A recent report by Salesforce reveals 44% of survey respondents estimated a loss in revenue as a result of poor quality CRM data.

Despite billions spent on CRM data management software, companies are still struggling with the most basic challenges of CRM data quality. From sending out emails with typos, to wasting millions in direct mails to wrong addresses, companies are buckling under the weight of poor data – in a data-driven age. 

The timing couldn’t be more wrong. But the good news is, you can still improve your CRM data quality by approaching the problem strategically. How?

Read on to know more.


To effectively manage customer relationships, organizations need to collect various types of CRM data such as:

customer 360 process

Contact data (first and last names, titles, phone number, address)

Demographic data (gender, age, marital status, education level, profession, job title, company etc)

Behavioral data (purchases, payments, customer support tickets, customer service activities)

Psychographic dat (opinions, lifestyle, personal values, social media preference etc)

Additionally, CRM data capture also includes transactional data like purchase history, customer preferences, and interactions to gain insights into customer behavior and tailor personalized experiences. Therefore, CRMs act as a central information database that everyone in the organization depends on.


In addition to being a central database, CRMs are pivotal for business operations. The data is used in marketing, sales, and business analysis to find opportunities for revenue and growth. However, it is an unfortunate reality that CRM data is often outdated, messy, and noisy.

For example, the issues highlighted in the table below are common data quality challenges that impact revenue, growth, decision-making, customer interactions, and overall business success.

Data Inconsistency- Different spellings of customer names (e.g., "John Smith" vs. "Jon Smyth")

- Inconsistent address formats (e.g., "123 Main St" vs. "123 Main Street")
Data Accuracy- Errors in data entry (e.g., incorrect birth dates, misspelled contact information)

- Outdated customer information (e.g., outdated phone numbers, addresses)
Data Duplication- Creation of multiple customer records for the same individual

- Duplicate entries resulting from system integration issues
Data Governance- Absence of clear data quality policies and procedures

- Unclear data ownership responsibilities leading to inconsistencies
Data Incompleteness- Missing customer preferences (e.g., product preferences, communication preferences)

- Incomplete capture of purchase history or transaction details
Data Integration- Challenges in consolidating data from different systems or platforms

- Inability to link customer interactions across various touchpoints
Data Security- Data breaches leading to unauthorized access to customer information

- Loss of data integrity due to inadequate security measures
Data Timeliness- Delayed or outdated data that hampers real-time decision-making

- Lack of timely updates to customer profiles and interactions

Data analysts and engineers spend thousands of hours fixing these CRM data quality issues. Moreover, small businesses that cannot afford data analysts expect their business users to manually solve these problems using Excel.

As a result, CRM data quality issues remain a challenge because companies are still relying on manual processes to resolve problems. To make it worse, most companies do not have a strategy to ensure data quality. There is an overall disparity in systems as well as in human resources to manage data quality challenges!

How do you ensure data quality in crm?

CRM data quality is defined by JW Peltier as:

“when the information collected across multiple transactions, touchpoints, and channels accurately reflects the behavior and sentiments of customers both collectively and individually.” 

Unlike product data, customer data is heterogeneous by nature and is collected from different sources and in different formats (textual and numerical).

This means, key areas of focus in CRM data quality must be:

Well integrated customer data from different touchpoints and channels
➡️ Example: connect website form data + social media data + offline survey data

A data quality management strategy that addresses dirty data at the collection point & ensures CRM data integrity
➡️ Example: Implementing data entry guardrails on web forms

Incorporating a data quality management tool or platform that facilitates business users to fix data quality issues.
➡️Example: using a no-code data cleaning solution to clean, deduplicate, and consolidate data

Despite being seemingly flexible and robust, most CRMs have failed to prioritize data quality. Even a popular CRM like HubSpot only offers basic data treatment options. More importantly, these platforms do not allow for organizational-level transfer of customer information, meaning companies are still struggling with disparate datasets! 

