The cost of poor data quality on B2B sales & marketing

High-quality B2B data supports growth, efficient operations, and informed decision-making. Inaccurate or outdated data, however, can lead to costly consequences that impact sales effectiveness and operational efficiency. Poor data quality not only restricts the potential of a sales team but also incurs hidden costs, often unnoticed until they disrupt business processes. As organizations handle increasing volumes of data, keeping it clean and structured becomes essential to remain competitive in the B2B sector. This article addresses the significant risks and costs associated with inaccurate B2B data, outlining effective approaches to mitigate these challenges for stronger data-driven outcomes.

Poor B2B data quality is characterized by inaccuracies, incompleteness, inconsistencies, and irrelevance within datasets. Common issues include typos, missing values, duplicate records, outdated information, and errors stemming from data entry, incorrect formatting, or intentional modifications. Bad data—whether incorrect, inaccurate, or irrelevant—can lead to flawed conclusions and hinder effective decision-making. When data is unreliable, it impacts business outcomes, often blocking the path to achieving strategic goals. Ensuring accurate, relevant, and well-structured data through regular checks and cleaning is essential for successful business campaigns and informed decisions.

B2B data typically includes company details like contact information, industry type, size, location, and revenue. Poor B2B data can negatively affect sales and marketing by leading to ineffective outreach or even damaging a company’s reputation. There are several types of poor data quality issues, outlined below.

Duplicate records are a frequent issue, arising from duplicate leads, accounts, or contacts, often due to human error. These duplicates disrupt workflows, skew analysis, cause storage issues, and lead to repetitive communications. For example, in Account-Based Marketing (ABM), repetitive emails to the same contact can make campaigns feel impersonal, reducing conversion rates.

Tips for securing unprotected data:
Relying solely on manual checks is rarely sufficient in managing duplicates. Instead, consider an data cleansing service provider that detects, cleans, and manages duplicate data, customized to align with your company’s specific criteria.

This often happens when companies purchase lead or email lists from unverified or less reputable service providers. Data privacy regulations like GDPR and CCPA have redefined the importance of data security. Lacking proper protections or non-compliance with these laws can lead to costly penalties and damage to a company’s reputation. Privacy laws require explicit user consent for data usage, and improper handling can result in severe consequences.

Tips for securing unprotected data:
Ensure compliance by keeping CRM entries secure and removing unnecessary data. Regularly merge duplicate records, simplify data systems, automate lead-to-account linking, and host your CRM on compliant cloud software.

Data can quickly become obsolete. Relying on outdated data is like using an inaccurate map, which can mislead decision-making. A customer who once filled out a form may later subscribe to newsletters or respond to emails, but if the CRM isn’t updated, potential opportunities could be missed. Additionally, outdated data can arise from job changes, mergers, or inactive records.

Tips for refreshing outdated data:
Remove obsolete data before migrating or integrating systems. Consider outsourcing data cleansing to a dedicated team for accurate and efficient results, saving internal resources for other tasks.

Incomplete data lacks critical information, making it challenging for sales and marketing. Nearly 45% of sales representative’s face issues due to missing data, which limits lead scoring, segmentation, and sales outreach. For example, having only a buyer’s phone number without an email limits campaign engagement.

Tips for completing data records:
Use validation rules, regular audits, and automation to fill in gaps. Manual verification, data standardization, and incentives can further enhance data quality. Considering an data enrichment service provider that detects, cleans, and enrich data, customized it to align with your company’s specific criteria.

Inaccurate data is a significant source of “data pollution,” leading to incorrect conclusions and flawed decision-making. Studies reveal that inaccurate data affects a company’s adaptability, with 41% of sales representatives facing challenges due to incorrect data entries.

Tips for correcting inaccurate data:
Prevent inaccurate data from entering systems by implementing validation rules, audits, and specialized data cleansing tools. Regularly monitor data with validation software, feedback loops, and strong governance practices to maintain data accuracy.

Duplicate data and inconsistent data differ in that duplicates are exact copies, while inconsistent data lacks standardization. For example, “CMO,” “Chief of Marketing,” and “Chief Marketing Officer” may all refer to the same role but can disrupt analysis if not standardized.

