The Hidden Costs of Poor CRM Data (and, how to fix it)

What is Poor Data Quality in CRM?

Before further depressing you with the consequences of poor data quality, it’s essential to understand what we mean by this term. Here's a handy reference table for understanding some common terms on this topic.

Aspect	Description
Inaccurate	Data with spelling errors, incorrect customer details, or typos.
Incomplete	Missing critical fields like contact numbers, job titles, or email addresses.
Duplicated	Multiple entries of the same customer or lead due to redundant manual entries.
Outdated	Information that no longer reflects the current situation of the customer, such as an inactive email address.
Improperly Formatted	Data that isn’t standardized, making it hard to extract meaningful insights.

CRM data can degrade over time for various reasons. People may enter data wrong (oops, was that me?), customers change their information, or a company simply does not regularly cleanse and update its records. The word regular is the key. Data management is a routine. And, without a strong data governance strategy, a CRM can quickly become a repository of useless junk, rendering it impossible to glean any insights or improve a even clesingle business outcome.

Why Focus on Data Quality?

A CRM system is supposed to provide a so-called “360-degree view of your customers”, but poor data quality destroys this vision, like a pair of broken glasses. For any successful marketing campaigns, sales follow-ups, or excellent customer service, accurate and up-to-date data is the most powerful tool in any business arsenal. The goal of this article is to help CRM managers, customer experience directors, and business executives understand the hidden costs associated with poor data quality, as well as to provide strategies to fix it before it becomes a critical problem.

The Hidden Costs of Poor Data Quality

1. Lost Revenue: Missed Sales Opportunities and Poor Customer Targeting

One of the most immediate impacts of poor data quality is lost revenue. It’s hard to ignore when number-go-down. As we know, CRM systems are designed to help businesses manage leads, contacts, and customer relationships. But, if the data in these systems is inaccurate, incomplete, or outdated, sales teams can miss out on valuable opportunities, literal money taken from your pocket. According to a Gartner study, poor data quality costs companies an average of $15 million annually.

When sales teams are working with poor CRM data, they may waste time chasing down dead-end leads or making incorrect assumptions about customer needs. Consider the following:

• Incorrect contact details: Sales reps may attempt to reach a decision-maker only to find out that the contact has moved to a different company. According to a report by Validity, 80% of deals are lost when the main contact leaves the organization
• Incomplete data: Without complete data, sales reps lack the information needed to tailor pitches to customer pain points, leading to missed upsell and cross-sell opportunities.
• Inaccurate data: A CRM filled with old data can lead to misguided targeting, causing your marketing efforts to miss the mark. 44% of businesses lose more than 10% of their annual revenue due to inaccurate CRM data.

To break it down further, consider this:

• Email deliverability issues: Bad email addresses result in bounced emails, hurting marketing campaigns. By the way, the can also damage a companies sender reputation. You can learn more about this here.
• Poor lead scoring: Inaccurate lead information can skew your lead scoring model, the holy grail of any marketing team manager. A poor lead scoring model results in low-priority leads being pursued till high noon while high-value opportunities slip away unnoticed.

A marketing firm targeting small and medium businesses (SMBs) found that over 30% of their CRM contact details were incorrect, leading to failed email campaigns and wasted time

After investing in a comprehensive data quality improvement program, they saw a 20% increase in conversion rates within three months.

A marketing firm targeting small and medium businesses (SMBs) found that over 30% of their CRM contact details were incorrect, leading to failed email campaigns and wasted time. After investing in a comprehensive data quality improvement program, they saw a 20% increase in conversion rates within three months.

2. Operational Inefficiencies: Wasted Time and Resources

Operational inefficiencies sounds like something discussed in a steel manufacturing plant, but it also applies to CRM systems. And, they are another hidden cost of poor CRM data quality. When your CRM is riddled with duplicate records or missing information, your teams will end up wasting their time searching for accurate data (if it exists at all), updating incorrect records with more confusing details, and even following up with the same lead multiple times without knowing it.

According to IBM, poor data quality costs U.S

businesses $3.1 trillion annually. Here’s how it manifests:

According to IBM, poor data quality costs U.S. businesses $3.1 trillion annually. Here’s how it manifests:

Issue	Description
Duplicate Records	Duplicated customer records lead to confusion. Sales teams may call the same customer multiple times, strangling any chance of future success, while support teams could be pulling their hair out as they try to get a clear idea of their customer interactions. All this redundancy leads to wasted labor hours and, worst of all, a poor customer experience that leads customers elsewhere - or in the worst case, leaving that dreaded negative review.
Manual Data Correction	Manual labor is so 90’s. When CRM data quality is low, teams spend significant time correcting errors that a trained chimpanzee could fix. In fact, data scientists spend 80% of their time cleaning data instead of generating insights.
Ineffective Collaboration	Poor data makes it harder for marketing, sales, and customer service teams to collaborate effectively. With more and more remote work taking place, this issue is even more present. When departments don’t trust the data, communication breaks down - not good for a company working remotely and can’t “fix it when we’re at the office.”