In order to fully benefit from the potential of CRM platforms, data quality metrics must be implemented as part of the data quality management strategy.

what are CRM Data Quality Metrics

CRM data quality metrics refer to specific indicators used to assess the accuracy, completeness, consistency, timeliness, integrity, and accessibility of data within a CRM system. By defining and monitoring data quality metrics, organizations can gain insights into the health of their CRM data, identify areas for improvement, and take corrective actions to enhance data quality.

To begin, here are some basic data quality metrics you can start implementing now (with examples).

CRM Data Quality MetricActionable ItemExample
Data CompletenessCreate a data completeness checklist and validate mandatory fieldsInclude mandatory fields: customer name, email address, phone number. Validate that these fields are populated for each CRM record.
Data AccuracyPerform data accuracy checks through external data validationValidate customer addresses by comparing them with a trusted postal address database or geocoding services.
Data ConsistencyEstablish data standardization guidelines and enforce formattingDefine a standardized format for phone numbers (e.g., +1 (123) 456-7890) and ensure consistency across all CRM records.
Data DuplicationImplement data deduplication processes and merge duplicate recordsIdentify duplicate customer records based on matching criteria (e.g., name, email) and merge them into a single, accurate CRM record.
Data ValidationVerify and validate address data, phone data, or email data More than 80% of address data is incomplete, obsolete, and inaccurate. Prioritize address data validation.

Interestingly, companies that have a defined data quality metric, are in a far better position to solve their CRM data quality challenges. For example, a company that has set completeness as its first data quality goal can identify the root causes of incomplete data. They might discover they need better web forms, or alternatively, they need to train employees with better data entry practices.

Low Quality vs High Quality Data costs

Ironically, many companies are reluctant when it comes to data quality, often fearing increased expenses. The thought of hiring analysts, revamping processes, and getting stakeholder approval can be scary.

However, ignoring data quality challenges won’t make it go away. In fact, it will only worsen problems. In the table below, we help you understand the costs of living with poor-quality data vs the cost of improving data quality.

Cost of Ignoring Low Data QualityCost of Maintaining Higher Data Quality

Verification Costs: Additional time and resources required to verify the accuracy of data. For example, a healthcare provider spending extra hours confirming patient information to avoid billing errors and insurance claim rejections.Efforts to Detect and Correct Defects: Implementing data quality checks and validation processes, reducing the need for manual verification. For instance, using automated algorithms to cross-reference customer data during data entry, minimizing verification costs.

Reentry Costs: Repetition of data entry or migration due to errors or incomplete information. For instance, a retailer having to reenter product information in their system after incorrect or incomplete data was initially captured.

Redesign Processes: Modifying workflows and implementing data capture mechanisms that reduce errors, minimizing the need for reentry. For example, integrating barcode scanning technology in inventory management systems to eliminate manual data entry errors.
Compensation Costs: Expenses incurred due to mistakes or inaccuracies, such as compensating customers for errors or rectifying service issues. For instance, a telecom company reimbursing customers for billing errors caused by incorrect data entries.

Data Quality Tools: Investing in automated data validation and cleansing tools that proactively identify errors, reducing the occurrence of compensation costs. For example, using software to detect and correct anomalies in customer billing data before issuing invoices.

Human Resources Costs: Additional resources required to manually review and correct data errors or discrepancies. For example, a financial institution employing data analysts to manually rectify discrepancies in customer account records.Training Employees: Providing comprehensive data quality training programs to employees, enabling them to input accurate data from the beginning, thereby reducing the need for extensive human resource involvement and the associated costs.

