The customer is always right. Their data, on the other hand…

Billions of mailpieces move around the country every year, mostly facilitated by high-volume mail-processing companies serving corporate clients. If you work for one of those mega-mailers, you’re probably familiar with the scenario in which a customer’s submitted address data – the lifeblood of that mailing job, in terms of its ability to reach its intended recipients – is riddled with errors. Whether the result of common data entry mistakes or simply sitting in an in-house database for too long without attention, those small incremental issues add up to a big problem for the job as a whole. Before you’ve printed a single mailpiece, its data quality is already compromised – leaving you chasing a train that’s left the station.

Industry standard validating and updating technologies can help you cover a lot of ground in this area for your clients (that’s why the USPS requires those check-and-balance tools to be used in order for mailings to receive automation discounts). But incorrect address data becomes harder to correct with age – the national Change of Address database only goes back 48 months, for example – making it smart for the owner of the data to also own some of the data quality responsibility. Like cold and flu prevention, data quality is best accomplished by dealing with problems early.

Besides, waiting to improve address data only just before it is used in a mailing protects that mailing, but not the myriad other uses the client may have previously had for that data. Profiling and analytics, individual customer communications, follow-up efforts – without reliable contact data, it’s all at risk.

If you’re reading this from the mail-processing side of the fence, consider reaching out to your clients and discussing with them how building up their internal data-quality processes can help their own bottom line. (You’ll be saving them money, and saving yourself headaches.) And if you’re paying someone else to handle your own high-volume mailings, remember: good data quality begins at home – and pays big dividends.