Tips for standardizing inconsistent data:
Adopt a centralized approach with uniform naming conventions across departments to ensure data consistency.

Companies often collect excessive data, hoping to gain future insights. However, data hoarding can lead to high storage costs, inefficient data exchange, and coordination issues across departments as they struggle to access critical information.

Tips for managing hoarded data:
Focus on essential data and consolidate it in a central location. This streamlines analysis, reduces storage expenses, and improves team collaboration by making key insights readily accessible.

Does poor data quality actually impact your business? The answer is a resounding yes. Below, we break down the key ways bad data affects sales and marketing, emphasizing why it’s essential to address.

Sales teams depend on CRM data to inform their strategies and outreach. Unfortunately, poor data quality can consume time in unexpected ways. For example, a study by Experian found that 94% of businesses suspect their customer data is inaccurate. Moreover, research from ZoomInfo shows that 27.3% of a salesperson’s time is lost chasing bad data. Sales reps often spend hours correcting errors, hunting for the right contacts, and sending emails or making calls that don’t reach their intended targets.

Bad data is a major barrier to generating quality leads. For Sales Development Representatives (SDRs), unusable data—such as incorrect phone numbers—prevents contact with potential customers. Poor data accuracy in contact fields obstructs searches for quality leads and wastes time on ineffective outreach. A skewed picture of target customers further drains lead generation and marketing efforts, ultimately harming conversion rates.

Decision-making based on outdated or inaccurate data can lead to missed sales opportunities. Poor data quality results in flawed sales pipeline analysis and inaccurate forecasts. Inconsistent data disrupts key engagement points in the sales funnel, limiting reps’ ability to connect with leads effectively. Without reliable data, salespeople are unable to nurture and convert leads, leading to lost revenue and reduced demand for products or services.

Accurate and clean email addresses are essential for reaching leads, starting sales conversations, and closing deals. Outdated or incorrect email addresses lead to undelivered emails, which not only wastes resources but also impacts the success of email campaigns. A high rate of undelivered emails can damage a company’s reputation with internet service providers (ISPs). If ISPs detect low engagement rates, they may flag the sender’s domain as spam, increasing the risk of future emails landing in spam folders and further reducing customer engagement.

Many industries have regulations related to data accuracy and management. In B2B, inaccurate data can lead to compliance issues and potential fines. For example, using B2B data without recipients’ explicit consent may inadvertently violate consumer protection laws, especially if they haven’t opted in for marketing communications. Adhering to data management regulations is essential to avoid legal risks and maintain a trustworthy brand image.

Inaccurate data can damage a brand’s reputation. Simple errors, like addressing a prospect by the wrong name on a cold call or sending an email to the wrong recipient, can make a company appear careless or unprofessional. Customers receiving inaccurate or outdated information may view the company as untrustworthy. For example, receiving a promotional email with outdated offers or incorrect personalization details can frustrate customers and reduce loyalty. Maintaining accurate data helps protect your brand’s reputation, ensuring a positive experience for current and potential customers.

Poor-quality B2B data can disrupt sales and marketing efforts, leading to financial losses, operational inefficiencies, and reputational damage. Below are the main ways that bad data affects business performance:

Bad data often leads to errors that frustrate clients, especially when contact details or preferences are incorrect. A CRM with outdated or inaccurate information can cause sales teams to misaddress clients, reducing trust and creating a perception of unprofessionalism. In some cases, a sales rep may mistakenly try to resell a product or service to a current client, which diminishes the client’s sense of value and erodes loyalty.

Poor data quality impacts revenue by resulting in misguided sales strategies and incorrect targeting. Companies risk losing potential customers due to reputational damage, which can hurt revenue over time. Additionally, regulatory fines for non-compliance with data standards can be substantial, and correcting data issues can incur significant costs, affecting the bottom line.

High email bounce rates due to incorrect or outdated email addresses can damage a company’s domain reputation. Over time, poor domain health could lead to blacklisting by email service providers, severely limiting the reach of outbound communication and making it difficult to connect with prospects.