Wrong Decision Costs: Financial impact resulting from incorrect decisions made based on inaccurate or incomplete data. For instance, a marketing campaign targeting the wrong audience due to flawed customer segmentation data, resulting in wasted advertising expenditure.Set up Management: Establishing data governance frameworks and policies that ensure data accuracy and reliability, reducing the likelihood of wrong decision costs. For example, implementing regular data quality audits and data stewardship roles to maintain high data standards.
Sunk Costs: Resources wasted on actions based on flawed data, resulting in ineffective or inefficient outcomes. For example, a manufacturing company investing in a product line based on inaccurate market research data, leading to low sales and financial losses.Data Quality Remediation: Implementing proactive data quality measures and investing in data cleansing tools to prevent sunk costs. This includes conducting regular data audits, employing data quality experts to identify and rectify inaccuracies, and utilizing data analytics to monitor and improve data quality continuously. By avoiding sunk costs resulting from flawed data, organizations can redirect resources towards more productive initiatives, enhance decision-making, and drive better business outcomes.

Businesses must invest in improving data. Ignoring or living with poor CRM data quality can cause long-term damage to an organization’s reputation. It may seem expensive and tedious, but improving your data can help your business with long-term growth.

CRM Data Quality Best Practices

If you’ve decided to improve your CRM data quality, here are some best practices our experts recommend that you can get started on immediately.

Understand Your Data:

Firstly, identify the “quality” of key data fields that impact your marketing activities, such as customer contact information, purchase history, and preferences. Are all names in the proper case? Are the records duplicated? Understand the problems of your data before making any changes.

Cleanse and Update Data:

Once you’ve identified the problems, you can create a CRM data cleansing plan. You can start small. For example, plan to reduce duplicates of customer data for the past year. Alternatively, you can plan to update customer information, such as addresses and contact details, to maintain up-to-date records.

Standardize Data Formats:

Next, establish consistent data formats for fields like phone numbers, email addresses, and postal addresses. For example, decide whether you want all company names to be in UPPERCASE or LOWERCASE. Or, decide whether you want all phone numbers to come with country codes. Based on this decision, you can then set up parameters on the web form, and proceed to standardize your data.

✅ Implement Data Validation:

Set up validation rules to catch data entry errors and enforce data accuracy. For example, validate email addresses to ensure they follow the correct format or require specific data fields to be filled before saving a record.

✅ Employee Training on Data Management: 

Provide training to your marketing team on data entry best practices and the importance of data quality. Remember, data quality is not a technology goal. Instead, it is more of a human resource goal. As long as humans are filling and handling data, they need to be trained on best practices.

✅ Leverage Data Quality Tools:

Explore user-friendly data quality tools that don’t require coding knowledge. These tools can help automate data validation, identify duplicates, and improve overall data quality.

✅ Monitor Data Quality:

Regularly monitor the quality of your CRM data through periodic audits and checks. Keep an eye out for inconsistencies, incomplete records, or outdated information. Address any issues promptly to maintain data integrity.

Collaborate with IT:

Lastly, work closely with your IT department to ensure data quality initiatives are aligned with technical capabilities. Collaborate on implementing validation rules, setting up data cleansing processes, and leveraging appropriate technologies.

As you can see, data quality is an ongoing process. Collect feedback from your team and stakeholders to identify areas for improvement. Regularly review and update your data quality practices based on evolving business needs and changing customer expectations.

How Can WinPure Help With a CRM Data Quality System?

WinPure is designed for both technical and business users. You don’t need to have coding skills. You don’t need to spend hours on Excel. You don’t need to worry about manually fixing data quality issues.

With its user-friendly interface and powerful features, WinPure offers a comprehensive suite of tools designed to cleanse, validate, and deduplicate CRM data. Don’t believe us? Watch this video on how to clean data using WinPure.

If you like what you see, feel free to download the free trial and experience no-code data cleaning!

Written by Farah Kim

Farah Kim is a human-centric product marketer and specializes in simplifying complex information into actionable insights for the WinPure audience. She holds a BS degree in Computer Science, followed by two post-grad degrees specializing in Linguistics and Media Communications. She works with the WinPure team to create awareness on a no-code solution for solving complex tasks like data matching, entity resolution and Master Data Management.

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