In the first installment of our data spring cleaning series, we explored the concept of dirty data and the red flags that indicate it might be time to do some cleanup. Dirty data might not seem like a huge deal — after all, how big of an impact can a few pieces of inaccurate, incomplete, outdated or duplicated information have in the grand scheme of things?
Well, as it turns out, just like dust can build up over time, so can the effects of dirty data, so much so that it might take a little extra elbow grease to clean up. But, before getting to the actual data cleansing piece of the puzzle, it’s important to understand the impact of dirty data on email marketing campaigns.
Targeting and personalization:
One-to-one marketing is all about using customer data to create customized experiences for your target audience. With inaccurate or outdated data, marketing messages may be directed to the wrong audience, making the message irrelevant, or an attempt at personalization may fail to resonate. Here are a few examples of how this could play out:
Higher bounce rates & deliverability issues:
From an email perspective, data errors can lead to higher bounce rates, meaning those marketing messages aren’t reaching the intended audience. While average bounce rates vary by industry, typically anything over 2% isn’t ideal. Beyond ensuring the right message reaches the right person, the reason for striving for a lower bounce rate is simple: high bounce rates lead to a lower “sender score.” A “sender score” essentially is a rating that indicates how reputable a sender is, and a low score is often a one-way ticket to the spam folder, where your email is either left in purgatory or quickly escorted to the trash. Both of these scenarios are less than ideal for demonstrating a return on investment (ROI) for a client.
Misleading performance metrics resulting in poor decision-making
Inactive, incorrect and duplicate emails can also negatively affect the measurement and optimization of email marketing campaigns. If inactive and incorrect emails aren’t removed, that can inflate the emails sent metric. This can simultaneously reduce the “open reach” metric, which is calculated by taking the total number of emails opened divided by the total number of emails sent. So if the number of sent emails is inflated, it may appear as though the “open reach” was lower than it was had those inactive or incorrect emails been removed.
Duplicate contacts and spam emails cause similar problems when measuring campaign effectiveness. One scenario in which this could occur is if an agency runs an email campaign for a car wash company, aiming to track conversions for a promotional discount offer. If some customers are listed in the database multiple times with slight variations to their emails (e.g. sarah.johnson@email.com and s.johnson@email.com), and this person opens both emails, that can falsely boost engagement rates.
Moreover, if Sarah Johnson decides to redeem the promotional discount multiple times, that could lead to an overestimation of new customer acquisition and distort ROI calculations. Because analytics are often so critical in determining the next communication a customer receives, these inaccurate performance metrics can lead to poor strategic decision-making.
The moral of the story is: Clean up that data routinely! In part three of our data cleaning series, we will look at a case study on the benefits of this practice, demonstrating how it enabled one of our clients to send personalized communications leveraging customer data more reliably.
At Mythic, we specialize in utilizing customer relationship management systems to help brands build lasting, meaningful connections with their target audience. By combining data-driven insights with personalized engagement, we craft tailored marketing strategies that foster loyalty, drive retention and elevate customer lifetime value.
Ready to strengthen your customer relationships? Reach out to us at newbiz@mythic.us to get started.
