The optimum situation for retailers is to have a buy-anywhere, return-anywhere policy that works efficiently between physical stores, catalogue sales and Web operations. No matter what the stated policy is, reality often tells a different story, and the culprit is usually bad data quality about individual customers.

Public Enemy Number One: Human Error

Despite efforts made to accurately enter data, no doubt human error comes into play and needs to be dealt with. For instance, consider a $50 purchase made over the Web for Aunt Jean in Texas by her niece Sara in Maine. The retailer has Sara’s customer identification data already, and she provides basic customer identification information on her aunt for the delivery to take place. Unfortunately, Jean decides that she wants to exchange her gift for a somewhat more expensive item -- $5 more -- in the retailer’s brick and mortar store in Texas, and uses a charge card to pay for the difference.

In theory this approach sounds effective, but what if Sara misspelled her aunt’s last name or got the street address or postal code wrong? What if Jean’s name on her charge card uses her maiden name rather than the new married name she just took a few months back? And what if this national chain also has an old address in a third state still listed for Aunt Jean? Let’s presume that the retailer has tied together its Web and store systems, which most logically do. But do both systems make the prerequisite comparisons and have the built-in intelligence to recognize that Aunt Jean’s the same person making the exchange as the one to whom the gift was sent? If not, what are the implications for the retailer?

Unfortunately, this is not a rare occurrence for large retailers. Despite millions of customer records, the relative quality of that customer data erodes rapidly over time…up to 50% a year by most estimates. In that time, customers will change their address, phone, email address or name, but such information will not be reflected across multiple organizational databases where that information is stored.

If the retailer isn’t applying data quality management adequately, Jean may be in for an unsatisfactory experience when she tries to exchange her gift. Even if the clerk in the store allows her to do the exchange, what are the odds that the store’s inventory control system will realize that this customer only purchased $5.00 worth of merchandise rather than $55? With incompatible data running rampant – not very likely and the risk of subjecting a valued customer to a negative experience is high.