Data-based customer interaction via real-time triggers


The demand for a unique customer experience and the need for a personalized customer approach are constantly increasing. This is applicable in all industries: Customers expect to be addressed at the right moment via the right channel with the appropriate message. 

In order for such an individual interaction to succeed, it is necessary to precisely understand the available data on customer behavior in order to reach customers with a relevant approach.  

In practice, this means that the use of triggers, so-called event triggers, offers the possibility of reacting automatically to activities in customer behavior or even events in order to create a unique customer interaction and a genuine customer experience.  

Using the defined triggers, emails or push notifications can be triggered based on real-time user behavior, for example, if a credit card has not been used within the last 30 days since the contract was signed. The marketing engine reacts in real time to the rule-based event triggers with the automatic playout of dynamically generated content via email or other lead management measures (SMS, app, push notification, call center).  

Simply put, customers can be reached individually via automatically triggered messages through predefined triggers that are activated by their behavior. This not only creates a value-added offering, but also enhances the customer experience.  

The prerequisites for a desired customer interaction are the definition of real-time cases that need to be triggered and a corresponding database about the customers and their behavior. An up-to-date database forms the basis for interacting with customers in real time. This also ensures that the defined target group remains dynamic and the defined marketing campaigns can be played out continuously.  

So, what is the workflow in the Marketing Engine system and how are triggers defined? 


  1. First, the existing database is integrated into the marketing engine via the data parameters of the customers.  
  2. Then, the dynamic target group is defined by the available data parameters and the rule-based triggers for the campaign are determined.  
  3. This means that it is determined here which target group gets which content displayed at which event/trigger.  
  4. The data parameters either come from the integrated data source or it is possible to access additionally generated parameters/scores. These are generated based on the available data points using machine learning algorithms, such as activity scores.  
  5. By determining exclusion criteria, customers can be explicitly excluded from the approach, e.g. those who have already been approached as part of another campaign or who have not given their opt-in consent.  
  6. clear targets can be defined to measure the success of the campaign, e.g. whether a link was clicked or a purchase was made in a particular category.  
  7. Finally, the playout channel (choice between email, SMS, app, push notification, call center), the maximum target group size and the type of content playout are determined. In the case of content playout, an algorithmically optimized playout (using Bayesian Bandit) can be included, whereby the playout is optimized to the extent that the appropriate content variation is played out to the respective person.  


Exemplary Use Cases 

Use Case 1: Overdraft on credit card Automatically triggered recommendation for installment loan / debit card  

Use Case 2:  Inactive use of credit card within the last 30 days Automatically triggered recommendation to use credit card  

Use Case 3: Customer holds a current account & 18th birthday Automatically triggered recommendation to take out a credit card

Added value through the use of real-time triggers

  • Increase customer interaction 
  • Efficient customer approach 
  • Strengthening customer loyalty 
  • Incentivizing customer activity  
  • Increasing conversion rate 
  • Highlighting visibility as a provider 
  • Time and cost savings through automation 
  • Smart handling of large amounts of data  

By establishing a marketing strategy that incorporates real-time triggers, a value-added customer experience can be created for customers.  In addition, significant cost and time savings can be generated on the supplier side through automation and smart data management.