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Understanding the Basics
Marketers often merge several different lists together to create a mailing list for a particular campaign. Problems arise when the same people, appearing in separate lists by somewhat different names, show up in the merged list as more than one person. Understanding a few of the merge purge basics will help you to communicate with your datahouse. This will help them execute an effective merge purge for you.Input Lists
Input lists are all the lists that are being combined and which will then need to undergo merge purge. These lists may be rented or traded, or they may consist of people who have asked that they not receive solicitations (referred to as the "do not promote" or DNP list or suppression list). Input lists can also consist of house lists.House Lists
House lists, owned in-house by the mailer, often consist of multiple lists. They usually consist of current and former customers, as well as internal DNP lists.Change of Address
Both Canada Post and Cornerstone provide data processes that identify recent movers and update addresses. By comparing before and after addresses, these processes prevent costly returns by identifying duplicate recipients at two different addresses and removing the entry with the old address.Finding Duplicates
Finding duplicates can be tricky. For one thing, addresses can look very different and still be deliverable. A single recipient, for example, might be listed as living at 123 Main Street, Toronto, or 123 Main Street, PO Box 22, Toronto, or even 123 Main Street, Don Mills. Catching these variations in data requires software that can standardize all the data as much as possible. While there are established methods of performing merge purge, different datahouses employ different techniques. Cornerstone, for example, has developed sophisticated proprietary tools and programs that examine all components of an address. These methods uncover every considerable clue to determine whether or not any name is a duplicate.Reports
Merge purge reports identify such key metrics as gross names in (the total number of names you started with) and net names out (the number of unique names you will send to a mailing house).Reports also identify inter-file records (duplicates between lists) and intra-file records (duplicates within lists).
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