Migrating Salesforce CPQ data demands precision and careful planning. The complexity of CPQ data lies in its deeply interconnected nature - products, pricing, discounts, and more, are all linked through intricate reference and relational data structures. When moving this data between Salesforce environments, even the smallest misalignment can cause significant disruptions and take hours to fix, potentially putting your CPQ tool out of action until issues are resolved. 

Manual data migration can take a ridiculous amount of time, and comes with a lot of risk. The sheer volume of data, coupled with the need to maintain precise relationships, makes manual processes both time-consuming and error-prone. 

In this article, we’ll dive into the most common pitfalls of CPQ data migration and provide actionable solutions to help you avoid them. Whether you’re migrating from a sandbox to production or between Salesforce orgs, understanding these challenges and how to tackle them will help you with a successful transition.

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1. Poor Data Quality 

In CPQ systems, data quality is everything. If your data is messy, such as having missing prices or duplicate entries, these issues won’t magically disappear during migration. Instead, they’ll be right there with you after the migration, potentially causing bigger problems. It can cause a trickle-down effect. 

Let’s break this down with some practical examples:

Outdated Products: 

Imagine you have a complex product catalog selling physical products in the manufacturing space. Your product catalog has thousands of products in it, but you only actually sell a few hundred of them, but because of time constraints you haven’t deactivated them or removed them from the system. 

If you keep this old data, you run the risk of your sales team accidentally selling it to a customer, when it’s a product you can’t even make anymore. When manually migrating data, it’s sometimes easier to migrate everything rather than taking records out and potentially causing more errors downstream, which is how outdated products end up continuing to live in production systems. 

Duplicate Entries: 

If the same product appears multiple times in your system - perhaps one version with basic software and another with an updated version, this can lead to confusion. One entry might include standard features, then the product is updated to offer advanced functionalities. 

When these duplicates are migrated, your CPQ system could generate conflicting quotes. When sales reps encounter conflicting product details, they would then have to manually investigate which entry is accurate. This often involves cross-checking with product management or other teams, pulling them away from closing deals. 

These delays can pile up, slowing down the entire sales cycle. Reps may need to rework quotes, seek approvals for corrections, or clarify with customers, all of which drag out the process. This can cause missed opportunities as customers will have to wait longer for accurate quotes.

How to Solve It

Understanding the impact of poor data quality is the first step; now, let’s focus on how to fix it.

1. Data Validation: Your data needs to be thoroughly checked for errors. Automated tools can quickly identify missing fields, outdated information, and duplicates. Ensuring that your data is up-to-date and consistent before migration is crucial.
2. Data Cleansing: Once you’ve identified the issues, clean up your data. Correct inaccuracies, remove duplicates, and update outdated entries. This might involve standardizing product descriptions and ensuring that all pricing reflects the most current information available.
3. Data Enrichment: Enrich your data by filling in missing details, such as adding updated product specifications or new configuration options. Comprehensive data will improve the accuracy of your quotes. 
4. Test Migrations: Before committing to a full-scale migration, perform test runs with a subset of your data. For example, migrate data for a specific product category and review the resulting quotes to ensure accuracy. This step helps you catch any potential issues early, allowing for corrections before the entire system is affected.

By taking these steps, you lay the groundwork for a CPQ system built on accurate, reliable data. 

The Bottom Line

Data quality is the foundation of a successful CPQ system. These systems are designed to streamline your sales process, enabling you to generate quick and accurate quotes and increase your revenue. But when the underlying data is flawed, the entire system falters. Quoting a customer based on outdated product information should never happen. Not only could this cost you the sale, but it also risks damaging your credibility and brand reputation.

These issues don’t just cause small hiccups. They can lead to significant financial losses, and frustrated customers. Poor data quality means your team spends more time fixing errors and less time closing deals. It disrupts the efficiency that CPQ systems are supposed to deliver, leading to delays, and dissatisfied customers who may look elsewhere for a more reliable service.

In short, ensuring data quality is about avoiding mistakes and maintaining the smooth operation of your sales process, protecting your profitability, and sustaining strong customer relationships. By investing time upfront to cleanse and validate your data, you set your CPQ system, and your business, up for long-term success.

