How to predict whether or not a customer will renew I have a dataset of customer contracts that specify a start date and if applicable an end date. Each month a customer is up for renewal. Below is an example of how the data is organized in excel:

ID   Customer Start Date   Customer Drop Date
1    Jan. 2018             Dec. 2018
2    Feb. 2018             July 2018
3    Mar. 2018             


Using the above example, I'm trying to predict whether customer 3 will drop in Jan. 2019, Feb. 2019, Mar. 2019, etc.. Essentially I'm trying to calculate the probability that a customer that's still active will renew their contract for a given month after Dec. 2018. What is the remaining life-time value?

Should I graph the the length of all historic contracts and see what distribution they match? If so how would I apply the distribution to the open contracts?
What you are trying to do is called Churn Prediction. Unfortunately, the dataset you have is not enough to train a model. You need a variety of different features for a proper prediction model.

For example, you need demographic data for the customers, but most important, data related to customer's actions. For example, if you have a mobile network company, you might need features as:


Age
Sex
Country
Daily usage (number of call minutes, SMS or mobile data)
Previous day usage
Mobile package (details about the contract etc)


In general, the more data you have around your problem, the easier would be to build such a model.