There are all sorts of reasons that you might have low-quality contact data in your CRM.
For example, Cliptoo sometimes has difficulty tracking the type of email communication that a contact has subscribed for, says Jesse Hendriks. And that can be problematic when you’re trying to stay GDPR-compliant.
“We commonly find that a CRM database becomes messy/dirty from our client’s demand generation campaigns when the sales team aren’t accurately updating the contact data in the right spot and on a regular basis,” says Chak Ng from Alchemise Consulting.
So marketers should do what they can to fix and avoid this situation.
“When we cleaned the CRM database of a multinational vendor organisation in the IT industry,” says consultant Peter Strohkorb, “we found that the sales reps had invented no less than EIGHT different ways of representing the Australian state New South Wales! The CRM field contained variations, such as: NSW, N.S.W., New SW, New South Wales, NSWALES, etc.”
“[T]his particular CRM system interpreted the different vehicle owner database spellings as sovereign states, which made sales pipeline reports very interesting indeed.”
“Once the issue was identified, ideally they would have used a software de-duping tool to correct it. However in the end it was so messy, the company had to employ a bunch of students to manually re-key the data field at considerable expense,” says Strohkorb.
“In hindsight it would have been easy for the CRM in this business to present a predefined choice of States for reps to select from. However in this unfortunate case the vendor had not thought that far ahead until it was too late.”
“Once again, Insycle makes the process of standardizing the way certain data like cities, states, countries, job titles, company departments, etc quite easy. Insycle will automatically determine the number of times a specific value exists in a field and make it easy for you to change it to a standard. For example, if 100 leads or salespeople entered Virginia for their State, but you want it to read, VA, you can quickly update all 100 rows with one update.” added Marguglio.
“I recommend to create a database washing machine,” says Oleg from DevCom, “unifying all the possible field values according to a pre-made naming convention.”
“For example, a database I was cleaning out had different values for ‘Country’ field like ‘United States,’ ‘USA,’ the United States,’ etc. I created an elaborate automation system predicting all the possible errors and unifying them into one value like ‘USA.’”
“Unfortunately, the same elaborate task had to be done for all the other fields and values. Yet, I had a clean and working database at the end of the day.”“Creating a unified naming convention and a washing machine cleaning out the database is a must for large databases if you want to send out proper email campaigns. Otherwise, a lot of contacts may be missing from your mailing list.”
Oleg wasn’t the only marketer who recommended automating your database-cleaning process.
Brian Serocke from Beacons Point had a similar problem: “Multiple data values that aren't standardized for a particular data point (i.e. California, CA, Ca, ca, Cali all listed as possible values for ‘state/region’).”These fields “were not standardized with a drop-down menu on web forms. In addition, offline data sources meant there was no standardization when data was collected.”
“In those instances, we needed to export data to a spreadsheet, manually check the field for multiple values, and edit each field to standardize the value. We would then need to reformat the spreadsheet (depending on the source) to upload successfully to our CRM.”
“We have since utilized marketing automation tools to run workflows that allow us to easily make data values consistent across all of our contacts.”And Beacons Point has seen great results.“We've reduced the time spent maintaining data by 30% by leveraging automation to do the heavy lifting. Plus, it has reduced potential errors and removed the need to task that duty to an employee.
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