When you use a contact database with your marketing software, bad data isn’t just bad, it’s the worst. In the race to deliver more personalized experiences, more complicated technology that interacts with your customer database can lead to some unexpected results.
What are some problems marketers experience that are caused by bad data?
From the bounces to the possibility of ESP blacklisting for marketing automation, to the simple embarrassment of sending an email to ExampleContactFirstName, data inaccuracy can hurt your marketing efforts, reputation and bottom line.
It’s not just in email marketing, it’s in how we handle our personal data, too. Who hasn’t taken an ill-advised duck-face selfie outside a restaurant, only to have Google upload it to the pictures of that restaurant on Google reviews? Not this guy, that’s for sure, why are you asking? The fact is our phones access and track more and more data about who we are and how we engage with the world. More data means a greater chance of erroneous, bad, or potentially embarrassing data escaping to places where you don’t control it.
How do I find the sources of my bad data?
Sources of bad data are going to depend on what data is important to you. But for most marketers, media sellers, agency new business directors and brand managers, buying a contact list can be a fantastic way to create a big list quickly. It can also be the fastest way to find your company on an email service provider blacklist.
Purchased lists have disadvantages of potentially not being specific to your industry, potentially not recent and potentially not accurate. Understand which fields in the list are important to you. For some that’s going to be name, phone number and email address, and for other people it will be more, like company and role or other fields. Determining which fields are “must have” for good data, and which are “nice to have” for accuracy can help you prioritize your efforts to assess and repair the data tables.
What can I do to fix bad data?
The first step to fixing your bad data is to find it, and that’s no easy task. At Winmo we call brands and agencies to confirm and validate every name in our database every 120 days. Yes, it’s a long and costly practice, but we think you prefer accurate email addresses over ones that are going to bounce and get you into trouble.
If you worry that your database might have bad email addresses, run it through an email validation tool like hunter.io, MailTester or NeverBounce – or better yet, use a combination of them. Emails that are returned as potentially invalid can be moved to a different bucket and tested in a second tool.
Validating email addresses in your marketing automation software is a great place to start, but what about phone number lists? The good news is that there are similar tools that can help you eliminate errors in those lists, too., and they’re easy to Google.
Names, job roles and business addresses get more tricky, but on an individual basis that data can be found in LinkedIn or in Google maps or on company websites. If you’re serious about cleaning up your lists, taking some time every day to verify these details will ensure you’re reaching out to the right person, and you’ll also find career moves and promotions that are ready-made reasons to reach out to prospects with a congratulatory message.
If all that seems like a lot of work, it is. You should get your free trial of Winmo and see how much time and effort you can save.
How can I make sure I don’t overwrite good data with bad?
If you get your data from a third party, or if your CMS uses integrations that upload datafiles from other sources, there’s a small risk that fields you’ve combed through and corrected or quarantined are going to be overwritten with bad data. To guard against this you should clean your new data before you add it to your database.
There are two ways your data can be bad. The first is that the quality is off – that it’s got inaccuracies in it, and you don’t want to add those records to your good data. The second problem may be that the type of data in each field may not match the actual data. In other words, make sure your phone numbers are all correctly formatted and in the phone number field.
Always use data vendors you trust, and use several sources if you can – that way you can compare one file against another and cross-check any discrepancies. If you find duplicate or mismatched data, remove it as part of your validation process.
Data management is serious business, but staying on top of the records you add to your database will mean your email lists retain their value, even as they decay and are refreshed with new data files.