Digital Marketing

Email marketing: how to clean your database to improve ROI

The databases are the foundation on which rests all your email marketing . As good as the content of your emails is, if your database is not well organized you will not get good results, and you may even end up classified as spam.

How to avoid this problem? Let’s see the reasons why databases get “dirty” and how to leave yours as streams of gold to improve your ROI.

Why clean up your database?

If you have been doing email marketing for years , it is common for you to have outdated contacts : email addresses that no longer even exist, invalid data or even people who never wanted to receive your emails. And this can bring you several negative consequences :

  • Email providers have a series of filters and criteria to prevent users from receiving spam . If your emails are marked as spam by more than 0.1% of the recipients or if they have an opening rate of less than 15%, the email provider will identify them as spam and they will never reach the inbox of the emails. recipients. As you can imagine, this can undermine your entire email marketing strategy.
  • As you surely know, the data protection laws at a European level have been tightened in recent years (the famous RGPD) . If your database contains contacts who have not explicitly consented to receiving your messages, you could be in breach of these regulations, and that can lead to serious penalties.
  • Being marked as spam can seriously affect your brand reputation . If users perceive that you send spam, you will have lost their trust, and with it the opportunity to turn them into customers.
  • And lastly, dirty databases also litter your metrics . If your emails do not reach the inbox of the recipients or you have a high bounce rate, it is more difficult to calculate what is the real effectiveness of your email marketing and implement strategies to improve your ROI.

Another frequent problem in databases is poor segmentation and disorganization . For example, there are often problems with formatting data. If you ask users about their job, they will most likely introduce several different options for the same job: “marketing director”, “CMO”, “chief marketing officer” …. If you do not have this type well controlled factor, these users could end up on different mailing lists, when in reality they should be the same. In the long run, this creates a lot of complications to manage your email strategy. So let’s see how to start solving this mess.

How to clean up your email marketing database once and for all

Delete spam emails

The fundamental principle to clean your email marketing database is to ensure that each and every contact on your list is a person who is genuinely interested in receiving your emails. Let’s see what you can do to get it:

  • Identify hard bounces and remove those emails from your list forever . A bounced email is one that never reaches its destination. Within this category, we have soft bounces (email cannot arrive due to a temporary problem, for example, recipient’s inbox is full or server is down) and hard bounces (email address or domain already they do not exist, or the server has blocked shipments of your brand). Generally, hard bounces have no solution, so the best thing you can do is remove those contacts from your database now.
  • It gives subscribers facilities to unsubscribe . Having more subscribers does not contribute absolutely anything if they are not interested in your shipments. What’s more, having uninterested subscribers can even harm you, as they won’t open your emails or even mark you as spam. So it always pays to make it easy for them to unsubscribe with a single click.
  • Search for inactive users and offer them the opportunity to unsubscribe . Normally, your email marketing provider will allow you to see metrics such as the last conversion date, the last form submission or the last open or clicked email. Thus, you will be able to detect the contacts that have not had activity in the last year. My recommendation in these cases is that you confirm if they are still interested in receiving your emails and if not, do not hesitate to delete them from the database.
  • Detects low quality contacts . Sometimes email marketing databases include people who have simply attended an event or fair, “guessed” emails based on the company’s domain and format, or third-party lists. In general, these contacts have not explicitly consented to being part of your database, so it is best to reconfirm their consent or delete them.
  • In some cases, we see that we have serious deliverability problems with a database and we are unable to regain the trust of mail providers . In those cases, you may have to opt for a radical solution: create a new database from scratch and offer existing contacts the possibility to re-subscribe to receive your emails. Logically, with this you risk losing a large percentage of the contacts, but sometimes it can be the cleanest solution.

Organize the data

Now that we have eliminated the people who really do not want to receive our emails, the next step is to put order in the contacts we have left. Let’s see some recommendations to get it:

  • Correctly format inconsistent data . There are several standard form fields that can give us problems: first and last name, phone numbers, postal and email addresses. The idea is to choose a unique format for each of them and unify everything. We also have to be aware to eliminate unnecessary whitespace and unwanted characters.
  • Create dropdowns with closed responses . For cases like the one we have discussed before different names for the same position (CMO, Marketing Director, etc.), the easiest thing to do is to decide on a standard nomenclature and create a drop-down list in the form so that the user can choose the option that best fit your case.
  • Delete duplicate contacts . In some cases, we may have the same person registered multiple times, especially if we have made imports, and it is even possible that this person is receiving our emails in duplicate or triplicate. So to finish, you also have to find and delete the repeated data.

Good practices to keep your database clean

If you have followed the previous steps, you will have a much more efficient database and you will see how your deliverability, openness, clicks and conversions improve , and with them the ROI of your email marketing .

But so that all this work is not in vain, regular maintenance must be done .

First of all, the recommendation is to repeat this cleaning process periodically, for example, once a month. Since you will no longer be dealing with accumulated errors from years, it will take much less time than the first time. And this way you will guarantee that your database is kept in top shape.

Secondly, it is always a good idea to apply a lead scoring strategy to assess the health of our database and act efficiently.

In short, lead scoring consists of designing and applying an algorithm that evaluates the quality of the contacts in our email marketing database and the moment of the process in which they are found. For example, we can assign points based on their responses to the initial registration form or the actions they take, such as opening an email or clicking. Depending on the score a contact receives, we will have different scenarios:

  • Unqualified leads. Users who have a very low score and who are not interested in us as potential customers at the moment would enter here, so we can eliminate them from the database or include them in a low maintenance list.
  • Marketing qualified leads . These contacts have an average score, so they would be ready to become part of our email marketing flows and receive communications.
  • Leads qualified for sales . The highest-scoring contacts are those who are ready to convert, so the action to take would be to send them personalized offers based on their interests.

In order to manage this entire process, it is essential to have a good marketing automation strategy that allows you to automatically assign scores to contacts and send them to different lists according to their characteristics.

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