Data Hygiene in Demand Generation: Essential Practices for Clean and Accurate Data

Maintaining clean and accurate data is a critical aspect of any successful demand generation strategy. With the increasing reliance on data-driven decision-making, good data hygiene can help ensure that marketing and sales efforts are driven by reliable information.

However, data hygiene is becoming increasingly challenging to maintain. Privacy regulations like GDPR and CCPA have imposed strict requirements on data collection and usage, making it essential for organizations to ensure compliance while maintaining data quality. The proliferation of multi-channel marketing has also led to an explosion of data sources, making it more difficult to maintain consistency across platforms.

Poor data hygiene can be costly, with Gartner estimating an average annual loss of $12.9 million across organizations. This stems from wasted marketing spend, lost opportunities, and inefficient resource allocation. Beyond the financial toll, poor data quality can also undermine team trust, reducing confidence in data-driven insights and leading to potentially misguided decision-making.

The role of data hygiene in demand generation

Campaign performance

Data hygiene plays a crucial role in campaign performance. Clean, accurate data supports reliable attribution models, enabling marketers to measure campaign impact across touchpoints and precisely calculate ROI. This allows for more informed decisions on marketing spend and resource allocation.

Data hygiene is also essential for reliable A/B testing. When working with clean data, marketers can trust that their results accurately reflect audience preferences, leading to more effective optimization of campaign elements such as subject lines, content, and calls-to-action.

Lead management

Accurate data helps to ensure that lead scoring models are effective, allowing sales and marketing teams to prioritize leads based on their likelihood to convert. This is particularly important in the transition from Marketing Qualified Leads (MQLs) to Sales Qualified Leads (SQLs).

Research indicates that only 33% of marketers feel they can rely on their CRM data to make decisions. This lack of trust can severely impact the sales team’s confidence in the leads they receive from marketing. Clean data can help bridge this gap, fostering better alignment between sales and marketing teams and improving overall lead conversion rates.

Marketing automation

The effectiveness of marketing automation relies heavily on data quality. Clean data allows for more precise audience segmentation, enabling marketers to create highly targeted campaigns that resonate with specific customer needs and preferences. This approach ensures that nurture programs effectively engage the right individuals with relevant content.

Email deliverability, a critical factor in digital marketing success, is also directly impacted by data hygiene. Inaccurate email addresses and high bounce rates can harm sender reputation, leading to emails being flagged as spam. Maintaining clean email lists helps improve deliverability rates, ensuring messages reach their intended recipients.

Budget optimization

Data hygiene is also crucial for effective budget optimization. Accurate data allows for precise channel performance analysis, helping marketers identify which channels are delivering the best results. This information is vital for making informed decisions about resource allocation and maximizing return on advertising spend (ROAS).

With data decay occurring at an average rate of 25% – 30% per year, regular data cleansing and updating are essential to maintain the accuracy of these analyses. Without proper data hygiene, marketers risk making decisions based on outdated or inaccurate information, potentially leading to misallocation of resources and suboptimal campaign performance.

Common data hygiene challenges

System issues

• Duplicate leads: A prevalent system-level issue in data hygiene is duplicate leads, often resulting from data collected from multiple sources or a lack of standardization in data entry. These duplicates can skew analytics, lead to redundant marketing efforts, and create confusion in the sales process.
• Integration inconsistencies: When different systems (such as CRM, marketing automation platforms, and analytics tools) are not properly integrated, data can become siloed or inconsistent across platforms. This can lead to discrepancies in reporting and hinder the ability to get a holistic view of customer interactions.
• Missing or broken tracking: Technical glitches, improper implementation of tracking codes, or changes in website structure can all lead to incomplete or broken tracking. This makes it difficult to accurately attribute leads and conversions to specific marketing efforts, hampering the ability to measure campaign effectiveness.

Data issues

• Incomplete records: Incomplete records are a common data issue in demand generation. This can include missing contact information, incomplete demographic data, or lack of behavioral data. These gaps can hinder effective audience segmentation and reduce the ability to personalize your marketing efforts.
• Outdated information: Job roles and company information can change all the time, particularly in B2B marketing. Up to 60% of individuals change job functions within their organizations every year, meaning regular data updates are essential for maintaining accurate targeting and relevant messaging.
• Inconsistent formatting: Variations in how names, addresses, or other data points are entered into the system can create inconsistencies with their formatting. This makes it difficult to search and segment data effectively and can lead to duplicate records if not addressed.

Process issues

• Unclear ownership: This is a common issue in many organizations. When it’s not clear who is responsible for maintaining data quality, it often leads to neglect and deterioration of data over time.
• Inconsistent standards: This is particularly problematic in larger organizations where multiple teams or departments are inputting or accessing data in different ways, as these inconsistencies can lead to data discrepancies.
• Poor documentation: Without clear guidelines and documentation for data processes and standards, it becomes difficult to maintain consistency in data management practices across the organization.

Implementing data hygiene best practices

Audit your technology stack

Implementing data hygiene best practices starts with a comprehensive audit of the technology stack:

1. Review system integrations: Examine the integration between systems such as CRM, marketing automation, and analytics tools. Identify any gaps that may cause data flow inconsistencies.
2. Create a data flow map: Develop a visual representation of how data moves between these systems. This mapping helps pinpoint potential areas for data loss or corruption.
3. Ensure proper field mapping: Verify that data fields are consistently named and formatted across all platforms. Consistency is key for accurate reporting and analysis.