Organizations in the UK, for example, must comply with data protection regulations enforced by the Information Commissioner’s Office (ICO). Failure to maintain accurate data may lead to fines from regulatory bodies like the ICO, potentially costing businesses thousands or even millions. Such penalties highlight the importance of adherence to high data quality standards.

Inaccurate B2B data reduces operational efficiency, as employees spend valuable time fixing errors and searching for correct information. Sales teams, for example, waste hours attempting to reach outdated contacts, and marketing campaigns may be delayed due to inaccurate customer data. These inefficiencies lead to wasted resources and hinder productivity.

Outdated or irrelevant data alienates customers. Research shows that 93% of consumers receive marketing communications that don’t apply to them, leading to unsubscribes or even blacklisting of domains. A clean, current B2B database is crucial to delivering relevant, personalized content that retains customer interest and engagement.

The internet has a long memory, and mistakes due to poor data can hurt a brand’s reputation for years. Maintaining a positive brand image takes time, but bad data can undermine this work in an instant, eroding customer trust and loyalty. Even a minor mistake due to bad data can harm years of reputation-building efforts.

Bad data leads to misdirected campaigns that frustrate or disappoint customers. Inaccurate outreach suggests to customers that a company doesn’t understand their needs, making it likely they’ll turn to competitors. Customer satisfaction declines when campaigns miss the mark due to poor data, further increasing customer churn.

Bad data produces inaccurate insights, leading to flawed conclusions that misguide business decisions. Basing strategies on unreliable data leads to lost opportunities, inefficiencies, and financial setbacks. As most strategies depend on solid data foundations, poor data quality can undermine even the best business plans.

Poor data quality can lead to compliance breaches and legal issues, especially under strict data privacy laws like GDPR and CCPA. Mishandling customer information can result in costly legal repercussions, and compliance is now a critical aspect of handling B2B data. Following data privacy standards helps protect customer information and the company’s reputation.

In summary, maintaining high-quality B2B data is essential for avoiding these risks. Accurate data strengthens business relationships, improves operational efficiency, and protects against financial losses and compliance issues.

Research from IBM highlights that poor data quality costs U.S. businesses approximately $3.1 trillion annually, accounting for both the expense of correcting errors and the broader effects, such as reduced productivity and missed growth opportunities.

A Gartner survey further reveals that bad data costs organizations nearly $13 million each year, yet 60% of companies lack full awareness of its financial impact, as many do not track the associated business losses.

The effects of poor data quality vary widely based on industry, company size, customer base, and service offerings. Certain sectors, like healthcare and finance, face especially high stakes when data quality is compromised, experiencing more significant operational and compliance-related consequences.

When purchasing B2B data, select a reputable provider like DataJi or DataGenie. Avoid compromising on data quality for cost savings, as high-quality data minimizes the risk of errors. Look for providers that validate contact information, regularly update their databases, and offer compliance with data privacy standards. Consider providers with data enrichment capabilities, which help maintain up-to-date records by avoiding duplicates and inaccuracies.

Consistently managing your data involves data cleansing , removing duplicates, verifying the accuracy of contact information, and enriching existing records with missing details. Frequent audits allow you to identify gaps and eliminate outdated data. Using data enrichment provider like DataJi can resolve parts of this process, making it easier to keep your data clean, accurate, and ready for business use.

Data maintenance is an ongoing effort. Train your team to follow best practices for adding new data, avoiding errors that cause duplicates and inaccuracies. Provide resources like standard operating procedures for data entry, conduct presentations on the importance of data quality, and establish clear guidelines for B2B contact data management. This ensures long-term adherence to quality standards and minimizes the risk of errors re-entering your database.

To avoid the significant costs associated with bad data, organizations must invest in a strong data ecosystem that ensures data accuracy and reliability. Partnering with a trusted B2B contact data provider like DataJi offers expertise in data management and analysis, filling gaps in resources and experience that can be difficult to find in-house.

Working with a skilled data provider helps businesses manage and analyze data more effectively, supporting informed decisions and sustained growth. By reducing the impact of bad data, organizations can protect against unnecessary costs and maximize their business outcomes.