We all know what bad marketing data looks like. We’ve seen it. We’ve bought it (though we’ll never admit it). We’ve uploaded it into our CRMs. However, what most of us don’t realize is that bad data—whether purchased, gathered, or stored in your internal database—costs companies billions every year in wasted resources and productivity. Research from the Data Warehouse Institute estimates that data quality problems cost U.S. businesses over $600 billion a year.
Despite this, most marketers collect millions of data about their customers — data that decay rapidly, and in turn waste a lot of time and money because marketers can’t figure out how to be data-driven.
That’s the bad news.
The good news is that they’re not alone. More and more marketers across America are realizing that their data is pretty lousy.
According to research from SiriusDecisions,
- 25% of the average B2B database is inaccurate
- 60% of companies surveyed had an overall data health scale of, “unreliable”
- 80% of companies have “risky” phone contact records
Data decay is primarily the result of employee turnover, which is accelerated as people change jobs, titles or responsibilities. According to the U.S. Department of Labor there is a 30-40% annual turnover rate in corporate America.
“The problem of data decay is that it’s faster than it’s ever been.” – Sam Zales, ZoomInfo
Dirty data represents a threat to any well-crafted marketing campaign. It undermines the heart of modern marketing and causes marketers to make poor decisions that are based around strings of inaccurate email addresses and phone numbers.
“Dirty data is the silent killer of marketing campaigns. It makes you look bad, depresses the impact of great content and offers, and can put your brand, reputation, and domain at risk.” – Matt Heinz, Heinz Marketing
Marketing automation is often the first at risk when it comes to outdated data. According to Experian’s Data Quality research, 78 percent of organizations have experienced email deliverability problems in the last 12 months, and 28 percent of those companies have lost revenue as a direct result of emails not getting through.
Source: Experian Data Quality Research 2015
When marketers rely on decaying data to fuel their campaigns, they risk bouncing emails, wasting time, and damaging their reputation.
Dirty data also takes a toll on online advertising. Without accurate data and targeted segmentation, advertisers can inadvertently serve up expensive ads to the wrong audience, which often results in little to no ROI.
A lot of CMOs want to hire a data scientist or a data consultant to help them find meaning in their CRM data, but applying data science to lousy data is just as hard as marketing and selling with lousy data. If you wouldn’t run a cold calling campaign on a dataset, you don’t want to apply data science to it.
Keeping your data clean enough to launch a data science initiative requires tons of legwork – especially when you don’t know which data move the needle for your business. However, there’s a way to gain insights from new data without importing more data into your CRM or building subpar models to race the data decay rate. Through a process called data integration, data engineers can match two existing datasets so you can access external data, such as Facebook presence or online review ratings, without buying a ton of new data and dedicating resources to its maintenance.
While data integration seems ideal, it’s one of the most difficult problems in data science. Theoretically, you could undertake a data integration project on your own. Realistically, you’d waste a lot of time trying to match datasets that may or may not ever matter to your bottom line.
Instead, we need to change the way we approach data quality problems. To see digital marketing success in the next 12 months, organizations will need to assess their data, identify and build a robust MarTech strategy, and invest in vendors that house proprietary datasets.
