Brands are losing an average of 5.9% in annual revenue as a result of bad customer data, according to new findings.
A study by Royal Mail Data Services and DataIQ collated views about data from over 250 senior marketers across the UK, including opinions on the barriers stopping businesses from using their customer data effectively.
The report, How better customer data drives marketing performance and business growth, found that a third (33%) said they were ill-equipped to make the connection between their data and revenue, 30.6% reported an annual revenue cost of up to 5%.
23.3% stated poor data cost their business between 6-10% of revenue, while 6.8% said this figure was likely to be 11% or higher. This equates to an average of 5.9% annual revenue across all businesses.
Asked about one of the greatest issues associated with data, many respondents highlighted customer churn.
On average, 19.8% of customers defect from companies over the course of a year, but some companies state levels that far exceed this baseline figure.
In 6.2% of companies, for instance, marketers report churn over 41% annually, while churn is between 20-40% for 7% of businesses. Worryingly, 26.8% of marketers admit that they are unaware how many customers their company is losing, with a lack of data often the critical component.
The report found a number of reasons for poor data existing, including incomplete data forms, out-of-date information, duplicate contacts and spelling mistakes.
In many respects this has led to businesses cracking down on their third party data use, in a bid to take a firmer control of their data.
However, marketing analyst, Juanita McGowen, believes third-party data users rarely check how the data provider builds and maintains its information.
Speaking to MyCustomer earlier in the year, McGowen stated that businesses need to make more effort to ask the following, initial questions around the quality of third-party data, as opposed to shifting away from third party data use altogether:
• How reliable colleagues think the data is, and why.
• How the data been reviewed and maintained since it was collected.
• How much (and what kind of) data is needed to carry out marketing activity.
• Whether an audit has been run recently to check for duplicate information and consistency.