6 Best Practices for Managing Leads with MDM
Posted: Tue Jan 21, 2025 10:34 am
Learn how to use master data management correctly to manage potential customers.
Having a 360ยบ view of the potential customer is possible for those who successfully implement master data management in their organization. MDM ( Master Data Management ) is a term that has been used for about a decade, and yet it seems to be new to some organizations that are still trying to avoid mistakes that were already being made years ago.
Every business, regardless of size, struggles with inconsistent lead data . Data is captured across multiple applications and in different parts of the organization. It is poorly synchronized, with room for improvement in quality and a highly variable sense of opportunity.
For many, the solution is to implement a CRM. They seek to optimize the efficiency of their processes and increase the effectiveness of their operations, gaining in connection and proximity with the customer. But, in practice, most CRM implementations do not achieve the desired ROI and one of the causes behind the failure is the lack of integrity in the consumer and potential customer data.
Does your business have a single customer view? Do business owner database users trust the information you share?
Preserving and maintaining the quality of customer master data is an essential requirement to support business processes and corporate decision making, and achieving this requires knowledge of some best practices.
Data quality as an essential part of MDM
How to properly use MDM to manage leads?
Best practices applicable to customer and prospect data for MDM and CRM implementations include:
Validate data using other sources : Using third-party validation can confirm that demographic data is free of errors. Postal codes, telephone prefixes, or addresses are frequently misspelled, which can undermine any business initiatives that rely on them. Using address validation software that works in conjunction with CRM or ERP can validate the information collected about the potential customer 's transactional behavior and complement it with demographic information that can help you identify the potential customer's business needs.
Having a 360ยบ view of the potential customer is possible for those who successfully implement master data management in their organization. MDM ( Master Data Management ) is a term that has been used for about a decade, and yet it seems to be new to some organizations that are still trying to avoid mistakes that were already being made years ago.
Every business, regardless of size, struggles with inconsistent lead data . Data is captured across multiple applications and in different parts of the organization. It is poorly synchronized, with room for improvement in quality and a highly variable sense of opportunity.
For many, the solution is to implement a CRM. They seek to optimize the efficiency of their processes and increase the effectiveness of their operations, gaining in connection and proximity with the customer. But, in practice, most CRM implementations do not achieve the desired ROI and one of the causes behind the failure is the lack of integrity in the consumer and potential customer data.
Does your business have a single customer view? Do business owner database users trust the information you share?
Preserving and maintaining the quality of customer master data is an essential requirement to support business processes and corporate decision making, and achieving this requires knowledge of some best practices.
Data quality as an essential part of MDM
How to properly use MDM to manage leads?
Best practices applicable to customer and prospect data for MDM and CRM implementations include:
Validate data using other sources : Using third-party validation can confirm that demographic data is free of errors. Postal codes, telephone prefixes, or addresses are frequently misspelled, which can undermine any business initiatives that rely on them. Using address validation software that works in conjunction with CRM or ERP can validate the information collected about the potential customer 's transactional behavior and complement it with demographic information that can help you identify the potential customer's business needs.