In the world of revenue operations, maintaining accurate and up-to-date customer relationship management (CRM) data is critical. The quality of CRM data can directly impact business decisions, customer satisfaction, and revenue growth. Poor quality CRM data can result in wasted time, missed opportunities, and decreased productivity. It is crucial to have a system in place to ensure that the CRM data is accurate, complete, and reliable.

What are CRM validation rules?

One of the most effective ways to maintain high-quality CRM data is through the use of validation rules. Validation rules are pre-defined conditions that check the data entered into the CRM system and ensure that it meets certain criteria. These rules can be applied to various fields, such as contact information, lead sources, or product details, and can be customized to meet the specific needs of the business to orchestrate CRM data entry.

Why CRM validation rules are crucial for CRM data quality?

CRM validation rules are essential for CRM data quality because they help prevent errors, inconsistencies, and inaccuracies in the CRM data. By enforcing data standards and ensuring data integrity, validation rules enable businesses to make informed decisions based on accurate and reliable data. 

Here are 25 CRM validation rules that can help improve revenue data:

1. Mandatory fields: Require specific fields to be filled out before the record can be saved. For example, requiring the “email” field to be filled out before a lead can be saved.

2. Field data type: Validate the format of data entered into a specific field, such as phone numbers or email addresses, to ensure consistency and accuracy. For example, validating that the “phone number” field contains only numbers and dashes.

3. Field length: Restrict the maximum or minimum length of a field to avoid data entry errors or incomplete information. For example, restricting the “first name” field to a maximum of 50 characters.

4. Range of values: Define a range of acceptable values for a field, such as a date range or a numerical range, to ensure data accuracy. For example, defining a date range for the “birthdate” field to ensure it is within a specific time frame.

5. Unique values: Enforce uniqueness for a specific field, such as a lead source, to avoid Salesforce duplicate records and ensure data accuracy. For example, ensuring that there is only one lead source named “Google Ads.”

6. Lookup relationships: Validate the relationship between two fields, such as the relationship between a contact and an account, to ensure data accuracy. For example, ensuring that a contact is associated with the correct account.

7. Cross-object relationships: Validate the relationship between fields in different objects, such as the relationship between a contact and a product, to ensure data accuracy. For example, ensuring that a product is associated with the correct contact.

8. Pattern matching: Validate the format of data entered into a specific field using a regular expression, such as validating a credit card number. For example, validating that the “credit card number” field contains only numbers and is 16 digits long.

9. Formula fields: Validate the formula used to calculate a specific field to ensure data accuracy. For example, ensuring that the formula used to calculate the “total sales” field is correct.

10. Record type: Validate the record type to ensure that the correct fields are being populated with the right data. For example, ensuring that a “prospect” record is being populated with the correct fields.

11. Workflow rules: Validate workflow rules to ensure that data is being processed correctly and consistently. For example, ensuring that a lead is being routed to the correct sales representative.

12. Data consistency: Ensure that data entered in one field is consistent with data entered in other related fields. For example, ensuring that the “state” field matches the “country” field.

13. Data completeness: Ensure that all required data fields are filled out before saving the record. For example, ensuring that all required fields are filled out before saving a lead.

14. Data accuracy: Validate data entered into fields for accuracy by comparing it with external sources. For example, validating that the “company name” field matches the name on the business card.

15. Data normalization: Standardize data entered in fields to ensure consistency and accuracy. For example, converting all states to their two-letter postal code format.

16. Data deduplication: Identify and merge duplicate records to avoid confusion and ensure data accuracy. For example, identifying and merging duplicate contacts with the same email address.

17. Data profiling: Analyze data to identify inconsistencies and inaccuracies, and validate data against business rules. For example, analyzing lead data to identify leads that do not match the target demographic.

18. Data segmentation: Classify data into distinct categories to improve analysis and reporting. For example, classifying leads by industry or product interest.

19. Data enrichment: Add missing data or update outdated data to improve data accuracy and completeness. For example, adding missing phone numbers or email addresses to contact records.

20. Data standardization: Standardize data to ensure that it is consistent and easy to analyze. For example, standardizing product names to ensure that all products are listed consistently.

21. Data migration: Validate data during the migration process to ensure that data is transferred accurately from one system to another. For example, validating data during the migration process from a legacy system to a new CRM system.

22. Data cleansing: Remove unnecessary or irrelevant data to improve data quality and reduce storage costs. For example, removing leads that are no longer relevant or have not shown any interest in the product.

23. Data governance: Establish policies and procedures for managing data to ensure that data is accurate, reliable, and secure. For example, implementing data governance policies to ensure that all data is entered and managed according to established standards.

24. Data validation checks: Run data validation checks to ensure that data is accurate and consistent. For example, running a validation check to ensure that all leads are associated with a specific sales region.

25. Data monitoring: Continuously monitor data to identify and correct data quality issues. For example, monitoring leads to ensure that they are being properly routed to sales representatives.

The sooner you implement CRM validation rules the better

CRM validation rules are essential for ensuring data quality in the CRM system. By enforcing data standards, ensuring data integrity, and preventing errors, inconsistencies, and inaccuracies, validation rules enable businesses to make informed decisions based on accurate and reliable data. By implementing these 25 CRM validation rules, and tracking sales activities to enforce them, businesses can significantly improve their revenue data and gain a competitive advantage in their industry.