One of the inherent challenges that many business leaders face with using customer relationship management (CRM) systems is that customer data is often duplicated or contains errors (outdated street addresses, phone numbers, email addresses, etc.).
It’s a widespread problem. A 2013 analysis of millions of B2B customer records by NetProspex reveals that more than half of the companies evaluated had a “data health” score of less than 2.7 on a scale of 1 to 5 (with 5 being the best possible score) across four areas: record duplication, record completeness, phone connectability, and email deliverability. Of these four areas, phone connectability or the accuracy of a customer’s phone number, generated an average score of just 1.2 out of 5.
Because so many companies rely on CRM data for marketing campaigns, customer segmentation,and other purposes, analytics can help marketers and other groups identify bad customer data and take corrective action. Doing so can improve operational efficiency and save money by eliminating or reducing wasteful, undeliverable email and direct mail.In the past, identifying duplicate customer records out of hundreds of thousands or even millions of entries was a daunting challenge. But today, there are a number of ways that analytics can be applied against customer data to identify identical customer records. For starters, analytics can identify false positives (matched records that aren’t truly matches) and false negatives (missed matches) and extract records that appear to be but aren’t actually duplicates.
Meanwhile, data discovery techniques can help marketers and other decision-makers quickly examine customer records and identify incorrect data and gaps (missing phone numbers, email addresses, and other information). These steps can include making notations in contact center systems to ask customers the next time they reach out for assistance to correct or provide information.
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