To succeed in B2B sales, businesses are required to have high-quality lead data. When data is of low quality, it causes wasted time, adverse financial effects, and lost opportunities. According to the IBM Big Data & Analytics Hub, poor data costs the US economy $3.1 trillion annually. Therefore, all B2B organizations that want their lead management to be effective should prioritize resolving issues related to data quality.

In this guide, we’ve discussed the five major data quality challenges faced by B2B companies and practical ways to overcome them.

Lead duplication and data deduplication

One of the most common problems concerning data quality with respect to B2B leads is duplication of leads. This occurs when one and the same potential customer is entered into your system more than once but with slight discrepancies within the provided information (e.g., different company names or email addresses).

Consequently, such duplication ruins your marketing and sales strategies, resulting in misused resources and bad customer experiences.

To fix this, you require a strong data deduplication process. Usually, this entails utilizing B2B lead distribution software with inbuilt deduplication features or implementing third-party data deduplication tools. These solutions rely on advanced algorithms and fuzzy matching techniques to identify and merge duplicate records, thereby guaranteeing a tidy and integrated lead database.

Lead-to-account matching and lead distribution

Accuracy is key in matching leads to the right accounts and distributing them to the appropriate salespeople in B2B sales. If not, mismatched leads cause inefficient sales processes and annoyance among customers.

One possible solution for this problem is implementing lead-to-account matching capabilities within your CRM or B2B lead distribution and management software. 

LeadAngel is a platform that offers various tools for lead management, including lead-to-account matching. This tool is designed to help sales and marketing teams by automatically associating incoming leads with the correct accounts in a CRM system. This process is essential for businesses to ensure that leads are appropriately managed, routed, and nurtured within existing customer relationships.

Also, you can use data enrichment services, which help fill in missing account details such as company size or industry type, to match leads more accurately to their respective accounts when they are routed for closure purposes.

Incomplete or inaccurate lead data

Incomplete or inaccurate lead data can severely hinder your sales and marketing efforts. Missing information or incorrect details, such as the names of positions occupied, email addresses, and company details, will result in time wastage, poor targeting, and low conversion rates.

You need to adopt data validation and enrichment processes to handle this issue. Data validation involves tools or scripts to ensure that lead data conforms to predefined formatting rules and is complete. However, data enrichment involves using third-party providers to bridge information gaps or confirm existing information accuracy.

Furthermore, introducing lead scoring and qualification procedures will help concentrate on highly qualified leads with potential opportunities.

Obsolete or rotting lead data

Many times, lead data becomes outdated because people change jobs, companies merge or go out of business, and contact information changes. Therefore, this decay can considerably affect your sales and marketing efforts, leading to wasted resources and missed opportunities.

To confront this challenge, you should adopt regular data-cleaning processes. Data cleansing refers to the identification, correction, or removal of outdated, inaccurate, and irrelevant data from your lead database. This can be done manually or by using automated tools that monitor and update your leads’ information.

Data silos and inconsistencies

Organizations segment their leads according to different departments. This is done to bring some order in the process, but it also creates disparities and duplicates. A lack of data integration across the board can create disjointed experiences for clients, ineffective processes, and lost business opportunities.

To overcome this problem, implement a centralized salesforce lead management tool in the system. This can be done by implementing an enterprise CRM like Salesforce or integrating existing systems through middleware or data integration tools.

Summary

This blog covers the top 5 data quality challenges faced by B2B companies in lead management.

  • Lead duplication (between Lead – Lead, Contact – Contact, Lead – Contact)
  • Lead-to-account matching
  • Incomplete/inaccurate data
  • Obsolete data
  • Data silos

It provides practical solutions to overcome these challenges, such as data deduplication, enrichment, validation, cleansing, and implementing centralized lead management systems.

Common questions related to B2B lead data quality

Why is data quality so important in B2B lead management?

Without high-quality lead data, businesses cannot effectively manage their b2b sales and marketing. Poor-quality data costs a lot, leading to wasted opportunities, resources, and reduced revenues. Research shows businesses can lose limitless money due to bad data quality.

How do I detect and delete multiple leads from my database?

Implement strong processes for data deduplication using built-in dedupe features in lead management software or third-party deduping tools. These systems are based on advanced algorithms and fuzzy matching techniques to identify and merge duplicate records.

What are some strategies for accurate lead-to-account matching?

Some of the best ways to match leads with accounts accurately include applying systems such as Salesforce’s lead router, which streamlines the distribution process. Data enrichment services can also establish missing account details, making precise matching easy.

How can I handle incomplete or inaccurate lead data?

Implementing validation processes for data can help ensure the formatting and completeness of lead data. Use enriching services to fill in gaps or validate accuracy. Implement high-quality lead prioritization through lead scoring and qualification.

What is data cleansing and why is it important?

Data cleansing involves identifying, correcting, or removing outdated, incorrect, or irrelevant information from your leads database. Avoiding data decay and keeping a current and correct leads database would require frequent cleaning activities.

How do I tackle siloed, inconsistent data across my organization?

Go for centralized and integrated lead management systems. Establish clear standards for data governance, standardized formats, and consistent naming conventions to maintain consistency.

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