Giving Your GTM Teams Quality B2B Data: Best Practices for Data Quality Management

Quality B2B data is paramount for Go-To-Market (GTM) to identify and understand their target audience more effectively, allowing them to tailor their messaging, offerings, and outreach efforts accordingly. 

Moreover, by leveraging high-quality data, GTM teams can make informed decisions, optimize their sales pipelines, and drive revenue growth. Without reliable data, GTM initiatives may suffer from inefficiencies, missed opportunities, and diminished customer satisfaction, hindering overall business performance.

This blog focuses on the critical aspect of data quality management within GTM operations, with a specific emphasis on conducting thorough data audits. Data audits are essential processes that involve evaluating the accuracy, completeness, consistency, and relevance of the available data sets. By performing regular data audits, GTM teams can identify and rectify any discrepancies or inaccuracies in their B2B data, ensuring that it remains reliable and actionable. 

The blog will explore best practices for conducting comprehensive data audits, including using advanced tools and techniques. Furthermore, it will highlight the role of data audit as a foundational step in establishing a robust data quality management framework.

What Do You Mean by Data Quality?

Data quality management in the context of GTM operations involves the systematic processes and strategies implemented to ensure that the data utilized by Go-To-Market teams is accurate, consistent, complete, and relevant. It encompasses various practices such as data collection, validation, cleansing, standardization, and governance aimed at maintaining the integrity and reliability of B2B data throughout its lifecycle. 

Essentially, data quality management within GTM operations focuses on optimizing the quality of data assets to support informed decision-making, enhance customer interactions, and drive business growth.

Accurate and up-to-date data empowers sales teams to identify qualified leads, personalize their outreach efforts, and prioritize their sales activities effectively. Similarly, marketing teams rely on quality data to create targeted campaigns, deliver relevant content, and optimize marketing strategies for maximum impact. 

Quality data is how you guarantee your sales and marketing teams are connecting with your target audience.

Moreover, data quality is crucial in fostering positive customer experiences and engagements. Businesses can build trust, loyalty, and long-lasting relationships with their clientele by ensuring customer data is accurate and consistent across all touchpoints. Data quality is fundamental to driving efficiency, effectiveness, and success across various GTM functions.

Auditing Your Current Data Quality

Before undertaking any data management actions, such as deletion, enrichment, purging, or deduplication, it’s crucial to comprehensively assess your existing data quality. This involves reviewing the quality of your data as it stands today, establishing realistic baselines, and understanding the percentage of clean and useful data within your datasets. 

Setting achievable goals is essential during this phase. You need to determine what percentage improvement in data quality you should aim for and the potential impact it will have on your business. For instance, setting goals to increase the percentage of clean and usable data by a certain margin can lead to more effective sales and marketing efforts, improved customer satisfaction, and enhanced decision-making processes. Reaching 100% accurate data is impossible, so you’ll want a goal that is realistic and measurable.

Auditing your B2B data involves systematically reviewing and evaluating the quality, accuracy, completeness, and relevance of your business-to-business (B2B) data sets. This process is essential for ensuring that the data you rely on for various business operations, such as sales, marketing, and customer engagement, is reliable and actionable. 

Components of B2B Data Audit

During a B2B data audit, examine various aspects of your data, including:

1. Accuracy: 

Assessing the correctness and precision of the information stored in your B2B databases. This includes verifying the authenticity of contact details, company information, and other relevant data points.

2. Completeness: 

Evaluate whether all necessary fields and information are present and up-to-date within your data sets. This involves identifying any missing or incomplete data that may hinder your business processes.

3. Consistency: 

Ensuring uniformity and coherence across your B2B data, particularly when data is sourced from multiple sources or integrated from disparate systems. Consistency helps prevent discrepancies and ensures data integrity.

4. Relevance: 

Determining the usefulness and applicability of the data for your specific business needs and objectives. This involves assessing whether the collected data aligns with your target audience, market segment, or ideal customer profile (ICP).

By conducting a B2B data audit, businesses can identify and address any data quality issues, improve decision-making processes, enhance customer experiences, and optimize business performance. The insights gained from the audit can inform data management strategies, data governance policies, and data cleansing initiatives, ultimately leading to more effective use of B2B data for achieving organizational goals.

By establishing realistic baselines and targets, you lay the groundwork for success throughout the data management process, ensuring that your efforts are focused and impactful.

Finding the Gaps During Data Audits

During a data audit, your primary goal is to identify gaps and issues within your dataset that may compromise its quality and usability. This involves asking critical questions to assess various aspects of the data:

Do Contacts Lack a Key Field You Need for Your Ideal Customer Profile (ICP)? 

