This algorithm enriches the customer profile with data about their engagement level with regard to received email messages, based on open and click rates. It also provides an overview of the total number of contacts in different clusters and how these change over time.

Note: this algorithm is different from the Customer Engagement one, because parameters to determine if a user is new or not, active or not, are automatically calculated.

Description

The open and click rate data is taken from the Contactsend database, without your involvement.

The output is a label that defines the engagement cluster to which the customer belongs. See Clusters for more details.

Customer Engagement input data and output

Clusters

The model computes clusters based on recency and open/click activity:

• Engaged.
  Customers in the database for at least X days/months*, who received regular communications and have recently opened and/or clicked**.
• Active new.
  Customers in the database for at least three months, who received regular communications and have recently opened and/or clicked at least once.
• Interested.
  Customers in the database for at least three months, who received regular communications and have recently opened and/or clicked a few times.
• Dormant.
  Customers in the database for at least three months, who received regular communications and have opened and/or clicked a few times, but not recently.
• Disengaged.
  Customers in the database for at least three months, who received regular communications and had opened and/or clicked many times, but not recently.
• Inactive new.
  Customers in the database for at least three months, who received regular communications but have not been active.
• Inactive.
  Customers in the database for at least three months, who received regular communications but have not opened or clicked.
• Off.
  Customers who have not been contacted recently.

Key:

* This value changes according to the characteristics of the customers in your database. For example:

• Whether you have a high number of new customers in your database or not, when the algorithm is run.

or:

• The frequency with which you send out your email deliveries.
  The algorithm is auto-adaptive, meaning that it may automatically change the parameters to best fit the available customer base.

** This changes according to the behavior of the total customer database population.

Output examples

The output is provided as new fields that are associated with each customer profile.

Example of the output

Tags:

• Aggregated.
• Digital messages.

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