8 Steps to Better Customer IdentificationThe first step in retaining profitable customers is to know who they are
By David Cameron ALLOW FOR COUNTRY-BASED BUSINESS RULESMarketers must account for national name and address conventions when identifying customers. In New York City, the address "20 5th Avenue 203" probably means house number 20 on 5th Avenue, unit 203. In Holland, "Vanhelsdingenlaan 16" means house number 16 on Vanhelsdingenlaan. Such varied use of numerics after street names requires different business rules to ensure you match the correct fields. In Spain, multiple middle names are common. In Quebec, women routinely retain their maiden names. In Britain, first initials replace first names in most instances. Without country-by-country adjustments to the matching rules, marketers can quickly and extensively introduce errors into the system and corrupt customer identification. MINIMIZE TOTAL ERRORIn traditional merge-purge processes, the goal is to minimize undermatching. What is undermatching? Here's an example: Two identical records fail to match only because one has an apartment number and one doesn't. One common way to fix this error is to remove the requirement that apartment numbers match, which prevents duplicate mail from being sent to the same individual. What happens, unfortunately, is that matching rules are biased to minimize undermatching at the expense of overmatching, which results in erroneous matches of completely different records. But pushing too far to fix one problem can inadvertently cause another. "Fixing" an undermatch causes too much overmatch in other areas, such as when the absence of an apartment number keeps legitimately different records separate from one another. Most often marketers never notice that they have actually increased error in an effort to reduce it. By adjusting rules so that overmatching and undermatching are treated equally, marketers can reduce total error. INCLUDE DATA QUALITY IN THE PROCESSMost errors in matching are not caused by inadequate business rules. In fact, confusion in identifying address elements causes most matching errors. If you identify a house number incorrectly, you can't use it in a match. This result is especially true when cleaning up business names. An important step is removing common words both universally (for example, "the," "a," "an", and so on) and geographically (for instance, "Bay State" in Massachusetts names, or the name of the street from the business name, like 31st Street Antiques). These matching errors are data quality issues. You also need to flag records based on common words. For example, key words like Corp., Inc., GmBh, SA, and so forth, connote corporations, whose names you can't use elsewhere. The absence of this suffix (for example, Town Pizza, which occurs in virtually every town in America) is also significant. Other flags to set and account for include common words flags (where a name consists of all common words, such as "Boston Consulting Group") and franchise flags (McDonalds, Burger King, Pizza Hut, and so on). These flags play a crucial role in the match. Also, creating a separate copy of the address purely for matching purposes is extremely important. For instance, you must not change Beacon Hill to Boston for a mailing because it often angers customers, but for matching reasons, you need to substitute the true city for this vanity reference. Incorporating data quality into the matching process answers all these issues and dramatically improves accuracy. IMPLEMENT BOTH CALLABLE AND BATCH-RULE EXECUTIONThis practice is critical to realtime relationship identification and successful one-to-one and permission marketing on the Internet. Basically, marketers must have the capability of running matching rules on demand or periodically. Most batch processes run periodically to clean up duplicates in customer databases or incorporate new activity into the customer view. Although this step is necessary, equally important are identifying individuals in real time while you interact with them and linking them to the total customer view in order to establish a dialog. Customer identification must be done consistently across all processes and touch customer points, regardless of platform. Technically, this process requires that your matching rules are available in library form so that you can match one record at a time into a database - also known as callable matching. You should use the same business rules in many-to-many batch matching processes. Most systems have inconsistent business rules for callable and batch matching and inconsistent rules across different platforms. Such discrepancy means it is impossible to consistently match data in both real time and during periodic updates, which prevents the establishment of good customer dialogs. IMPLEMENT A TIERED RULE SYSTEMThe most effective matching systems are multitiered and involve multiple points of control. The content of data fields is the first point of control, such as data quality. The second point of control is which fields you use - for example, name and address; name and email; name, address, and email. It should be possible to choose any set of fields for matching. The third point of control is the thresholds for a match when comparing any one field. For instance, comparing two email addresses requires an exact match, but when comparing two last names, one or two errors is acceptable. The fourth point of control is the combination of matches required to associate two records. For example, if the first and last name are exact, but the city is totally different while the street address is close, that might be considered a match. However, if errors occur in the first and last name, the city might increase in importance to overcome those errors. These complex points of control ensure that you can implement sophisticated business rules to accommodate special situations. In summary, the realtime requirements of Internet marketing and the rise of global marketing have forced significant changes on traditional customer identification systems. Marketers need to retrain themselves in order to achieve scalability and extensibility, forethought, and a well-designed architecture. Reliance on traditional methods will result in an inability to cross from the present generation of marketing science to the next. DAVID CAMERON [david.cameron@wheelhouse.com] is vice president of data integration and analytics for Wheelhouse Corp., a Burlington, Mass.-based marketing infrastructure services provider. Cameron is a frequent consultant, speaker, and publisher of topics in this field. RESOURCES Related Articles on IntelligentEnterprise.com: "The Model Customer," January 30, 2001 "I Buy, Therefore I Am," April 28, 2000
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