Using predictive models, we can detect behaviors within the User Life Cycle that indicate whether a user is more or less likely to carry out an action that affects the present and future value that they bring to the organization.
For example, if we detect a behaviour that has historically been adopted by users before abandoning us as a supplier, we can anticipate it and develop a marketing plan to avoid this loss and retain them . This will directly affect the LTV of that particular user, and will be reflected in the aggregate CLTV metric.
Let's imagine that a new user starts visiting our website with a higher than freight forwarders brokers email lists average frequency, and the visit time of a page with a specific product is also higher than average. We can recognize, historically, that users who bought for the first time in our e-commerce had a behaviour based on an increase in the number of visits to our site and in the time spent browsing the page that contained information about the product they ended up buying. Well, the moment we detect a new user with a behaviour pattern equal to that described above, we will be able to act on him and make him act as we want, by buying.
Therefore, we can say that it is this type of marketing actions that take into account the moment in which the user behaves according to a pattern, and the strategic context of the User Life Cycle , which will allow us to increase ROI through highly focused email marketing campaigns.