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2. Incorrect Data Transfer

The second pitfall you might come across when migrating CPQ data is data not being transferred during the upload, or not being transferred correctly, resulting in incorrect product information, price rules or product rules in your target org. 

This could be something like product descriptions not getting updated because you forgot to map the fields during the upload process, or it could be more serious like price rules aren’t updated correctly and are causing critical failures in your production system, like prices being overridden when they shouldn’t be. 

Let’s take a look at a practical example:

Incorrect Field Mapping

When using a data loader, you have to manually map the fields on your spreadsheet to the fields in Salesforce. This can be easy with a small amount of data to load, but with large volumes of data, like with Salesforce CPQ, this becomes a long, tedious process that is very easy to mess up accidentally. 

Let’s say you’re migrating price rules. Price rules consist of multiple related objects like price conditions and price actions that also need to be migrated, and in the correct order. Forgetting to map a simple field to a price condition or action can result in the rule not working properly, causing incorrect quotes to get generated or even worse, your CPQ system may not be usable at all until the problem is diagnosed and resolved. 

How To Solve It

1. Use API Names in Excel Sheets: To make it easier to map your data from excel sheets in data loader, use the API name for each column, to ensure you know exactly which field you’re mapping where. 
2. Review the success file: Once the upload is complete, review the success file to ensure everything looks as expected and then spot check in the system to ensure the records look accurate. 
3. Testing and Validation: Run tests in your target environment to ensure that the changes you made have taken effect, for example, if you change price rules, run a few tests and make sure the price rules are taking effect when they should be. 
4. Use an external tool for CPQ configurations: The best way to ensure data is transferred seamlessly through multiple Salesforce environments is by using a tool that is dedicated to deploying CPQ data.
   
   Deploying CPQ data is not like deploying ordinary data - not only are you deploying metadata in some cases (new fields, additional picklist values, changes to lightning record pages, etc.), you are also deploying configuration data, i.e. data that is used as the backbone of the configuration of your CPQ tool. Metadata and data deployments are completely independent in normal circumstances, and this can bring disruption to users when CPQ metadata and data are not deployed together.
   
   Using a tool that is specifically designed to handle this complex and unique workload is essential for seamless deployments that don’t interrupt your day-to-day operations.
   
   Tools like Salto for Salesforce CPQ can help make your migration of complex, related data, simple, easy and stress-free. It not only automatically maps everything from your source environment to your target system, but it allows you to visualize those complex CPQ relationships in a simple and easy way.
   
   

When using an external tool to deploy your CPQ data, rather than migrate it like data records, you can be confident knowing that what you built in your development environment is exactly what will be migrated to your target system, especially if your target is your production environment.

Salto for Salesforce CPQ is free to try - so, stop worrying about incorrect data transfer and start migrating your CPQ data in a stress-free, automated way today. 

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The Bottom Line

Transferring data between Salesforce environments is incredibly time consuming and manual work that can easily result in incorrect data being transferred between systems. It’s not always immediately obvious what the incorrect data was, and waiting for issues to arise can significantly hinder your user’s use of your CPQ system, meaning customers aren’t receiving quotes on time, or are receiving inaccurate quotes - either way you are potentially losing revenue. 

The best way to reduce incorrect data transfer is to use a specialized and dedicated tool for migrations, like Salto for Salesforce CPQ. By using dedicated tools you are ensuring data accuracy in every deployment, resulting in faster migrations, allowing you to deliver changes more quickly to your end users and increase customer satisfaction with more accurate and reliable quotes. 

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3. Lack of Proper Testing

Even with reliable and correctly transferred data, testing isn’t just a box to check. 

It’s a critical step that ensures your system functions as expected after migration. Skipping or rushing through this phase can leave you with a system with issues that only become apparent once you go live, causing disruptions that are both costly and time-consuming to fix.

Let’s break this down with some practical examples:

Data Errors

Suppose you’ve migrated a large volume of recent CPQ configuration changes between a development environment and production, but you haven’t run any tests once the data is migrated to production. Let’s say, during the migration, some product and pricing configurations didn’t transfer correctly, but that wasn’t flagged in any error files because you simply forgot to map the 

The results might be that a promotional bundle that includes a special discount might lose its discount linkage, resulting in quotes that either overcharge or undercharge customers. This kind of error can affect multiple transactions, and most importantly, your revenue. 

