Direct marketing relies upon reaching a targeted audience, based on customer data gleaned from point-of-sale, subscriptions and purchased lists. The potential for error when dealing with thousands or millions of customer records is indisputable and sometimes devastating. The horror stories are well-known: companies sending credit card offers to pets, children or even the deceased? These mistakes are inexcusable and easily avoided using basic data quality solutions.
The most common example of bad data is caused by a lack of data entry standards. Data originates from the fingertips of humans, who often make typos, forget to complete a field or apply different standards when entering information. The result can be multiple, inconsistent and variant views of the customer.
For example, Joe Smith downloads different products from the same online software site. He completes the online checkout form as a “guest” each time, meaning that he has to reenter his information at each purchase. One time, he gives his name as “Joe Smith.” The next, “Joseph Smith.” On the third download, he types in “Joe Smith” again but gives his work phone number instead of his home number. Later, when calling for upgrade information, the call center rep enters his name as “Jo Smith,” and a call to tech support the following week logs a call from “Joe Smith.”
Without data quality running in the background, the applications that manage customer data will view these as five separate instances of Joe Smith. This can lead to Joe receiving as many as five catalogs in the mail – a waste of money for the business. More importantly, Joe may call in to request information on a product he already owns, but the call center rep may not be able to find his record. Ultimately, Joe can get disillusioned by the process, and the website will lose an outstanding customer.
The “five faces” of Joe Smith isn’t just a matter of excess costs or reduced lifetime value. Companies are required to remove customers and prospects from call campaigns if they submit their number to Do Not Call lists. Or, for companies in financial services and similar industries, there are many regulations governing who you can – or more accurately, can’t – do business with (criminals, terrorists, fraudsters, etc.). Data quality can have a lasting and severe impact on an organization, but with the right people, processes and technology, you can increase the reliability of customer data and create a more customer-centric organization.
