
Step-by-Step Implementation
Define Your Sync Fields
Start by mapping which fields sync between systems. Not every field should sync bi-directionally. Some fields have a clear system of record (like lead source from your marketing platform), while others might need bi-directional sync (like contact email).
Create a field mapping document that specifies: field name in each system, sync direction, conflict resolution rule, and data transformation requirements.
Implement Loop Prevention
The biggest risk in bi-directional sync is creating infinite loops. System A updates, triggering sync to System B, which triggers sync back to System A, and so on. Make provides several methods to prevent this.
The most reliable method is timestamp comparison. Track the last sync time for each record and only sync if the record was modified after the last sync. Store sync metadata in a Make data store or custom CRM field.
Set Up Conflict Resolution
When the same field changes in both systems between syncs, you need a conflict resolution strategy. Common approaches include: last-write-wins (newest timestamp wins), master-system-wins (designate one system as authoritative), or field-level priority (different rules per field).
For most GTM teams, a hybrid approach works best: CRM is master for sales-entered data (like deal stage), while enrichment tools are master for firmographic data.
Build the Sync Scenarios
Create separate scenarios for each sync direction. This provides clearer debugging and allows independent scheduling. Your HubSpot-to-Salesforce scenario watches HubSpot, transforms data, and upserts to Salesforce. Your Salesforce-to-HubSpot scenario does the reverse.
Use Make's built-in upsert modules where available. They handle create-or-update logic automatically, reducing scenario complexity.
Test with Production Data Patterns
Testing sync scenarios requires realistic data volumes and patterns. Create test records that match your actual data complexity: records with special characters, long field values, and edge cases like empty required fields.
Run scenarios manually first, then enable scheduling. Monitor the first few days closely for unexpected failures.
For teams already using Clay data to CRM sync patterns, Make can extend these workflows with additional transformations and routing logic that Clay's native integrations don't support.
Error Handling and Recovery
Production sync scenarios will fail. APIs go down, rate limits get hit, data validation fails. The difference between a reliable sync system and a constant source of problems is how you handle these failures.
Built-in Error Handling
Make provides several error handling directives that you can attach to any module:
| Directive | Behavior | Best For |
|---|---|---|
| Ignore | Skips the failed bundle, continues scenario | Non-critical updates that can be skipped |
| Retry | Waits and retries the failed module | Transient failures like rate limits |
| Break | Stores failed bundle for manual resolution | Critical data that cannot be lost |
| Rollback | Reverts all modules in current run | Transaction-like operations |
| Commit | Saves progress and stops scenario | Partial success scenarios |
For CRM sync, the Break directive is particularly valuable. When a sync fails, the record goes into Make's incomplete executions queue where you can inspect the error, fix the underlying issue, and retry. This prevents data loss from temporary failures.
Implementing Retry Logic
Rate limiting is the most common cause of CRM sync failures. Both HubSpot and Salesforce enforce strict API limits. Make's retry directive handles this automatically, but you should configure it appropriately:
- Initial interval: Start with 60 seconds for rate limit errors
- Maximum retries: Set to 3-5 attempts before breaking
- Exponential backoff: Double the wait time on each retry
For persistent failures, the Break directive captures the failed record so you can investigate. Common issues include: missing required fields, invalid field values, and permission errors. Understanding the role of CRM hygiene in achieving GTM alignment helps prevent many of these errors at the source.
Monitoring and Alerting
Make provides execution history for every scenario, but proactive monitoring requires additional setup. Connect Make to your alerting system (Slack, email, PagerDuty) to notify when:
- Scenario execution fails completely
- Error rate exceeds threshold (e.g., more than 5% of executions fail)
- Incomplete executions queue grows beyond limit
- Execution time exceeds expected duration (indicating bottlenecks)
Make stores incomplete executions for 30 days by default. Set a weekly reminder to review and resolve these. Unresolved incomplete executions represent data that never synced, accumulating discrepancies between systems.
Advanced Sync Patterns
Basic record-level sync covers most needs, but sophisticated GTM operations require more advanced patterns. These patterns address complex real-world scenarios that simple sync cannot handle.
Hierarchical Data Sync
Syncing accounts and their associated contacts requires maintaining relationships across systems. The challenge: Salesforce uses Account IDs while HubSpot uses Company IDs, and creating contacts requires the parent company to exist first.
The solution is a two-pass sync:
- First pass syncs companies/accounts and stores the ID mapping in a Make data store
- Second pass syncs contacts, looking up parent IDs from the data store
This pattern also applies to syncing deals with their associated contacts, or any parent-child relationship. For teams managing complex data relationships, the RevOps guide to automated CRM enrichment and deduplication provides additional context on maintaining data integrity.
