INtelligent Data: Data Hygiene Fixes in a MarTech World

All marketers know from experience that the root of error for most flawed campaigns is bad data. From personalization fails like “Dear Fred” when Fred’s first name was on Suzy’s record. Suzy was all like “Who is Fred? You guys have no idea what you’re doing!” But where did Suzy go? Oh yeah, Suzy is the first name for John’s record. Why? Because the CSV file had commas in the value for address 1, and the marketer didn’t double quote encapsulate the file before importing into the ESP. Lesson learned, but is that knowledge transferred to the next person who may be doing the import next time?

What about email addresses using the wrong syntax anything@anything.anything and the form submitter put “1.com” as their email address to download your eBook. Or worse yet, an almost close enough spelling error like jenny@gmai.com missing the “L”. So close but yet so far, and now it’s unuseful.

What about relational data where there is a one-to-many ratio like product purchases yet it is stored in a single field? Suzy bought three products, including the book Soap Making for Dummies, and now the marketer is trying to target Soap Making for Dummies purchasers and uses a merge field to send a personalized next-likely-purchase email like “Dear Suzy, Since you purchased Soap Making for Dummies here are some suggestions for your next purchase.” Instead, it comes out like “Dear Fred, Since you purchased Soap Making for Dummies | Rainbow Toe Socks | Exploding Kittens Card Game, here are some suggestions for your next purchase.” When the other purchases have zero relevancy to the purchase suggestions, Suzy feels misunderstood and instead of buying, she unsubscribes.

 

To optimize success, data must be clean, accurate, understood and useful. In this MarTech marketing world, if the marketer is more Mar than Tech, that is easier said than executed. If more Tech than Mar, accuracy is there, but usefulness may not be.

Tips for the Mar in MarTech:

• Create (if it doesn’t exist already) a data dictionary of what each field value means for all fields you use for marketing purposes. For instance, obvious fields like First Name everyone understands, but something like Gender where there are values like 1, 2 or 3, you will need to know that 1 means male, 2 means female and 3 means undetermined when you’re trying to use that information.
• If your marketing objectives cannot be met with existing data, consider running your file against a data provider to append additional fields. For instance, if you have a direct mail file, you can match and append an email address for the record at about a 30% match rate.

Tips for the Tech in MarTech:

• Any data collection forms or database manual entry forms should have built-in rules for what data formats are allowed and real-time data field validation to check accuracy. For example, date field formats defined YYYY-MM-DD or MM/DD/YYYY. Validate that alpha fields have alpha characters and numeric fields only have numeric characters. To eliminate spelling errors from skewing your data, create fields with drop-down preformatted options to eliminate spelling and other fat-finger entry errors.
• Validation takes on different forms, but for email address, there are many tools like Fresh Address or BriteVerify that will validate whether the email address supplied is an actual/deliverable email address before marketers attempt to use it. It will save many deliverability headaches as well as ESP sending reputation.
• Duplicate records should be identified, merged or purged monthly or at the very least quarterly. The frequency depends on how often duplicate records are created.