At some point in our careers, we've all had a data mishap. A colleague recently shared a direct mail promotion he received from a high-end jeweler. The headline read, "KEVIN, this Valentine's day, give LESLEY the gift she really wants," along with an image of a beautiful diamond necklace. The only problem—Kevin and Lesley are brother and sister. (And yes, they were horrified at the jeweler's suggestion.) Was the jeweler trying to promote sibling love? Doubtful. More likely: Major. Data. Fail.
Obviously, this example is variable data gone wrong—either mismatched data points, misinterpreted data relationships or just plain bad data. But whatever the reason, with GDPR just around the corner, it's crucial that your data is in order. Understanding who's in your database, as well as the age and viability of each record, is a foundational piece of GDPR prep. Think of it this way: retaining junk data is a liability for you. Why risk costly fines due to keeping questionable records?
What's Lurking in your Data Pool?
Over time, junk records creep into your files and weigh down your performance metrics, create potential marketing disasters and set you up for GDPR problems. Time to scrub the pool! The best way to identify junk data and gain more insight into the composition of your database is by creating a marketable records segmentation. Any groups regularly suppressed should be pulled into this segmentation. What should you be looking for?
Inactive Records: Since GDPR stipulates not retaining data longer than necessary, flag outdated records—or in the absence of a defined expiration date—those that have not opened or clicked on an email or have not visited a webpage in the last 12 months. We’ll try to reactivate these names—more on that topic in a minute.
Disqualified Records: Be on the lookout for trash and disqualified records especially, usually corresponding to a lifecycle stage of trash or disqualified and including names rejected by sales.
Role Accounts: These are email addresses for a specific role that don't have a human associated with them. Under GDPR, such records are not considered "personal data" but since they don't benefit sales, remove them. To do so, include a filter for email that starts with and contains descriptors such as news@ administrator@ unsubscribe@ customerservice@ webmaster@ info@
Junk Domains/Data: Just as the name suggests, these bogus domains include data strings such as "ABC," "XYZ," swear words and email addresses without an @ symbol. Dump the junk!
Undesirable Personas: Examples include students, retirees, and maybe the media. If not a viable lead, they are not worth the potential risk of retaining.
Country Data: Run a query to determine if all records have normalized country information. Flag those that do not or are missing country data altogether.
Opt-In Sources: Is consent GDPR compliant? Do you have proper record-keeping to back that up? Create a separate segmentation based on current compliance status and deploy a whitelisting campaign for records compliant with current EU Directive legislation that may fall short of GDPR standards. Remember, this may include records that have consent, but the consent is dated.
Preserving Potentially Viable Records
OK! You've done some cleaning on your database; now it's time to look at the questionable and non-compliant records to retain as many as possible before GDPR goes into effect. Campaigns you'll want to run sooner rather than later include:
Read the full post on the Perkuto Blog.
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