By following these steps, you can achieve seamless data flow, enhance accuracy, and improve the effectiveness of your demand generation efforts.

Set clear data standards

Developing clear data standards is essential for maintaining data hygiene within your organization. To achieve this, consider the following steps:

1. Establish naming conventions: Set standardized formats for specific data fields, like phone numbers and company names, to ensure consistency in how information is entered and stored.
2. Define required fields: Clearly identify which fields must be completed for a record to be deemed valid. This helps capture the essential information necessary for effective demand generation.
3. Set quality criteria: Establish standards for data accuracy, completeness, and timeliness. For example, an organization might require that email addresses be verified within 30 days of entry to be considered current.

By implementing these data standards, organizations can significantly improve the quality and consistency of their data.

Establish data validation processes

Implementing robust validation processes is key to maintaining data hygiene:

1. Set up automated checks: Implement automated processes to flag issues like invalid email formats, duplicate records, or missing fields. These checks can significantly reduce the manual effort needed for data cleaning and ensures consistent application of data standards.
2. Conduct manual reviews: While more time-consuming, manual reviews are crucial for certain aspects of data hygiene, including periodic audits of data quality and reviews of records flagged by automated systems. These reviews can catch issues missed by automated systems and highlight areas for improvement.
3. Define error handling procedures: Establish clear protocols for managing data that fails validation checks, be that correction, quarantine, or deletion. Clear procedures ensure data issues are handled consistently and efficiently.

These validation processes not only improve data hygiene but also help uphold high-quality data standards.

Strategies for achieving data hygiene success

Team collaboration and training

Achieving effective data hygiene is a collective effort that requires collaboration across multiple departments. Each team plays a distinct yet interconnected role:

• The demand generation team plays a crucial role in defining data requirements and standards that align with their campaign and reporting needs. As they’re the primary users of the data, they can provide valuable insights into how data quality impacts their day-to-day operations.
• Marketing operations teams are typically responsible for implementing and maintaining data hygiene processes. This includes setting up automated data cleansing routines and managing integration between marketing systems. They often act as the primary point of contact for data-related issues.
• Sales teams are key stakeholders in data hygiene efforts, particularly in B2B organizations. They often input valuable data into CRM systems, so it’s crucial they understand and adhere to data entry standards. These teams can also provide feedback on the quality and usefulness of the data they receive from marketing.
• Naturally, IT teams play a vital role in the technical aspects of data hygiene. They manage system integrations and implement data security measures, often providing support for data migration or cleansing projects. They ensure that data management practices comply with relevant regulations and company policies.

To support these cross-team efforts and maintain long-term data hygiene, establishing clear accountability is essential. This involves defining roles and responsibilities for data management, setting performance expectations related to data quality, or possibly incorporating data hygiene metrics into team or individual evaluations.

Comprehensive training programs are vital to this process. By providing guidance on regulations and compliance, organizations can ensure team members understand the legal implications of data management. Additionally, training on data hygiene tools equips employees with the skills needed to maintain data quality.

This proactive approach not only enhances data accuracy but also fosters a culture of responsibility, ensuring that everyone understands the importance of maintaining high-quality data for overall success.

Choosing suitable tools and solutions

A variety of tools are available for maintaining data hygiene in demand generation:

• Data cleaning tools can help to identify and correct issues such as duplicate records, invalid entries, or inconsistent formatting. These tools can range from built-in features in CRM systems to specialized software designed for large-scale data cleansing.
• Data enrichment tools can enhance the value of existing data by adding information from external sources, filling any gaps, and providing additional context for segmentation and targeting. For example, these tools might add company size or industry information to B2B records, or demographic data to consumer records.
• Validation tools ensure the accuracy of data, particularly for contact information. Email validation tools can verify that email addresses are properly formatted and active, while address validation tools can ensure that physical addresses are correct and deliverable. Phone number validation tools can verify that phone numbers are in service and formatted correctly.

When selecting data hygiene tools, several key criteria should be considered:

Integration capabilities – these tools must connect seamlessly with existing systems like CRM and marketing automation platforms to facilitate smooth data flow and automate cleansing and enrichment processes as much as possible. For organizations dealing with large volumes of data or expecting significant growth, scalability is also an important factor.

Cost-effectiveness – while data hygiene tools can yield significant ROI, it’s important to weigh their costs against potential benefits, such as improved marketing efficiency, reduced manual data cleaning, and enhanced decision-making from higher-quality data.

Measuring and maintaining success

To demonstrate the value of data hygiene efforts and identify areas still in need of improvement, it’s important to establish clear metrics and key performance indicators (KPIs) that track data quality over time.

Regularly reviewing these metrics can highlight trends, such as the percentage of records with complete information or the rate of duplicate entries, allowing organizations to pinpoint specific issues. Additionally, linking data hygiene outcomes to broader business objectives – like improved campaign performance or enhanced lead conversion rates – can illustrate the tangible benefits of maintaining high data quality.

By continuously measuring and analyzing these factors, organizations can make informed decisions, adjust strategies as needed, and ultimately drive greater success in their demand generation initiatives.

Improve your campaigns with clean, reliable data

Your demand generation success starts with clean, actionable data. Poor data hygiene can lead to missed opportunities and wasted spend—but it doesn’t have to. To generate high quality leads with clean data today Visit our demand generation service page.