In our last blog post, we discussed some of the benefits of data cleansing. Now we’re taking a look at some core data cleansing terminology and techniques.
Understanding different techniques and definitions is an important part of understanding why data cleansing is critical to direct mail marketing campaigns.
Common Data Cleansing Terminology
A suppression list is a list of names and addresses of people that wish to be excluded from communications. Suppression lists vary and can be specific to individual factors as well as more general exclusions.
Deduplication is the removal of duplicate copies of repeating data from data sets.
The use of a specific suppression list to cleanse a database of the details of any people that have passed away.
Involves the identification and removal of the details of people that have moved houses. Data files are either flagged for awareness or immediately removed.
Home Mover Tracing
Home mover tracing involves updating a database with new address information for identified gone-away files.
Is a technique used by the senders of B2B direct mail to remove businesses from their database that have moved or closed down.
More advanced versions work at an individual employee level and also remove the names of people that have moved jobs or passed away from the database.
House suppressions involve using a custom house suppression list for exclusions. These lists are more general and can are amended based on the requirements of a customer.
Mailing Preference Service
The Mailing Preference Service (MPS) is a list people can register to in order to prevent the receipt of personally addressed direct mail from companies of the Direct Marketing Association. The MPS will also take steps to prevent the receipt of direct mail from non-DMA members.
Data Health Check
A data health check is a comprehensive check on the overall quality of a data set. Health checks are usually provided free of charge and are used to identify the incorrect or out of date items within a data set.
Address and Postcode Validation
Involves checking with the Royal Mail Postal Address Finding Database (PAF) to update and remove/amend incorrect addresses from a dataset.
Profiling is the automated processing of personal data for evaluation analysis or prediction. This will usually involve collecting statistics and summaries of the data to help break it down and segment the data.
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