The first column in this series described the most basic type of customer matching software, merge/purge systems. These systems parse incoming addresses into elements such as first name, last name, house number, street name, city, state and postal code. They then standardize these elements, correcting for variations such as misspellings, nicknames and alternate place names. Finally, they compare the elements in pairs of records, calculate a similarity score and flag as matches any pair scoring above a user-specified level.

Merge/purge systems are relatively fast, cheap and easy to set up. But applying the same scoring formula to all records inherently fails to take into account significant differences in particular situations. For example, matching an uncommon last name should count for more than matching a common one. The second class of customer matching software is able to take such differences into account.

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