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Picture Perfect Customer Data

Martin Doyle July 30th, 2014 Data Governance, Data Quality
picture perfect customer data

what is the Perfect Customer Data

In today’s data-driven world, with so many points of interaction with customers and so many data points about customers, pointing to a consistent and comprehensive representation of customer data is a complex challenge.

This is why maintaining a customer relationship management (CRM) system with a current and complete picture of every customer is more important than ever. Your business needs every customer interaction to have the benefit of accurate, up-to-date data, such as name, postal address, telephone number, and email address.

Unfortunately, even when you achieve accurate, up-to-date data it doesn’t stay that way because data decays. Customers get married, move, change phone numbers, and switch their primary email address. Snapshots of customer data are taken from different sources, which do not change at the same time, and not all customer data attributes within a source stay up-to-date. For example, a call center might be the best source for telephone number, while a billing system could be the best source for postal address, and confirmations sent by a self-service web portal may verify the best email address. External reference databases can also provide alternative sources for these and other customer data attributes.

This is why one of the most essential data quality functions leveraged by CRM systems is data matching, which links customer data from multiple sources, internal and external, merging and perfecting an accurate picture of any customer at any time—and maintaining an accurate picture over time.

Picture perfect customer data helps your business achieve its customer service, marketing, and sales goals.

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Written by Martin Doyle

Martin is CEO and founder of DQ Global, a Data Quality Software company based in the UK. With an engineering background, Martin previously ran a CRM Software business. He has gained a wealth of knowledge and experience over the years and has established himself as a Data Quality Improvement Evangelist and an industry expert.