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Best Practices for Maintaining Good Contact Data

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Good data hygiene for your contact data is the result of a process—and more important, having people tasked to “own” this process across all stakeholders using this data. Here are some of the common best practices for maintaining good contact data:

Select a USPS-certified validation service

Be sure the validation service you use is CASS-certified. CASS stands for Coding Accuracy Support System and is a rigorous set of tests administered by the USPS to verify the accuracy and performance of address validation software. Service Objects has provided CASS-certified address services for more than a decade.

Validate addresses at the time of data entry

Install real-time address validation APIs at the point of capture or onboarding of contact data. Data entry errors are a key cause of bad address data, and whether you are using a web form, call center agents, or remote data entry, validating address data in real-time provides the opportunity to check and correct address in the moment.

By integrating address validation directly into the data entry functions of your marketing automation, CRM or ERP platform, the person inputting these addresses can be presented with corrections and suggestions at the time and confirm and/or change this data in real time. Use strong and specific error messaging on your web forms, to help users make more targeted corrections: for example, error messaging that specifically says:

“123 Main Street — the street number is out of the range of known street.”

Revalidate regularly and at time of use

If you capture addresses correctly, they are accurate— on that day. But given the rate of data decay as people move, change jobs and addresses, and come and go from the workforce, it is essential to clean and purge your marketing database on a regular basis, as well as before every marketing or customer contact campaign. Sources say that up to 25 percent of your contact data goes bad over the course of a year, given the volume of address changes and new addresses filed every day.

This step is particularly important if your address data links to contact data in other channels, such as telephone, text messaging or email. Sending unwanted marketing activity to contacts that have changed can cause severe penalties for non-compliance with laws such as the US Telephone Consumer Privacy Act (TCPA), the CAN-SPAM act for email, and the European Union’s recent General Data Protection Regulation (GDPR).

Use a failover service for bad addresses

If an address fails the standard address validation process, all is not lost. There are secondary validation services that focus on resolving and correcting difficult, messy addresses, often using clues from other data points such as phone, email and IP location data. Using this type of service as a failover for hard to resolve addresses enables you to recover them, preserving valuable leads and customer contacts.

Assign data stewardship

On top of these best practices, it normally makes sense to have a central point of responsibility for contact data quality within your organization. While in years past businesses simply accepted a certain amount of bad contact data, or cleaned up this data on a periodic basis.

In today’s competitive environment—and in particular, the risk of compliance penalties from new data privacy laws—now make this a function that requires oversight. Many larger firms now have formal data governance functions, including C-level managers such as a Chief Data Officer (CDO). Regardless of size, every business should ensure there is specific ownership for data hygiene responsibilities.

Want to know more?

Our data quality specialists are always happy to share ways that address quality can improve your business, feel free to contact us.

If you are interested in testing our US, Canada and International Address Validation products, you can sign up for a free trial key with 500 free transactions.

This article is an excerpt from our free whitepaper, The ROI of Address Validation, which we invite you to download.