This kind of predictive model is actually a more advanced form of churn model.
Basically, in a churn model, we are computing the probability that a given customer moves from the “normal customer state” to the “churned customer state”. Let?s illustrate this with a small graph:
The “” means “Average Customer life-time value” of all the customers inside the segment_s: this value must be given by the business analyst or the marketing team.
The forecasting of the CLV(Customer-Lifetime-Value) of an individual “” that is currently inside the “active state” segment is:
…where is the probability of churn in the next 3 months for the individual ““.
This probability is computed using a predictive model (that is typically built with TIMi).
Here is another chart that explains the more general case:
Once all the different predictive models are computed, there exists inside our analytical ETL tool (Anatella) a small box that directly predict the CLV of each customer. This box is quite simple: For example, the predicted CLV (in a 3 month window) of an individual “” currently inside the state “Low-value-customer” state is:
Datamining vendors are often searching to add new functionalities inside their tool instead of improving the functionalities already provided. Indeed, its very difficult to improve prediction accuracy (i.e. to obtain a higher lift on the TEST SET) and it is a lot easier to provide a new (barely working) functionality like this “CLV estimation system”.