Sanitise mailing lists with merge/purge for better CX and OpEx

Sanitise mailing lists with merge/purge for better CX and OpEx

Merge/purge, qualifies as a critical data processing function that needs to be undertaken by a direct marketing business entity.  The staggering costs that goes towards paper and handling costs makes it all the more important to rely on merge/purge.

The process of gathering files together for the purpose of mailing, including elimination of duplicates among the lists has the desired result of creating a list with less number of duplicates, that are a lot more clean.  This process is mandatory as mailers depend on similar lists to set up houseflies. This sometimes has the undesirable fallout in internal hygiene, and direct marketing companies often end up grappling with these problems.

Customer profiles of lists are likely to be similar in a few overlapping businesses, resulting in duplication.  Housefiles are also highly likely to have duplicates when existing customers receive new customer numbers  as a result of the ‘new’ customer status that comes with Web Orders. These are in fact false positives, and merge/purge can help to remove duplicates, creating a set of records that are unique.

Small to mid-sized direct marketers, with the odd larger sized organizations, regularly rely on third-party service providers for processing of data. The services include specialized merge/purge in addition to standard hygiene and database services. Third party vendors who specialise in DSF2 , NCOA and LACS have access to large databases of consumers and businesses and this access offers organizations better options in merge/purge processes.

The mailer’s service bureau has their objectives cut out – to create a unique set of fields/names that will be used for the purpose of dispatching materials that are promotional in nature. One of the objectives are to slash wastage by dispatching just one number of the promotional material to the targeted individual. Yet another objective is to take a granular look at the lists to decode relationships among various lists that become a part of the input for the merge/purge. Names that occur more than once across lists are typically labeled as ‘multi-buyers or multis’ in short. They comprise that part or category of the database that is considered as responsive.

During the process of combining data from multiple sources, elimination of duplicate records will create an unique record, which will feature data that is valuable. Organizations that aim to look at a large collated mailing list to promote services or products find this necessary.  It is important to note that duplicate records will sometimes comprise information that is important and unavailable in the original record, and this creates a situation where this information needs to be saved.  For instance, a duplicate customer record may contain the same name and address, but the original record may have a field with the mail ID of the customer whereas the other may have a field that has the phone number of the customer. The merge/purge process relies on an advanced data-matching technique to generate one single record from all the duplicate records in a way that will retain valuable information while eliminating data that is duplicate.

Contact information data may at times land up from multiple files, and to compound things further, may actually be in formats that are disparate. Word processors used as options complete this tasks will demand the change of the file structure or format. Not only does this consume time, it is error prone. Software specially developed for merge/purge renders such processes more efficient and effective.

Apart from cutting down on the amount of time that goes into these processes, it also results in considerable cost cutting. The compilation of a single standalone data record, carrying all information relevant to a unique customer improves efficiency considerably while eliminating duplicates resulting in better CX and OpEx.  Typos which are basically human errors that increase with repetitive tasks, are identified and eliminated from data through phonetics and algorithms that match data.  True to its name, records are merged from multiple lists, and duplicated data or records are purged which cleans the data set.

Superior software permits users to exercise control over various paramaeters in the merge/purge process. For instance, matching tools exist in software that works on parameters of degrees of similarity, and this parameter is defined by fuzzy logic algorithms. Options exist that permit the user to choose records on the basis of updated data, or to spell out a set of metrics or match codes, which will be the criteria for selection of records.

The merge purge process involves the process of combining multiple databases from different sources – for instance, SQL server, Excel etc.  The process involves three stages – import of data, combining the data and export of the combined data to database formats that are uniform. The algorithms identify matches amongst records, by a process of laying down thresholds of minimum percent matching in fields. Acronyms are identified to look for matches, and syntax parameters are checked to get rid of all that is unwanted, which leads to confusion in eliminating dupes, and libraries are applied to bring about uniformity, which is best seen in first name matching processes.

Information thus collated will then be captured in a new field which offers the user the advantage of receiving one master record. The merge purge software typically retains the source file information in temporary memory, which enables the user to try out different combinations before settling on the best one.

A customer database that has duplicate records results in wastage of a repetitive nature and merge/purge effectively standardizes data by creating a single unique clean file as a database, eliminating dupes despite combining lists from multiple sources. This involves a process of examining and sorting records, looking at threshold levels for dupes and zeroing in on the most suitable record. Superior software will keep the numbers of false positives down, while ensuring that there is no scope for duplication. The completeness of the information in the fields is of utmost importance, and a good software will utilize appropriate tools to look for the most complete address from among similar records. Business activities are typically not the same, unless they happen to be of the same domain and demographic. Therefore, it will be necessary for merge/purge software to offer customization options to users, in addition to reports that offer insights.