The Hidden Costs of Dirty Data

Things can get downright messy when marketers have too much dirty data in their customer records. Dirty data—i.e. customer information that’s inaccurate, incomplete, or redundant—can wreak havoc on even the most well-planned, highly orchestrated marketing campaigns.

You know you have dirty data when “it’s in a state in which it simply can’t be trusted,” says Brian Deagan, global business owner, DMP, at IgnitionOne, a digital marketing technology. “When [gleaning] useful insights from the data requires an undue amount of manual intervention, that’s when you have dirty data.”

Dirty data creeps into customer databases in a number of ways; for example, consumers don’t update their information, internal departments don’t share data to create consistent or complete profiles, or third parties don’t quality check their data before providing it to marketers.

And with those persistent challenges, one question comes to the fore: can marketers develop a system to virtually eliminate dirty data? “Absolutely, yes…. With a little due diligence marketers can identify sources of data, know where that information came from, and require transparency from third-party data providers about the source of the information,” says Chris Stark, SVP of product marketing for Grapeshot, a platform that analyzes Web pages to place relevant ads. “Marketers can make the call and [then] determine the source of the problem.” He adds that once marketers know the source of their dirty data, they can simply choose not to use it, or they can determine how to ensure that that source provides clean data.

However, if bad data does make its way into a database, there are several ways marketers can clean it up. Stark and Deagan agree that marketers should focus on three main things: a data strategy, a budget for data hygiene, and the integration of different data sets.

“Obviously, all three of those are important, especially the data strategy,” Deagan says, adding that having a plan or set of rules on how to collect, handle, and validate information will yield clean data and, ultimately, help everyone—from the customers who get more relevant messages to brand marketers who are then more likely to reach their goals. “There’s no doubt the benefit of [having a data strategy] spans several groups within an organization.”

Stark adds that data hygiene and integration should be top priorities for marketers. “The idea that you should allocate some amount of your budget to ensure what you’ve got is actually good is a must,” he says. “It can be hard to determine how much of the budget it should be, but you should have a budget for [data hygiene].”

Stark says that if marketers want to get the most out of their data, they need to integrate data sets: “It’s important [for marketers] to get the most nuanced picture of their customers,” he says. “The more [information] that you can layer, the more refined results.”

A word for direct mail marketers
The problem of dirty data for direct mailers is a unique dilemma, says Kurt Ruppel, marketing services manager at IWCO Direct, a provider of direct mail marketing services. “In the mailing world dirty data is the address information that’s incomplete or has incorrect elements in it,” he says. “Or it’s simply an address that isn’t current, [often stemming from] not keeping up with people that are moving.”

Ruppel warns that without correct addresses the effort to create and send targeted mailers to the right people will simply be wasted. “With mailing lists marketers need to constantly update and run specific processes against the lists to make sure they have accurate, up-to-date addressing.” He suggests using certified software from USPS, such as CASS—or Coding Accuracy Support System—to determine accuracy and precisely match addresses to potential customers. “Develop [clean] lists based on demographics and behaviors of people [who are] likely to be interested in your products or services,” Ruppel says. “You really want to make sure you’re putting your best foot forward with your prospects.”

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