Attio Migration Playbook

I promised zero all-nighters and delivered a clean cutover.

Context

Migrating a CRM is rarely a simple task; it's often a high-stakes operation that can disrupt sales, marketing, and customer success if not executed flawlessly. My personal affinity for Attio stems from its unparalleled flexibility—the ability to truly shape the CRM to fit our unique business processes, rather than forcing our processes into a rigid system. This particular migration, however, was more than just a technical challenge; it felt like a final exam for my RevOps expertise. We were tasked with moving a substantial dataset: 120,000 records, encompassing 37 critical properties, and supporting complex multi-team workflows that touched every revenue-generating department.

The previous CRM, while established, had become a bottleneck. Its inflexibility hindered our ability to innovate, automate, and gain the granular insights we needed. The decision to move to Attio was strategic, aimed at unlocking greater agility and efficiency. However, the sheer volume and complexity of the data, coupled with the need to maintain business continuity and avoid any downtime, presented a formidable challenge. This wasn't just about transferring data; it was about re-architecting our entire revenue operations to leverage Attio's capabilities, ensuring a seamless transition for all users, and ultimately, delivering on the promise of a more efficient and insightful CRM environment. The success of this migration hinged on meticulous planning, robust data handling, and comprehensive user enablement.

Stack I leaned on

• Airbyte + dbt for extraction and normalization: Airbyte was instrumental for efficiently extracting the vast amount of data from our legacy CRM. Its wide range of connectors allowed us to pull data from HubSpot with minimal custom coding. We then leveraged dbt (data build tool) to transform and normalize this data, ensuring it was clean, consistent, and perfectly structured for Attio's schema. dbt's version control and testing capabilities were crucial for maintaining data quality throughout the process.
• Attio API + TypeScript scripts: The Attio API was the backbone for programmatically interacting with the new CRM. We developed custom TypeScript scripts to handle the complex data ingestion, property mapping, and the creation of new records, ensuring data integrity and adherence to Attio's specific requirements. These scripts also managed the relationships between different record types (e.g., companies, people, deals).
• Supabase for staging/QA: Supabase provided a flexible and scalable environment for staging our migrated data. This allowed us to perform rigorous Quality Assurance (QA) checks, identify discrepancies, and validate data before the final cutover, minimizing risks during the live migration. Its real-time capabilities also helped in monitoring the data as it flowed through our pipeline.
• Notion + Loom for enablement: User enablement was crucial for a smooth transition. Notion served as our central hub for creating comprehensive training materials, FAQs, and step-by-step guides. We supplemented these with Loom video walkthroughs, providing visual and easy-to-digest instructions for all teams, ensuring rapid adoption and proficiency with the new system. This multi-modal approach catered to different learning styles.

Playbook

1. Defined success criteria and blocked a freeze window: Before any data movement, we meticulously defined what a successful migration would look like. This included specific data completeness and accuracy targets (e.g., 99.9% of records migrated, 0 critical data discrepancies), performance benchmarks for the new system, and user adoption rates. Crucially, we established a "freeze window" – a period during which no new data entries or significant changes were allowed in the old CRM – to ensure data consistency during extraction.
2. Created field mapping and dedupe rules versioned in Git: One of the most critical steps was creating a comprehensive field mapping document, detailing how each property from the old CRM would translate to Attio. This involved mapping data types, picklist values, and relationships. Simultaneously, we developed robust deduplication rules to ensure a clean dataset, identifying and merging duplicate records based on predefined criteria. Both the field mapping and dedupe rules were versioned in Git, allowing for collaborative review, auditability, and easy rollback if needed.
3. Ran a dress rehearsal with 5% of data and documented gaps: To minimize risks, we conducted a full "dress rehearsal" migration using a representative 5% sample of our data. This allowed us to test the entire pipeline – extraction, transformation, loading, and automation rebuilding – in a controlled environment. Every discrepancy, error, or unexpected behavior was meticulously documented, and the identified gaps informed refinements to our scripts and processes. This iterative testing saved us from major issues during the final cutover.
4. Rebuilt automations with Attio Actions and tests: Our legacy CRM had numerous automations that were vital to our operations (e.g., lead assignment, task creation, email sequences). Instead of simply porting them, we took the opportunity to rebuild and optimize them using Attio's native "Attio Actions" capabilities. Each new automation was thoroughly tested to ensure it functioned as expected and integrated seamlessly with our new workflows, often improving upon the original logic.
5. Delivered trainings and a health dashboard after cutover: A successful migration isn't just about data; it's about people. Post-cutover, we delivered targeted training sessions to all affected teams, focusing on Attio's new features and updated workflows. We also launched a "health dashboard" in Metabase, providing real-time visibility into data quality, system performance, and key user adoption metrics, empowering teams to monitor the new CRM's health proactively and address any issues quickly.

