Cleaning Up Your Email Marketing Lists
How much is dirty data costing your email marketing efforts? One of the biggest challenges for marketing automation is, without a doubt, the effect that unclean data has on conversion. In the case of email marketing, dirty data can end up wasting a great deal of time and costs, setting your goals back and wasting budgets significantly. When you waste time interacting with subscribers that are no longer in your system, or those that have outdated records, duplicate email addresses, missing information or any other issues, you are essentially taking time away from active subscribers.
Email list hygiene comes down to improving your sender reputation while making sure that your emails do not get sent straight to spam. It is all too easy for data issues to slink into your lists. This can happen at any point during list-building or lead nurturing campaigns. A simple way to keep your data clean is to do regular checks for duplicate, outdated, incorrect or otherwise invalid entries. With that said, it is not always as easy as doing this manually. It is also very important to understand just how much of a knock-on effect dirty data has on your email marketing strategies.
First things first, what exactly do we mean by ‘dirty’ data? This is a broad term that covers duplicate entries, missing, stale or incomplete entries, incorrect data collection strategies and anything else that results in a less than accurate result when tracking campaigns. As most email service providers use open, click, unsubscribe and unread results to identify spammers, having too many emails that are deleted or left unopened without any engagement can very quickly increase your risk of being blacklisted. It goes without saying that this is the last thing you want from any email marketing campaign.
Email list hygiene is important because it helps to prevent valid leads from slipping through the cracks as you waste time on dead ends. Having a proper list hygiene process will help you identify a number of risks, too. This includes send reputation threats such as spam traps and honeypots; inbox delivery threats such as spammers and bots; and even customer conversion threats such as profanity and false usernames.
Email Marketing Hygiene Best Practices
How do you go about getting your email marketing lists looking squeaky clean? For starters, make sure that you follow these best practices for optimal list health.
Avoid bought lists.
As tempting as it may seem to purchase a list that is already established, this can be deadly on many levels. Here in South Africa, there are a number of laws prohibiting practices such as unsolicited email. At the very least, it is considered extremely unethical to purchase email lists. Sending out an email without express permissions is a one way ticket to being blacklisted or having your emails marked as spam. Rented cold lists are also potentially harmful. Another issue with both of these practices is that it increases the odds of dirty data, with many entries being outdated or incorrect.
Sort out your email collection process.
How do you collect email addresses? This can be a bit of a challenge, particularly with South Africa’s anti-spam laws. All new email addresses should be validated. As a general rule of thumb, it is always best to ensure that you have confirmation emails in place to be sure that email information is correct. This has the added bonus of verifying genuine leads and preventing any potential mistakes.
Reduce email bounce rates.
Bounce rates are something that every email marketer deals with at some point or another. These are a warning sign that something has gone wrong at some point in the process. It could be due to the lead’s email address or even the wording of your email subject or body. There are two types of bounce – soft bounce and hard bounce. After a soft bounce, the system may try resending the email until a prefined bounce limit has been reached. With a hard bounce, the majority of systems will suspend an email address that returns a hard bounce to avoid a bad reputation with internet service providers and being marked spam. If you consistently get bounces, it is best to tag with an inactive status so that the address does not get re-imported at a later stage.
Segment your lists according to engagement.
Lead segmentation is a very useful tool for any email marketing campaign. In the case of data cleaning, segmenting on engagement is a good way to ensure that you do use your time wisely. There are a few categories you could use, such as the following:
- Active leads that open and interact with emails.
- Leads that have not engaged with emails in the last 12 months (these can be flagged for re-targeting).
- Leads that have not engaged with emails in the last 1 to 2 years (these can be sent a re-confirmation, and then tagged as dormant if there is no response).
Ideally, try and select a cut-off point for leads that have not engaged in over 2 years. Chances are very high that leads will not re-engage after this point, regardless of whether you send out any further emails or not.
Merge lists carefully.
Proceed with caution when merging lists. This is a prime opportunity to undo any clean-up you may have recently done. There is a big risk of importing duplicate entries and even re-importing leads that have already opted out of your email mailing lists. If and when you do merge, re-apply these hygiene practices to your merged lists so that you are left with no duplicates or issues.
Avoid human error.
The biggest cause of dirty data is often human error. To that end, it is essential that your systems are designed and automated to avoid the risk of human oversight. There are a few ways to do this, including picklists (static field values that require precision); read-only fields (these contain info for marketing or sales, such as comment history, lead source and other back-end info); and clear processes on following procedure when entering data.
Make data hygiene a priority, invest time and effort into regular bi-annual or quarterly clean-ups, take greater care when entering data and you will start to see a difference in your data quality.