Lists: Double Vision
Perhaps you are processing a merge/purge for an upcoming mailer. At the same time, you have an ongoing email prospecting campaign and telemarketing reactivation program running. Maybe it would be most effective to coordinate all three campaigns to avoid duplication of effort and even possible damage to your business reputation. Ever wonder how people view inconsistent or overlapping messages? I’d expect not very well.
For many marketers, this is a familiar story. Direct mail, email messaging and telemarketing each have a legitimate place in your marketing plans. Sooner or later, you find yourself searching for strategies to merge duplicates between online and offline data sources.
Searching for Duplicates
The first step is recognizing that duplicates must be linked between online and offline data. What does this mean? Finding email lists that also contain postal addresses or phone numbers can provide the common contact data points that allow duplicate identification. While not possible with every list, the trick is selecting data with a high population of email, telephone and postal information.
Apply the same rules to your internal customer and prospecting lists. Don’t skimp on your Web forms, surveys, data acquisitions and exchanges, or even on simple key punching, by excluding email address, phone number or postal address fields from them. To complete internal files missing key components, search out a quality data provider who can append the missing information. As much as appending email addresses and phone numbers is a common practice, so is reverse appending the postal address based on the same email address or phone number.
After your lists are selected, running a merge/purge using only basic postal address match logic may not yield adequate results. A similar mistake would be using just an email address or a phone number to identify duplicates.
A better approach is to search for duplicates on any combination of the contact name plus email address, phone number or postal address. This way, any two email address matches with different postal addresses are merged. Another example duplicate match would be to match any records with the same contact name and phone number, but with different email addresses. Using this logic, each of the records in the chart (see the mediaplayer at right) are merged.