Key Principles of a Successful CRM Migration

• Meticulous planning and scope definition: Clearly define migration goals, success criteria, and the scope of data and workflows to be migrated. This includes identifying all stakeholders and their requirements.
• Data cleanliness and normalization: Prioritize cleaning, deduplicating, and normalizing data before migration to ensure data integrity in the new CRM. "Garbage in, garbage out" applies strongly here.
• Phased approach with dress rehearsals: Conduct multiple dry runs with subsets of data to identify and resolve issues before the final cutover. This iterative testing minimizes risk and builds confidence.
• Robust QA and validation: Implement comprehensive testing procedures at every stage to verify data accuracy, integrity, and functionality in the new system. This includes both automated and manual checks.
• Comprehensive user enablement: Provide extensive training, documentation, and support to ensure smooth user adoption and minimize disruption. A migration is only successful if users embrace the new system.
• Automation of data transfer and workflow recreation: Leverage tools and scripts to automate data extraction, transformation, and the rebuilding of workflows in the new CRM. Manual processes are prone to error and delay.
• Clear communication and stakeholder management: Maintain transparent communication with all stakeholders throughout the migration process, managing expectations and addressing concerns proactively. Over-communication is better than under-communication.

Common Failure Modes (and Fixes)

1. Underestimating data complexity:
   • Problem: Assuming data from the old CRM is clean and directly transferable, leading to unexpected errors, data loss, and significant delays during migration. This is a common pitfall.
   • Fix: Conduct a thorough data audit and profiling before migration. Identify data inconsistencies, missing values, non-standard formats, and hidden dependencies. Plan for extensive data cleaning, transformation, and validation.
2. Lack of clear field mapping:
   • Problem: Inadequate or ambiguous mapping between old and new CRM fields, resulting in data loss, incorrect data types, or misaligned information. This can break reporting and automations.
   • Fix: Create a detailed, version-controlled field mapping document. Involve key stakeholders from each department to validate mappings and ensure business requirements are met. Use automated tools to verify field types and constraints.
3. Ignoring existing automations and integrations:
   • Problem: Focusing solely on data transfer and overlooking the need to rebuild or reconfigure critical automations and third-party integrations. This can cripple business processes post-migration.
   • Fix: Document all existing automations, workflows, and integrations in the old CRM. Plan for their recreation and rigorous testing in the new system. Prioritize mission-critical automations for early rebuild and testing.
4. Insufficient user enablement:
   • Problem: Launching the new CRM without adequate training, documentation, or support, leading to user frustration, low adoption, and decreased productivity. Users will revert to what they know.
   • Fix: Develop a comprehensive enablement plan including hands-on training sessions, video tutorials (e.g., Loom), detailed FAQs, and a dedicated support channel. Emphasize the benefits of the new system to users and address their concerns proactively.
5. Skipping dress rehearsals:
   • Problem: Rushing directly to the full migration without conducting smaller, controlled test migrations, leading to unforeseen issues, downtime, and a chaotic live cutover.
   • Fix: Always perform at least one full "dress rehearsal" migration with a subset of real data. Document all issues encountered and refine the playbook based on these learnings. Treat dress rehearsals as critical learning opportunities.

Metrics & Telemetry

The success of the Attio migration was quantified through several key metrics:

• Data completeness: Achieved an impressive 99.3% data completeness, ensuring virtually all critical information was successfully migrated from HubSpot to Attio. This was measured by comparing record counts and key property values before and after migration.
• Automations rebuilt: Successfully rebuilt 12 complex automations in just three days, demonstrating rapid adaptation to the new CRM environment and minimizing disruption to sales and marketing workflows.
• Annual stack cost reduction: Realized a significant 42% reduction in annual stack costs, highlighting the efficiency gains and cost-effectiveness of moving to Attio and optimizing our RevOps tooling.
• User adoption rate: Achieved 95% active user adoption within the first two weeks post-migration, indicating a smooth transition and positive user experience.
• Data accuracy: Post-migration audits showed a 99.8% data accuracy rate for critical fields, validating the effectiveness of our data cleaning and mapping processes.
• Downtime: Zero downtime during the entire migration process, ensuring business continuity and uninterrupted operations.

