We’ve all done it before...fill in our names as “Anna Banana” with a title of “Miss Universe” to get to a free white paper, webinar or just to see what the next screen is! And, if you have a job like mine, you probably have tools and techniques to identify and, where possible, clean these entries so that your database quality and marketing ability is improved.I was recently doing some spring-cleaning and wanted to share some of the results!Eloqua provides a set of tools to deal with clean data. One of the more useful (and free!) ones is a cloud app called Name Analyzer which helps to identify known bad values and patterns in first name, last name, full name, phone numbers and email addresses.On top of these tools, I also employ a number of other techniques to further cleanse data. Some personal techniques include looking for duplicate values across fields, and improbable letter clusters (asdf, hjkl, zzz, xxx, x, y, xyz,abc). We use a contact washing machine to normalize job titles. Of course, you have to be careful about making assumptions because you don’t want to inadvertently exclude anyone from Hell, Norway.We have found that at at least 14% of form submissions contain obviously fake data. The biggest single source of bad data in this sample set appear to be in the form of spam, which usually contain html links and terms like “Gold”, “Free” and “WoW”.Having bogus data in your database is not great, but it is unavoidable. The good news is that even if a phone number or title is fake, the other information may be fine. The other good news is that tools exist to help marketers deal with these records and in the meantime, it can be a great source of laughs for data professionals! We’ve included some of the funnier entries on our chart. And, hey if that’s the real Slim Shady who is interested in marketing automation, then please stand up.
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