It is in the nature of many organizations to avoid investing in preventative measures, rather to wait until something bad happens and then take reactive measures to fix the problem. This is often the case with email list cleaning.
As Jim Harris relates on his Obsessive-Compulsive Data Quality blog, “The majority of data quality initiatives are reactive projects launched in the aftermath of an event when poor data quality negatively impacted decision-critical information.”
Most of these efforts fail, says Harris, because they are based on the flawed notion that data quality problems can be permanently fixed by a one-time project rather than a sustained program.
This is particularly true for email lists that are continually undergoing churn. As Infogroup’s “The Email Ecosystem” relates, tolerating dirty data leads to inefficiency and extra costs. It may seem cheaper to let data hygiene remain a low priority, says Infogroup, “but the cost of operating with poor data is potentially much higher in the end.”
There are many studies that seek to quantify the cost of bad data. One of the most commonly cited is the 1-10-100 rule, attributed to Sirius Decisions, which postulates that it takes $1 to verify a record as it is being entered, $10 to cleanse a bad record, and $100 to repair the damage if nothing is done.
Other studies also have found data quality to be a serious and widespread problem, with results showing 2% to 5% or more of an email list containing inaccurate or missing contact data in any given month, and that bad data such as this costs organizations 10% to 20% of their revenue. Dun & Bradstreet studies found that data decays at the rate of 1% to 3% per month, and that poor data quality costs the U.S. economy six hundred billion dollars annually.
A study by LeadJen found that launching a lead-generation program without first conducting data cleaning wastes 27.3% of a sales rep’s time, or 546 hours per year of a full-time sales rep. The study found that 30% of the sales rep’s time was wasted because of invalid contact information, either because of bad data or the contact not being the right fit.
Data quality, deliverability, sender reputation, and success in email marketing are all closely related. As previously reported, email marketers have raised red flags, warning that the negative consequences of undeliverable emails have risen because ISPs have instituted more stringent measures for intercepting and labeling email as spam.
As Ron MacDonald notes on Chief Marketer, “The surest way for an email address to find its way on to an ISP blacklist is to build a track record of recipient complaints or to generate emails that are identified as spam.”
A regular and proactive regimen of email list cleaning is the best approach to maximize deliverability, maintain a strong reputation, and eliminate cost. As Infogroup recommends, “Marketers must commit to continuously maintain excellent hygiene for their own customer and prospect lists.”
Among the most important measures to take, say the experts, is to verify data at point of entry. By validating emails at the sign-up point, you prevent bad data from entering your list and avoid the cost of cleaning up later.
As Infogroup advises, “Start with clean data that feeds the rest of the email campaign, maximizing efficiency and impact.”
The effort to maintain a clean email list will be seen at the bottom line. As LeadJen reported, when it comes to return on investment of lead generation campaigns, “investing in clean and accurate data positively influences both the Return and the Investment.”