Customer data that's streaming in from numerous sources - social, mobile, email, chat, CRM, recorded contact center interactions, etc. - are providing companies with a wealth of information about their customers that can be used to fine-tune customer experiences and develop highly-personalized offers and messaging. Yet despite all the buzz about the promise that Big Data holds, organizational leaders and front-line employees continue to be hindered by dirty customer data that can hamper decision-making and potentially damage customer relationships.I had a recent conversation with NetProspex president Michael Bird on this topic just before the company released a B2B marketing data benchmark report. The study, which is based on an analysis of millions of B2B contact records from customer and prospect databases, reveals that more than half of the companies assessed had a "data health" score of less than 2.7 on a scale of 1 to 5 (5 being the healthiest) across four practice areas: record duplication, record completeness, phone connectability, and email deliverability. Of the four practice areas that were evaluated, phone connectability or the accuracy of a customer or prospect's phone number, generated an average score of just 1.2.
By contrast, record duplication represented the category with the highest average score at 4.3. Although Bird says he's heartened to see companies make progress toward eliminating duplicate customer records, those organizations that continue to use redundant customer records not only waste spending through unnecessarily direct mailings, email, and phone communications with customers, they also run the risk of upsetting customers through repetitive messaging. This is true for both B2B and B2C customers.
Other studies reinforce the challenges around customer data integrity. A recent Experian QAS study found that 94 percent of businesses believe that some of their customer and prospect data may be inaccurate.
The insights that analytics tools can help provide customer-facing employees and executives are highly dependent on the quality and integrity of the data being used. As companies increasingly rely on data for evidence-based decision-making, the accuracy of the data being used is critical for making the right calls on the next best action to take with customers and to provide customers with relevant and personalized messaging and offers based on what's known about them
What do you find are the chief customer data obstacles facing your organization?