Daniel Sims is the Manager of Demand Generation Marketing at Experian. He oversees web/digital and offline campaigns, marketing operations, and lead engagement and qualification as we move to an advanced metric-driven waterfall demand generation model.
Marketing automation tools are a wonderful thing. They are designed to make a marketer’s job infinitely easier, and allow them to execute some truly extraordinary campaigns. However, if the integrated nature of these campaigns is the circulatory network of veins, then email addresses are its blood.
Without valid, deliverable email addresses, marketing campaigns will flounder.
Dan Zarrella at HubSpot conducted some research across 40,000 HubSpot customers that revealed if you reduce your form fields on your ‘Contact Us’ page from four to three, conversions increased by 50%. Who wouldn’t want more conversions on their forms?
The problem is many marketers feel the sales team needs every bit of that information and therefore need every one of those form fields.
"Virtually everything in business today is an undifferentiated commodity, except how a company manages its information. How you manage information determines whether you win or lose."Bill Gates
The lifeblood of email marketing is the quality of email address data; virtually every email campaign metric can be traced back to it. The better the quality of the data, the more likely marketers are to experience higher email open and click through rates, more conversions, and often times, a higher ROI from the expenditure. On the flip side, poor data quality can mean bad things for demand generation.
Although marketing executives all over the world lament the quality of their databases, all recognize that great data quality does not just happen. In fact, according to a recent Experian Data Quality study, on average, organizations believe a quarter of their database is inaccurate.
The financial impact of poor data can be quantified as well. Gartner estimates that businesses waste $8 million on average per year as a direct result of poor data. “The longer incorrect records remain in a database, the greater the financial impact,” says Jonathan Block, SiriusDecisions Senior Director of Research. “This point is illustrated by the 1-10-100 rule: It takes $1 to verify a record as it’s entered, $10 to cleanse and de-dupe it, and $100 if nothing is done, as the ramifications of the mistakes are felt over and over again.”