A SaaS company found that its CRM contained thousands of duplicate records, causing the sales team to call the same customer multiple times

This not only frustrated customers but also wasted valuable sales time. After implementing an automated data cleaning process, the company reduced duplicated records by 90%, freeing up their sales team to focus on more productive tasks.

A SaaS company found that its CRM contained thousands of duplicate records, causing the sales team to call the same customer multiple times. This not only frustrated customers but also wasted valuable sales time. After implementing an automated data cleaning process, the company reduced duplicated records by 90%, freeing up their sales team to focus on more productive tasks.

3. Increased Customer Churn

Customer churn is another silent killer of revenue growth. When customers feel that their interactions with your company are impersonal or based on inaccurate or misleading information, they are more likely to leave for competitors. No rocket science behind that I’m afraid. However, poor CRM data plays a significant role in this.

For instance, 75% of businesses report losing customers due to poor data quality that led to ineffective outreach. If you’re sending marketing materials to the wrong people, or worse, ignoring existing customers because of bad data, expect churn rates to rise.

A telecommunications company discovered that 25% of its customer profiles had outdated contact details, resulting in billing errors and unresolved support tickets

This led to a 15% increase in churn over two years. After implementing a data cleaning initiative, the company reduced churn by 10% within a year.

A telecommunications company discovered that 25% of its customer profiles had outdated contact details, resulting in billing errors and unresolved support tickets. This led to a 15% increase in churn over two years. After implementing a data cleaning initiative, the company reduced churn by 10% within a year.

4. Compliance and Regulatory Risks

If poor data quality in your CRM isn’t carefully curated, it could cost you more than just sales and salary bonuses—it could lead to massive compliance fines. Data privacy regulations have ramped up rapidly over the last decade. Compliance guidelines such as GDPR and CCPA require businesses (by law) to maintain accurate, up-to-date customer records. Failure to do so could result in fines or other penalties.

This is not just scary boogie man talk, the enforcement behind these acts is equipped with more teeth every year. Take the example of, British Airways who were fined £20 million for mishandling customer data, much of which was outdated and incorrect

Mismanagement of opt-ins: If your CRM isn’t tracking customer preferences correctly, you could send marketing materials to customers who haven’t opted in, risking compliance violations.

The risks aren’t just financial; they can damage your brand’s reputation as well. Customers expect companies to handle their data responsibly, and any breach of trust can lead to long-term damage. As you’re probably aware, news spreads like wildfire in todays online driven world, and data breaches and compliance failures are easy pickings for an exciting story.

5. Inaccurate Reporting and Decision-Making

Business intelligence is only as good as the data it relies on. In fact, any intelligence is. Inaccurate CRM data often leads to wonky reporting, which in turn can steer even the most seasoned sales mangers into a poor decision. A whopping 44% of companies report losing revenue due to poor CRM data impacting their decision-making processes. Can you imagine how much money that is?

Just think about making strategic decisions based on faulty reports:

• Bad forecasts: Never mind bad weather reports, bad sales can lead to a whole myriad of problems, from overstocked products, out-of-touch revenue targets, unrealistic growth expectations and much more.
• Misaligned marketing efforts: Bad data skews marketing segmentation, making all the work you put into your campaigns less effective and less able to gain insights from.

Strategies to Improve Data Quality in CRM

Let’s get to the exciting part. The good news is that poor data quality is a solvable problem. But it’s not something you can address once and forget about. A good dentist would never tell you to brush your teeth once a year. In the same way, data quality management in CRM systems is an ongoing process that requires continuous effort, regular audits, and proper tools. Below are several strategies that can dramatically improve the quality of your CRM data and, in turn, your company’s operational efficiency, customer satisfaction, and revenue generation (yes -that means more money!)

1. Data Entry Best Practices

The first step in improving CRM data quality is to ensure that the data entered into your system is accurate and consistent from the very start. Data entry errors are one of the leading causes of poor data quality, so implementing clear guidelines and standardized processes can reduce the likelihood of human error.

How to Improve Data Entry:

• Mandatory Fields: Make sure critical fields like name, email, phone number, and job title are mandatory for every entry.
• Use Validation Rules: Employ validation rules to ensure that the data entered adheres to pre-determined formats (e.g., a valid email format or the correct number of digits in a phone number).
• Training: Educate your teams on the importance of accurate data entry and provide training on how to avoid common mistakes.
• Automation: Where possible, automate data entry with tools that pull verified data from external sources or directly from customer input forms.

2. Data Validation and Cleansing

No system is 100% perfect, and no matter how careful you are with data entry, errors will eventually slip through -it’s something we all just have to accept. And, it means that regular data validation and cleansing routines are critical to maintaining clean and accurate CRM data. Data cleansing involves identifying and fixing problems such as duplicates, outdated information, or incomplete records.