Without proper testing, these issues might go unnoticed until they start impacting customer quotes, causing frustration for your customers and leading to mistrust. 

Integration Failures 

It is highly likely your CPQ system will be integrated with other critical business systems like your CRM. These integrations are vital for ensuring that quotes, customer data, and inventory levels are synchronized across your business. 

If you skip detailed end-to-end testing, you might miss critical issues in how these systems interact post-migration. For example, an approved quote in your CPQ might fail to trigger the necessary actions in your CRM, such as creating a follow-up task for the sales team. 

Maybe it doesn’t update inventory levels in your ERP, leading to over-ordering. Such issues can disrupt your operations, causing lost sales opportunities, and increased operational costs as your team scrambles to resolve the problem. 

How to Solve It

Addressing the lack of proper testing is critical to ensuring a smooth and successful CPQ migration. Here’s how to implement a thorough testing strategy:

1. Comprehensive Testing Plan: Begin by developing a detailed testing plan that covers all aspects of the migration process. This plan should outline the specific areas to be tested, the types of tests to be performed, and the criteria for success. Ensure that your plan includes testing for data accuracy, system functionality, and integrations with other critical business systems like CRM and ERP.
2. Use Sandbox Environments: Conduct all testing in a sandbox environment that mirrors your production system. This controlled setting allows you to simulate real-world conditions without risking your live data or operations. Run test migrations with a variety of data sets, including those that are complex or likely to cause issues, to identify potential problems before they affect your live system.
3. Iterative Testing Cycles: Testing should be an iterative process. After each round of testing, review the results, identify any issues, and make necessary adjustments. Then, run additional tests to ensure that the changes have resolved the problems. This cycle of testing, refining, and retesting helps to eliminate errors and ensures that your system is fully prepared for launch.
4. Simulate Customer Interactions: To ensure that the system performs well under real-world conditions, simulate common customer interactions within the testing environment. This might include creating quotes, applying discounts, and processing orders. By mimicking actual customer usage, you can catch issues that might not be apparent in more technical tests.
5. Final Pre-Launch Testing: Before going live, conduct a final round of testing that includes a full migration of a subset of data. This last test should mimic the actual migration as closely as possible and include all system integrations and user workflows. Review the results carefully to ensure that everything functions as expected and that any remaining issues are addressed.

By following these steps, you can mitigate the risks associated with inadequate testing and ensure that your CPQ system migration is successful. 

The Bottom Line

Lack of proper testing can seriously jeopardize the success of your CPQ migration. Inadequate testing can lead to significant disruptions once the system is live. 

To avoid these pitfalls, rigorous testing is essential. This means more than just spot-checking data; it involves comprehensive, end-to-end testing in a controlled environment that mimics real-world conditions. Run test migrations with different data sets, simulate customer interactions, and validate that all system integrations work well together. This testing phase should be thorough and iterative, allowing you to identify and correct any issues before they impact your business operations.

By investing the necessary time and resources in a robust testing process, you ensure a smooth transition to your new CPQ system, maintaining the efficiency, accuracy, and reliability that your business depends on. Proper testing not only prevents costly disruptions but also reinforces confidence in your new system, both for your team and your customers.

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Summary

Migrating Salesforce CPQ data is complex, time consuming and highly manual, unless you have a tool in place to assist you. While an external tool can take the complexity out of migrations and leave you feeling confident about your migration, poor data quality and lack of testing can still impact your CPQ data migrations, so it is important to address these issues within your business. 

By employing the help of a tool like Salto for Salesforce CPQ you can remove the stress of CPQ deployments and focus on things like improving your data quality and testing strategies, ensuring an efficient and streamlined CPQ system, that works for your users and allows them to create accurate quotes, quickly for customers - resulting in more revenue and happier customers. 

Get in touch with Salto today if you would like to see how easy your CPQ migrations could be with Salto for Salesforce CPQ.