“Garbage in/garbage out erodes customer satisfaction,” stated Forrester Research in a report entitled Poor Data Quality: An Often Overlooked Cause Of Poor Customer Satisfaction Scores.” Customer service agents need the right data about their customers, purchases, and prior service history at the right point in the service cycle to deliver the right answers. But when their tool sets pull data from low-quality data sources, agents don’t have the right information to answer their customers.”
Sixty-nine percent (69%) of organizations believe that inaccurate data will undermine their ability to deliver an excellent customer experience, according to a 2018 study conducted by Insight Avenue for Experian Data Systems.
Poor customer experience has a direct impact on a brand’s reputation:
“Overall, 71% of respondents said their typical response to a bad experience is to stop doing business with the company. A slight majority (55%) typically tell friends and family about it in person or by email, while 42% said they complain to the company and 26% post a comment on social media,” according to a study released by the Economist Intelligence Unit (EIU).
The impact is that poor customer data costs organizations 6% of annual revenues, according to a Royal Mail study of businesses in the U.K.
How bad is the problem? A recent study of the more than 10-million customer records by Global-Z showed the following:
- 45% of the records contained invalid information.
- 38% had terms such as “Need New Address” in the address field.
- 12% had duplicate information in multiple fields.
- 5.5% had the identical values in the given name and surname fields.
Many of the remaining 55% valid records contained significant problems for creating a quality customer interaction. Different records about the same customer often did not match exactly. For example, information may have been typed incorrectly or a customer may enter his or her name and address differently on different documents. Name matching becomes a critical factor in building complete customer records.
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Data Quality Issues In China
Correcting Customer Data Problems
Best Practices for Data Integration