In the lecture/blog all the events are described as a single point in time. How can i go about modelling an event which has a duration? The only thing i can think of is separately model the start and stop time, but i'm not sure the model would be able to learn that the start-time and stop-time belong to each other.
One other thing i was thinking about is to figure out if anything can be said about the change of influence during the event. What do i mean by that? Well suppose we take the "influence of event on the churn rate" as a curve that happens during the duration of the event. And the area under curve as the total influence. Depending on the type of event the influence can be more biased towards the start or end time of the event.
Suppose your business is a bank. Your customer has saving and you want to predict if your customer keeps a lot (say more than 1000 dollar) saving with your bank, or churns when the customer falls below that. Now your customer has taken a mortgage with your bank. Which is a one time event, but the customer is now in the state "having a mortgage" (with a duration).
Maybe in the first week after taking the mortgage, because the customer is already dealing with financial things it will reconsider where to stall it's savings. So influence/likeliness on churn decision is high.
Maybe in the 2 to 5 years after that it's likely that the customer keeps the savings in your bank because he won't switch mortgage now and thus this feature also keeps the customer satisfied about the bank in general and thus keeps the savings there as well. Now this feature causing this customer to be less likely to churn.
What also makes this more complicated is that several events might now be going on at the same time. Raising the question of how one event going on influences another event. Could that be extracted from the model or would it need to be retrained for such a question (because now we are looking at events influencing each other and not at the churn anymore).
In the lecture/blog all the events are described as a single point in time. How can i go about modelling an event which has a duration? The only thing i can think of is separately model the start and stop time, but i'm not sure the model would be able to learn that the start-time and stop-time belong to each other.
One other thing i was thinking about is to figure out if anything can be said about the change of influence during the event. What do i mean by that? Well suppose we take the "influence of event on the churn rate" as a curve that happens during the duration of the event. And the area under curve as the total influence. Depending on the type of event the influence can be more biased towards the start or end time of the event.
Suppose your business is a bank. Your customer has saving and you want to predict if your customer keeps a lot (say more than 1000 dollar) saving with your bank, or churns when the customer falls below that. Now your customer has taken a mortgage with your bank. Which is a one time event, but the customer is now in the state "having a mortgage" (with a duration).
Maybe in the first week after taking the mortgage, because the customer is already dealing with financial things it will reconsider where to stall it's savings. So influence/likeliness on churn decision is high.
Maybe in the 2 to 5 years after that it's likely that the customer keeps the savings in your bank because he won't switch mortgage now and thus this feature also keeps the customer satisfied about the bank in general and thus keeps the savings there as well. Now this feature causing this customer to be less likely to churn.
What also makes this more complicated is that several events might now be going on at the same time. Raising the question of how one event going on influences another event. Could that be extracted from the model or would it need to be retrained for such a question (because now we are looking at events influencing each other and not at the churn anymore).