This question addresses the completeness of your data, ensuring that essential information required for effective targeting and engagement is present.

Where Are Those Contacts Coming From?

Understanding your data sources is crucial for evaluating its reliability and relevance. This question helps assess the diversity and consistency of data sources and identifies potential inconsistencies or discrepancies.

Does One Data Source Use a Different Data Format?

Inconsistent data formats among different sources can challenge data integration and analysis. This question highlights the importance of data standardization and compatibility across various systems and sources.

Where Do the Sales or Customer Success Teams Encounter Issues?

Identifying pain points experienced by sales or customer success teams when utilizing the data is essential for understanding its usability and effectiveness. This question helps pinpoint areas where data quality issues may hinder their ability to engage with prospects or provide satisfactory customer support.

By addressing these questions during the data audit process, organizations can gain insights into the quality and reliability of their B2B data, enabling them to make informed decisions and take corrective actions to enhance data quality and optimize GTM efforts.

Deciding on Your Rules and Standardization

After looking over all your data, decide on your rules and standardization. You’ve got to set ground rules and guardrails to help you move from point A to point B. Teach your teams exactly how contact data should look. Discuss numbers, capitalization, abbreviations, monetary values, and field descriptions.  

If you are trying to do territory mapping and routing leads and lack data standardization, then you will have twice as hard of a job mapping your fields and building out your routing logic. You must account for every abbreviation, name, or zip code. If you have everything standardized in a pick-list format before mapping, you will have a much easier job.

Case sensitivity is essential. Eliminating case sensitivity is the best path forward. The more case-sensitive fields you have, the more likely you are to have errors, validation problems, etc. Check spelling usage. Different dialects or regions can have different spellings or data systems. Plan to have everything abbreviated or nothing at all. You don’t want to mix the verbatim form or the abbreviation-coded form.

The Cost of Low-Quality Data 

The real cost of low-quality B2B data to your business can manifest in various ways, impacting crucial aspects such as sales revenue and customer engagement. Here are some key factors to consider:

1. Lower Email Deliverability: 

Low-quality B2B data often leads to higher bounce rates and lower email deliverability rates. Emails failing to reach their intended recipients due to outdated or inaccurate contact information directly affects sales revenue. Not only does this result in wasted resources spent on email marketing campaigns, but it also hampers your ability to connect with potential leads and convert them into customers.

2. Impact Email Open Rates: 

Inaccurate or irrelevant data can significantly impact email open rates. When recipients receive emails that are not tailored to their needs or interests, they are less likely to open them. Low open rates not only diminish the effectiveness of your email marketing efforts but also reduce the opportunities for engaging with prospects and driving conversions.

3. Impact Email Reply Rates: 

Poor-quality B2B data can also affect the email reply rate, indicating the level of engagement and interest from prospects. If emails are sent to incorrect or outdated addresses, the likelihood of receiving replies decreases, impacting sales team productivity and hindering the progression of sales opportunities.

4. Lost Revenue Opportunity: 

Ultimately, the cumulative effect of low-quality B2B data can result in lost annual revenue for your business. Inefficient email campaigns, low open and reply rates, and missed sales opportunities all contribute to diminished revenue streams. The cost of not using high-quality B2B data extends beyond immediate financial losses, affecting long-term growth and competitiveness in the market.

The true cost of not utilizing high-quality B2B data can have far-reaching implications for your business, affecting sales revenue, customer engagement, and overall profitability. Investing in data quality management strategies and ensuring the accuracy and relevance of your B2B data is essential for maximizing business success and maintaining a competitive edge in today’s market.

Mastering Data Quality: A GTM Journey

We have highlighted the critical importance of data quality management for Go-To-Market (GTM) teams. We discussed the significance of quality B2B data in driving effective sales, marketing, and customer engagement strategies. Key points covered included the need for comprehensive data audits to assess and improve data quality, the impact of low-quality data on various aspects of business performance, and the importance of setting realistic baselines and targets for data quality improvement initiatives.

Finally, it’s essential to emphasize the long-term value of investing in data quality for sustained business growth and success. While the immediate benefits of data quality management may be evident in improved sales performance and operational efficiency, the long-term impact extends far beyond financial gains. 

Investing in data quality sets the foundation for long-term success, enabling organizations to make informed decisions, build trust with customers, and adapt to evolving market trends. By committing to ongoing data quality management practices, GTM teams can position themselves for sustained growth and competitiveness in the dynamic business landscape.

Prioritizing data quality management should be a strategic imperative for GTM teams long term. By embracing data quality as a core component of their operations and investing in continuous improvement efforts, GTM teams can unlock the full potential of their data assets and drive sustainable business growth and success.