Migrating your Data is one of the most consequential stages in any Salesforce CPQ (Configure, Price, Quote) implementation.
Done correctly you may not even notice it, however, even the best-implemented CPQ can fail if the data is poorly structured or incomplete. You will likely experience quoting errors, failed automations, and disrupted workflows. Meticulous planning and expertise to help manage pricing models, ensure accurate data mapping, and resolve legacy system issues are key to
successful migrations.
With extensive experience managing successful Salesforce CPQ migrations, we understand how to address common challenges through a structured and tested approach. Here, we explore the most frequent Data Migration issues and strategies to resolve them, ensuring your Salesforce CPQ implementation is built on a strong foundation for success.
1. Duplicate data
- Duplicate records are one of the most common challenges during Salesforce CPQ migrations, often originating from inconsistencies in legacy systems. These duplicates can result in operational inefficiencies, quoting errors, and a frustrating user experience, especially for sales teams who rely on clean, accurate data for customer interactions.
- To overcome this, we perform an in-depth audit of the dataset to identify duplication patterns. With a deep understanding of CPQ’s data model, we ensure critical data points are accurately processed and use custom SQL scripts to eliminate duplicates, ensuring clean data is migrated. Identifying the causes of the duplication helps implement safeguards to prevent future issues.
2. Mapping data between old and new
- Data mapping is another key step in any Salesforce CPQ migrations. Legacy systems often organize data (pricing data, product bundles, or discount rules) differently from Salesforce CPQ, which if not carefully addressed leads to misaligned or missing information.
- The way we have addressed past projects is to extract and analyze the legacy data to identify gaps and misalignments. We then ensure all critical data points—both front-end and back-end—are accurately aligned to the correct fields. Additionally, CPQ-specific records, such as pricing schedules and product configurations, are created and validated to uphold the integrity of automated workflows, ensuring seamless functionality post-migration. Failure to map the data accurately means you could encounter significant issues post-migration, such as disrupted quoting processes or approval flows that fail to function correctly.
3. Configuring pricing and discount rules
- One of the more complex challenges in CPQ data migration is transferring pricing models and discount rules. Businesses often rely on unique pricing strategies—such as tiered pricing, volume discounts, or customer-specific rates—that need to be replicated in Salesforce CPQ without discrepancies.
- To manage this, pricing structures and discount schedules should be thoroughly analyzed and tested during migration. This includes replicating all relevant pricing conditions and ensuring compatibility with CPQ’s data model. Tools and workflows can be used to validate pricing logic, ensuring accurate quotes across all potential scenarios. Improperly configured pricing rules can result in significant quoting errors, causing frustration for sales teams and customers alike, as well as potential revenue loss.
4. Complex data models
- Many CPQ implementations involve intricate data models, including parent-child relationships between products, bundles, and pricing schedules. Migrating these complex models while preserving data integrity is critical to ensuring your Salesforce CPQ’s automations work seamlessly.
- The first step involves mapping and documenting these relationships in detail, ensuring that every connection (including everything from lookups and junction objects to nested bundles) is accounted for. You also want to simplify overly complicated data models by removing unused or redundant dependencies which will improve system performance and reduce maintenance needs. If this isn’t addressed, it can lead to automation breakdowns, delays in quoting, and increased long-term system costs, as teams work to address post-migration issues.
5. Testing and validation
- Thorough testing is vital to ensuring data accuracy and functionality within Salesforce CPQ. Every aspect of the system, from product configurations, pricing logic, and discount application, to the quote-to-order process should be tested rigorously before go-live.
- Automated validation tools can significantly reduce the time and effort required for this by comparing migrated data against predefined criteria and identifying discrepancies. Real-world scenarios should also be simulated to verify that CPQ workflows operate as expected. This ensures confidence in the system’s accuracy from day one and helps prevent you launching Salesforce CPQ with unresolved errors, leading to downtime, disrupted sales operations, and reduced user adoption.
Migrating data into Salesforce CPQ is far more than just a technical task, it’s the foundation for accurate quoting, streamlined processes, and scalable growth.
With Proventeq’s experience and expertise, challenges like duplicate records, improper data mapping, and inconsistent pricing can be effectively resolved, ensuring businesses fully leverage Salesforce CPQ’s capabilities.
