Transactional data allows institutions to offer personalized recommendations based on each customer’s behavior.
Let's take a bank as an example. If transactional data shows that a customer who always pays their bills on time has started to delay payment, they can be offered a personal loan with lower rates for this period of default , which makes it easier to organize their finances.
Another possible scenario: if a customer frequently uses their estonia mobile database card limit or makes large installment payments, the institution may suggest a line of credit with higher limits or more advantageous installment terms.
In this way, it is possible to create solutions to meet the specific needs of each client and also improve their experiences with the institution.
Increasing Customer Lifetime Value (LTV)
The use of transactional data is a key point in increasing customer lifetime value (LTV) , especially for credit services.
By identifying behaviors that lead to greater engagement, such as recurring use of credit, your institution can propose solutions that expand the relationship.
For example: a customer who frequently goes to the bank for small loans may be encouraged to take out a line of credit with a higher limit and better conditions.
Additionally, if the data indicates that the customer is close to paying off a loan, the bank may offer new credit options, such as a vehicle or home loan, in order to keep the customer active. In addition to maximizing the value generated over time .