Conditional Sync Based on Context
Not every record should sync the same way. A high-value enterprise lead might need immediate sync with full enrichment, while a small business lead might batch sync daily. Make's routers enable conditional logic based on record attributes.
This is where a context engine like Octave becomes valuable. Instead of hardcoding sync rules in Make, you can call Octave's API to determine sync priority and routing based on your ICP definitions and lead scoring. The sync scenario becomes simpler while the intelligence lives in a maintainable, versioned system.
Delta Sync for High-Volume Systems
When syncing thousands of records, full syncs become impractical. Delta sync only processes records that changed since the last run. Make supports this through:
- Cursor-based pagination: Track the last processed record ID
- Timestamp filtering: Only fetch records modified after last sync
- Change data capture: Use CRM change streams where available
Salesforce's Streaming API and HubSpot's changelog API enable efficient delta sync. Make can subscribe to these streams for near-real-time updates without polling overhead.
Best Practices for Production Sync
After implementing sync scenarios, these practices ensure long-term reliability and maintainability.
Documentation and Naming
Future you (or your replacement) will thank you for clear documentation. In Make:
- Name scenarios descriptively: "HubSpot Contact to SFDC Lead - Bi-directional"
- Add notes to complex modules explaining their purpose
- Document field mappings in a shared spreadsheet
- Maintain a changelog of scenario modifications
Testing and Staging
Never test sync scenarios against production CRMs directly. Both HubSpot and Salesforce offer sandbox environments. Create Make connections to sandboxes for development and testing, then swap to production connections when ready.
Before deploying changes to production scenarios:
- Clone the scenario
- Make changes to the clone
- Test thoroughly in sandbox
- Schedule production deployment during low-activity periods
- Monitor closely for the first 24-48 hours
Performance Optimization
Make charges based on operations, so efficient scenarios save money. Optimization strategies include:
- Batch operations: Use bulk upsert modules instead of individual record operations
- Filter early: Apply filters before expensive API calls, not after
- Reduce polling frequency: Only poll as often as your use case requires
- Cache lookups: Store frequently-accessed reference data in Make data stores
When using AI research agents to keep CRM data up-to-date, these same optimization principles apply. Efficient sync workflows reduce costs while maintaining data freshness.
Frequently Asked Questions
Deletion sync is optional and risky. If enabled, implement soft-delete patterns where records are marked as deleted rather than removed. This prevents accidental permanent data loss and allows recovery. Make can watch for status field changes (e.g., "Archived" or "Deleted") rather than actual record deletion events.
Make's infrastructure is highly available, but outages happen. Webhook triggers queue events during brief outages and process them when Make recovers. For longer outages, some events may be lost. Implement daily reconciliation scenarios that compare record counts and modification timestamps between systems to catch any gaps. This is particularly important for maintaining CRM data quality.
Yes. Make's CRM connectors support custom objects and fields. When you connect your CRM, Make discovers your schema including custom elements. For complex custom objects, you may need to use Make's HTTP module with the CRM's REST API for full flexibility. The reusable workflow patterns we discuss elsewhere apply here too.
Multi-select fields often use different formats. HubSpot separates values with semicolons while Salesforce uses a different delimiter. Use Make's text functions to split, transform, and rejoin values. Create reusable functions in Make for common transformations you'll need across multiple scenarios.
Make pricing is based on operations (each module execution counts as an operation). A simple two-system sync might cost $29-99/month depending on volume. Complex multi-system sync with high volume can reach $299+/month. Start with a free trial to estimate your actual usage before committing.
Use native integrations when they meet your needs exactly. They're simpler, cheaper, and more reliable. Use Make when you need: custom field mapping, conditional sync logic, multi-system orchestration, or data transformation that native integrations don't support. Many teams use both, with native integrations for simple sync and Make for complex workflows.
Conclusion
CRM sync isn't a one-time setup but an ongoing operational concern. As your GTM stack evolves, your sync requirements change. Make provides the flexibility to adapt without rebuilding from scratch. The visual scenario builder makes changes accessible to non-developers while maintaining the power needed for complex enterprise requirements.
Start with a clear understanding of which data needs to sync, in which direction, and how conflicts should resolve. Build incrementally, testing each sync direction independently before combining into bi-directional flows. Invest in error handling and monitoring from day one, not as an afterthought.
For teams looking to add intelligence to their sync workflows, consider how a context engine like Octave can determine sync priority, routing, and data enrichment based on your ICP and persona definitions. This separates the "what" (Make handles execution) from the "why" (Octave handles context), creating more maintainable and adaptable systems.
The patterns in this guide will serve you well whether you're syncing two systems or twenty. The key is building on solid fundamentals: clear field mapping, robust error handling, and thoughtful conflict resolution. Get these right, and your CRM sync will be an asset rather than a constant source of data quality problems.