Lists: Double Vision
Choosing the Best Records
This raises some questions. When data are merged and different email addresses, phone numbers or postal addresses are found for the same person, which data should be retained?
- Should lists be prioritized based on costs?
- Could some lists have better email addresses while others have higher quality postal addresses?
- Does the last update date for the list, or even the record, make any difference?
- Should data quality filters be applied to postal addresses, email addresses and phone numbers?
The answers are all "yes." Setting up business rules for data retention involves many considerations that require careful planning—list costs, historical ROI, update frequencies, data compilation techniques, data maintenance, NCOA, eCOA, phone number verification and others. Due diligence in researching each data source with list brokers, managers, compilers and owners can pay large dividends.
Also, the converse is true and could provide equally important insight into data quality. If more than one data source provides the same email address, phone number or postal address, there is an increasing likelihood the data is valid. For readers who are merge/purge experts, this is an extension of calculating multi-buyers. Instead of counting the number of times an individual is sourced, count duplicate email addresses, phone numbers or postal addresses from different sources. Naturally, more is better.
Merge Before ... And After!
Removing duplicates, prioritizing lists, applying keycodes and validating data are all common merge/purge practices to carry out before the mailing. As the results come in, some marketers may turn their attention toward the next campaign. Others tend results by tabulating responses by keycodes, URLs or another response tracking. Smart marketers apply the same merge/purge strategies used before the campaign to process the data collected after campaign execution.
Campaign responses may come in the form of website visits, inbound telephone calls or email messages. Communication may cover the range of product inquiries, survey results, sales or even simple questions. Might capturing and merging these responses be beneficial? Could combining these campaign results with the output of your last merge/purge provide valuable insight? Once response data is merged with the output of your last merge/purge, a very linear, cause-and-effect analysis quickly calculates the response ratio per list source. Taking this a step further and using the strategy over time, it becomes possible to calculate response trends per list source, estimate the number of mailings required to generate the first response, and establish the lifetime value of customers.