What stuck with me

• Owners need 5-minute video walk-throughs per flow: A key learning from the enablement phase was the power of concise, visual training. While comprehensive documentation is essential, busy team members often prefer quick, digestible content. Creating short, 5-minute video walk-throughs for each critical workflow or new feature in Attio proved incredibly effective. These videos, often recorded using Loom, allowed users to quickly grasp complex processes, reducing friction and accelerating adoption far more efficiently than lengthy written guides alone. They served as an invaluable, on-demand resource.
• Never ignore obscure properties; there's always a hidden integration: During data migration, it's tempting to overlook seemingly "obscure" or rarely used properties from the old CRM. However, this migration taught me a valuable lesson: every property exists for a reason, and often, that reason is tied to a critical, albeit hidden, integration or a specific business process. Ignoring these can lead to broken workflows, data loss, or unexpected system failures post-migration. A thorough audit of all properties, no matter how minor they seem, is crucial to uncover these hidden dependencies and ensure a truly seamless transition. This often requires deep dives with long-time users or system administrators.

Cost Snapshot

The financial investment in the Attio migration was strategically managed, with a clear focus on leveraging efficient tools and minimizing external dependencies.

• Airbyte Cloud: ~$50/month (for data extraction and initial loading, scales with data volume).
• dbt Cloud Developer License: ~$100/month (for data transformation and modeling, crucial for data quality).
• Attio (new CRM subscription): This replaced our HubSpot costs, resulting in the 42% annual stack cost reduction mentioned in metrics. The specific cost varies based on plan and users.
• Supabase Pro Plan: ~$25/month (for staging database and QA environment).
• Notion (existing subscription): No incremental cost, as we leveraged our existing workspace.
• Loom (Pro Plan): ~$10/month (for creating user enablement video walkthroughs).
• TypeScript development (internal engineering time): Approximately 2-3 weeks of dedicated engineering time for custom scripting and API integrations. This was the primary "cost" in terms of internal resources.

The total incremental tooling cost was less than $200/month, a negligible amount compared to the significant cost savings from switching CRMs and the improved operational efficiency. The investment in internal engineering time was quickly recouped through reduced manual effort and enhanced data quality.

FAQ

Q: How long did the entire migration process take from planning to full cutover? A: The entire process, from initial planning and discovery to the final cutover and post-migration support, took approximately 8 weeks. This included data auditing, field mapping, script development, multiple dress rehearsals, automation rebuilding, and comprehensive user training.

Q: What was the biggest challenge faced during the migration? A: The biggest challenge was undoubtedly the complexity of data normalization and deduplication. Our legacy CRM had accumulated years of inconsistent data entry and duplicate records. Meticulously cleaning and structuring this data for Attio's schema required significant effort and iterative refinement of our dbt models and deduplication rules.

Q: How did you handle historical data that wasn't directly relevant to Attio's structure? A: For historical data that didn't fit Attio's core structure but still held value, we archived it in a data warehouse (e.g., Snowflake) and provided access via Metabase dashboards. This ensured we retained historical context without cluttering the new CRM with irrelevant or poorly structured data.

Q: What advice would you give to other RevOps professionals planning a CRM migration? A: Start with a thorough data audit and cleaning. Over-communicate with all stakeholders. Don't skip dress rehearsals – they are invaluable for identifying issues early. And most importantly, focus on user enablement; a technically perfect migration is useless if your team can't or won't use the new system effectively.

Q: How did you ensure business continuity during the migration? A: We implemented a strict "freeze window" in the legacy CRM during the final data extraction. The migration itself was executed over a weekend to minimize impact. Our dress rehearsals ensured that all critical processes could be quickly restored in Attio, and we had rollback plans in place, though they weren't needed.

What I'm building next

I'm currently in the process of packaging this entire migration playbook, including all the scripts, checklists, and best practices, into a command-line interface (CLI) tool. The vision is to create a repeatable, semi-automated framework that other RevOps professionals can leverage for their own CRM migrations, significantly reducing the manual effort and risk involved. This CLI will guide users through each step, from data extraction and transformation to QA and enablement, making complex migrations more accessible. Additionally, I'm exploring integrating AI-powered data validation directly into the CLI to proactively identify potential data quality issues before they become problems. If you're a RevOps practitioner facing a CRM migration and are interested in being an early tester for this tool, please reach out and DM me. Your feedback would be invaluable in refining this solution.

Want me to help you replicate this module? Drop me a note and we’ll build it together.