What Does Poor Data Quality Cost You?

As you work hard to squeeze the most out of every direct marketing dollar, one of the most important ingredients -- and one that is often overlooked -- is data quality. And, as we market to our customer databases, it is so important that the data that is housed within it is accurate.

In a recent article in BtoBOnline, Bob Orf, President of Data Mentors describes how the cost of direct marketing with dirty data can wreak havoc on already stretched budgets. As Bob points out, "As one-to-one relationship marketing becomes more the norm, databases are expanding at exponential rates. In fact, corporate databases double in size roughly every six to nine months. These two factors alone should be sufficient stimulus for businesses to get their databases in order in a cost-effective manner. The average business database contains a staggering amount—15% to 40%—of bad data. That means roughly one in four pieces of marketing material mailed is worthless."

Yikes! That'll keep you up at night -- 25% of your direct mail is not getting to the intended recipient? I can imagine the C-Levels all over the country clutching their hearts over this statistic. In an economy that demands that every dollar be spent as intelligently as possible, you would think that this would be totally unacceptable at any company.

When you think about it, it isn't that difficult to clean up your customer database. Let's face it -- the widely available data hygiene tools are cheap and readily available in the marketplace. There just isn't a good reason anymore to have dirty data.

And, once you get your data assets cleaned up with these hygiene tools, it's easier and more cost effective than it used to be to keep your data managed effectively. Not only does this save you money, effective data management enables you to touch your customers efficiently. Once you've got this orchestrated, this will positively impact customer satisfaction as well.

The savings realized by effectively fixing your data quality issues can be staggering. Let's look at Orf's example:

Let’s assume a company’s 10 million-record database consists of 15% bad data. The company uses the database to conduct synchronized direct response mail and one-to-one relationship marketing campaigns. It touches the entire customer base quarterly. That’s an annual total of 40 million pieces of printed material at an average unit cost of approximately $1.05 each (labor, material, postage). Shaving 10 points off the bad data percentage would translate into a gross savings exceeding $4 million.

$4 million in savings will make anyone look like a hero in their organization. When you add in the positive impact to customer satisfaction and all of the time and resources saved by implementing a sound data quality strategy, you've hit a home-run!