Pursuit of the Perfect Exchange

If data is managed properly, the end result can be much more productive for all parties. Even though Jean’s name is spelled two or more ways in the transaction, and her address was off by a few characters, proper data matching with other records used by the data quality management system can recognize that Jean is still the same person to whom the original gift was sent. The clerk will be able to quickly verify that an exchange can be made for the $50 value of the first item, immediately securing a satisfying customer experience.

Also, because the retailer’s corporate inventory and transaction systems can recognize and analyze the true transactional events of that individual customer. The company can record that the sale of $55 worth of merchandise, with $50 arriving via the Web and another $5 from a physical store in Texas. It will also reflect that the original gift that was purchased over the Web is back in inventory.

If such IT systems are unable to recognize Jean as just one customer in this situation, poor data quality will undermine if not reverse the success of follow-on customer relationship management (CRM) initiatives as well. For example, if the Web system never learns that Jean exchanged the first gift purchased by Sara for her, it would logically put her on a list of customers for that gift’s product category, prompting follow-on letters highlighting similar offerings. Those mailings will be counterproductive and a waste of resources for the retailer if Jean neither owns nor wants the original gift.

The Final Analysis

Virtually all of a retailer’s analysis systems are put at risk by corrupted or poor customer data. Those systems, including Point-Of-Sale analysis, operational analysis, analysis of the value provided by customer service, and valuation of the retailer’s total profitability, are totally dependent on accurate data inputs to be able to provide intelligent outputs upon which accurate executive decisions can be made.

These details may seem trivial on the surface. If margins are high, many companies may not see the urgency of keeping their customer data clean and up to date. However as margins remain thin and competition continues to increase, data quality becomes a substantial competitive differentiator and advantage, particularly in a post-holiday season of complicated returns and exchanges. After all, there is no better time to impress and secure a customer then when they are shopping for their most important clientele -- family and friends.

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