Customer Data Management

Customer data management (CDM) is a strategy that comprises the tools, processes, and people required to collect, manage, and analyze customer data.

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Customer data management (CDM) is a subset of master data management (MDM) that specifically manages customer data. CDM includes the collection and integration of data, data management, analysis and reporting, and data activation. 

This strategy combines customer data from diverse sources across the organization, including marketing, sales, product, and customer support, aggregating and normalizing it to create a detailed, unified customer profile. Marketing, sales, and other departments use this unified profile to create personalized, unique customer experiences.

Customer data management also involves legal to ensure that customer data is appropriately managed according to compliance and privacy regulations. 

Customer Data Management Technologies

Several technologies are involved in customer data management, including customer data platforms and data management platforms (DMP). 

Customer data platforms (CDP) collect and integrate zero, first, second, and third-party customer data from various sources, cleansing the data and creating a unified customer profile. CDPs can also provide analytics, such as predictive analytics, to help analyze customer data and activation channels to act on data insights.

Data management platforms (DMP) store and manage third-party customer data or anonymized first and second-party customer data. DMPs are primarily used to help improve ad targeting.

Read More: What Is The Difference Between CDP Vs. DMP Vs. CRM?

Why Do You Need Customer Data Management?

Organizations that implement customer data management ensure their customer data is high quality and accurate, improving data-driven decision-making and increasing loyalty and retention.

The best customer data management strategies include four main concepts:

  • Data governance: Implement standards for how data is captured, managed, stored, retrieved, and used.
  • Data quality: Ensure only high-quality data is maintained by implementing validation and cleansing processes against all data integrated into a single source of truth.
  • Data relevance: Only capture the data necessary to support business goals and be transparent about why and how that data is used.
  • Data security: Include the technology and processes to ensure all customer data is secure and accessible only to those who need it. Also, customer privacy preferences are stored and applied appropriately.

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