Best Practices for Data Cleansing:

• Schedule Regular Data Audits: Periodically review your CRM to spot and eliminate duplicates, incomplete entries, and outdated information. When something becomes routine, it no longer feels like a chore.
• Automated Cleansing Tools: Use automated tools that help identify data issues, such as incorrect formatting or expired customer information. Solutions like Plauti Data Management or Plauti Data Action Platform can automate many aspects of data cleansing, allowing you to scale your efforts and keep your CRM clean over time​.
• Set Up Alerts for Duplicates: Ensure your CRM flags potential duplicate entries as they are entered, allowing users to address issues as they happen.
• Review and Update Contact Information: Build a process where contacts are regularly reviewed and updated. This could involve integrating third-party tools that enrich your CRM with more accurate and up-to-date information​.

An ongoing cleansing process not only keeps your CRM reliable but also improves efficiency by reducing the clutter and ensuring the data you have is useful.

An ongoing cleansing process not only keeps your CRM reliable but also improves efficiency by reducing the clutter and ensuring the data you have is useful.

3. Data Integration Across Platforms

For most businesses, CRM data doesn’t exist in a vacuum. Various departments may use different platforms—marketing automation tools, customer service systems, financial platforms—that all house customer data. The key to maintaining comprehensive CRM data quality is integration across these systems. This is know as a tech-stack, and we have a detailed guide about this you can check out here.

Benefits of Data Integration

• Unified Customer Profiles: Integrating data from various platforms into a single CRM system ensures that every department has access to the same, up-to-date customer information, minimizing the risk of data silos and inconsistencies.
• Real-Time Updates: With a good selection of integrations in place, customer data can be automatically updated across all systems, reducing the chance of outdated information creeping into your CRM.
• Automation: When data is integrated across platforms, automated workflows can ensure consistency and reduce the chance of human error.

4. Continuous Monitoring and Governance

As we’ve mentioned before, and will emphasize again; CRM data quality isn’t a one-time project—it requires ongoing monitoring and governance. It’s really important to stress this point, because so many people just want a “one-time, done-and-dusted” solution. Unfortunately no such solution exists, and probably never will. Data is like a river, a continuous stream of water entering your organization. Continuous data quality monitoring ensures that issues are filtered out before it contaminates your other data.

Continuous Monitoring Strategies

Automated Monitoring Tools: Tools such as Duplicate Check offer ongoing data monitoring capabilities, flagging errors like duplicates, missing fields, or inconsistencies in real time, letting you take action as they occur​.

• Data Health Dashboards: Set up dashboards that provide real-time insights into the health of your CRM data. These dashboards can highlight areas that need attention and help you track improvements over time​.
• Data Governance Framework: Establish a data governance policy that clearly defines who is responsible for managing and maintaining CRM data quality. This includes setting data entry standards, determining how often data audits should be conducted, and assigning roles for data quality control​.
• Set KPIs for Data Quality: Track key performance indicators (KPIs) like duplicate rate, invalid email rate, or percentage of completed fields to ensure data quality is improving.

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5. Leveraging Machine Learning for Data Quality Improvements

While traditional data cleansing and validation methods are critical, businesses can also leverage machine learning algorithms to identify and rectify data issues proactively. Machine learning tools can analyze patterns in your data and highlight anomalies or inconsistencies that manual processes might miss.

How Machine Learning Can Help:

• Data Decay Prediction: Machine learning algorithms can predict which customer records are likely to become outdated based on past trends, allowing companies to update them proactively​.
• Error Detection: Machine learning tools can identify patterns in data entry errors, helping companies fix the root causes of data quality problems before they become widespread.
• Automated Data Enrichment: Machine learning algorithms can automatically enrich customer profiles by pulling in additional, verified data from external sources.

As machine learning tools become more advanced, they offer businesses the potential to dramatically reduce the time and resources spent on data management while improving the overall accuracy and reliability of their CRM systems.

Wrapping up

Poor CRM data quality can have a devastating impact on your business, from lost revenue and operational inefficiencies to increased customer churn and compliance risks

The hidden costs are significant, but they can be avoided with the right strategies. By implementing data entry best practices, regular data cleansing, integrating your systems, and establishing continuous monitoring frameworks, you can dramatically improve the quality of your CRM data. Additionally, by embracing advanced technologies like machine learning, businesses can stay ahead of potential issues and ensure that their CRM remains an asset rather than a liability.

Wrapping up

Poor CRM data quality can have a devastating impact on your business, from lost revenue and operational inefficiencies to increased customer churn and compliance risks. The hidden costs are significant, but they can be avoided with the right strategies.

By implementing data entry best practices, regular data cleansing, integrating your systems, and establishing continuous monitoring frameworks, you can dramatically improve the quality of your CRM data. Additionally, by embracing advanced technologies like machine learning, businesses can stay ahead of potential issues and ensure that their CRM remains an asset rather than a liability.