Unfortunately, data quality is a boring topic. No new CMO has ever joined a company and said, “First, let’s perform a merge/purge on our account and contact records, standardize the fields, and enrich the records.” (OK, I’m being hyperbolic, there may have been a few). No, they want a shiny new marketing automation platform, new branding, and an advertising campaign that gets the company noticed.

Sadly, there is little glory in improving your marketing database — unless, of course, you want to improve your lead nurturing, scoring, segmentation, routing, and sales ready lead quality.

Quality is generally seen as a cost center but it can just as easily be viewed as a cost saver. Bad quality erodes your marketing effectiveness, hurts your brand, kicks the knees out from under your nascent big data experiments, and demoralizes sales reps. A bad company or contact record is like a virus propagated from system to system raising the cost over time.

Furthermore, how can you think about predictive analytics when your databases are rife with bad, incomplete, and out of date records?

Bad data isn’t simply a mistyped address. It’s also:

• Missing lead firmographics making it difficult to nurture, score, route, and qualify leads.
• Invalid emails that hurt your deliverability scores and decrease the likelihood your messages will be delivered to inboxes instead of spam folders.
• Junk fields on web forms because the individual didn’t want to fill out a dozen fields to read your whitepaper.
• Large gaps in your segmentation analysis labeled UNKNOWN.
• Hosting costs for storing out of date and duplicate data.
• Leads with missing linkage that were held for nurture because the marketing automation system didn’t know the location was a subsidiary of a Fortune 500 company.
• Poor marketing messaging and targeting that tell the recipient that you know nothing about their business, job function, industry, or company size.

Finally, bad lead quality incentivizes sales reps to ignore leads because marketing never seems to send the “Glengarry” leads. Instead, they become demoralized as they call invalid phone numbers or find that the contact “doesn’t work here anymore”.

Henry Schuck, CEO of DiscoverOrg, describes the situation well:

Sifting through crappy leads as a sales person is incredibly demoralizing. Their commission – which often translates into their ability to save for their family’s future, have disposable income or cover their mortgage and car payments – depends on them being able to close business. Their ability to close business, in turn, depends solely on their ability to find, set appointments with, and CLOSE new opportunities. If the leads provided by your company will not help them do that – how does that feel? They just moved companies to come work for you and their future is uncertain, at best.

So look at data quality holistically. Address it at the front end in your call centers and web forms and then enrich and maintain your database over time. As contact records decay at a 25% rate per annum you need to view data quality as an ongoing process, not simply an annual refresh (which is more than many companies even do).

So by flipping your perspective, it is easy to find myriad tangible benefits which justify the cost of data quality programs. It may not lead to glory, but by recognizing the distributed costs of bad data and then remediating them, you can generate significant ROI.

Photo Credit: Data Hygiene report from Dun & Bradstreet NetProspex Workbench