Lifecycle engagement: how the engagement of your database contacts develops

This is the second chapter of the new MailUp Data series. In the first article, we talked about sending emails, their timing, and their impact on recipients.

Now, let’s focus on user behavior and how the user’s degree of involvement fluctuates while in the database, from subscription to unsubscription.

In Email Marketing, engagement measures how and to what degree the contacts in a database interact with the email campaigns they receive. A high level of engagement translates into high rates of email openings and link clicks. This is a good prerequisite for optimal conversion rates.

By analyzing the engagement of your contacts, you can either ensure that the content you send is relevant and engaging for your audience or learn that something in your strategy is amiss. Perhaps your subject lines are irrelevant to your email content, or its structure is not so engaging.

The engagement ladder

Essentially, engagement tells you how interested your mailing list contacts are in your messages. You can draw useful conclusions from this analysis, and thus make your email campaigns more performing while improving the loyalty of your contacts.

The contacts of an Email Marketing database fall into one of the following 4 levels of engagement:

• very active
• active
• not so active
• inactive

Cluster-to-cluster shifts can be measured through a monthly timeline for two reasons: to draw conclusions about overall database engagement and to divide your contacts into different groups to be reached with differentiated Email Marketing strategies that leverage different stimuli.

All database contacts have a limited life, from the moment of subscription to unsubscription. It’s both interesting and super useful to evaluate the lifecycle of contacts in terms of engagement and to know the behavior of users while in the database.

We conducted a quantitative and qualitative analysis of user behavior throughout their life span within a database. After analyzing the sample and defining its homogeneity, we measured the engagement rate and its related variations over time based on the number of communications received.

Engagement level over time

First, we analyzed engagement level variations from the subscription of each recipient to their respective mailing list.

The graph clearly details that an initial increase in engagement (from the timeline’s starting point, i.e. subscription) is followed by a more substantial and evident declined interest in communications from users. In fact, we see the value of the median decrease and then level off in the following months. What does this mean? This means that the first months of subscription are decisive to build the customer loyalty of the acquired contacts. In just a few months, a lacking or completely absent engagement strategy can lead to the conversion of a new user into an inactive, and probably irrecoverable, user.

Engagement level based on mailings

Secondly, we analyzed engagement level variation based not on time, but on the number of communications received.

In the following graph, the horizontal axis in the next graph does not represent time intervals. It rather shows individual emails (m01, etc.) that each user has received during their lifecycle.

The sample’s median clearly and progressively decreases until it levels off at the 14th message, showing that nearly half of the entire database becomes “inactive”. On the other hand, the 3rd quartile that corresponds to the most active 25% of the cluster keeps up with the 18th message through relatively high scores (14–13).

Focusing on mailing frequency highlights its importance in determining whether or not a contact is engaged: the greater the distance between one message and another, the greater the disengaged recipients.

Contact mobility in engagement levels

Finally, from one engagement level to another, we analyzed the transitions that users make during their life as contacts and to what extent these transitions transform the database composition over time.

Indeed, databases are characterized by a certain mobility of contacts in the various engagement levels. Meaning that a contact, during its lifecycle, can go from the initial “active” to “inactive” level, then back to being “active” if correctly re-engaged.

The graph below shows the study’s results, clearly indicating how a static database is only apparent. Contacts move under the surface. They continuously shift the weights and results of an email campaign based on how they react to our communications. Therefore, it’s essential to consider the different engagement levels of recipients when defining the targeted mailing.

Results of the study

This analysis of lifecycle engagement has allowed us to find some dynamics which, if harnessed, can help improve the results of an Email Marketing strategy. Acting on a contact’s lifecycle means working to ensure that their involvement stays higher for much longer.

Here are some key findings from this study:

• the first months of subscription are decisive for acquiring the contact’s loyalty: a lacking or absent engagement strategy can lead to the conversion of a new user into an inactive user in just a few months with an impact on deliverability and sender reputation
• sending frequency has a direct relationship with engagement: a greater frequency in mailing communications contrasts the audience’s declined physiological interest; on the other hand, the more time passes between one message and the next, the greater the number of disengaged recipients
• the database only seems static: contacts move under the surface where the weights and results of an email campaign continuously shift based on how they react to our communications
• there’s a continuous shift between “inactive”, “not so active”, and “active” contacts: user mobility belonging to these categories is very high and subject to the stimuli generated by new communications and reactivation campaigns.

The key ideas that we can draw from these results are, on the one hand, that it’s important to consider the various levels of recipient engagement when defining the target of a mailing. On the other hand, a high mailing frequency reduces transitions from one level to another, favoring the stability of the clusters, and the unsubscription of users who aren’t interested in the brand nor its communications and, consequently, the database’s spontaneous cleaning.

• Create a welcome series to engage new members right from the start.
• Expect a high sending frequency in the first months of subscription.
• Constantly monitor your database engagement levels to check for possible anomalies.
• Segment the target of your campaigns based on the level of engagement.
• Start periodic reactivation campaigns to recover inactive contacts that have not been totally lost.
• Encourage those contacts who are no longer interested in your communications to unsubscribe: the unsubscribe button must be present and clearly visible in your email footer.

Think of Email Marketing engagement as an additional piece of information about your contacts. Create segments based on subscriber engagement levels to target specific groups of users. This will help improve the effectiveness of your campaigns and increase the lifetime of your leads.