I am trying to understand the strength of impact of variables on customer churn (attrition). I want to predict the probability that a customer will churn after time period t (after 1 months, after 3 months) from start date of service.

My intention is not to predict how many customers will churn in next month. Instead, I want predict outcome given a customer start date and other variable. I need to know the probability of churn after one month from start date (customer will stay for one month before churn). Can I use linear regression or survival analysis methods? Explaining the predictive ability of variables is also important to me.

My data looks like this, i have several independent variables. date format is mm/dd/yyyy

id  start_date var1 var2   end_date
1   1/1/2018    30   2     2/15/2018
2   1/24/2018   5    9     2/10/2018
3   3/2/2018    10   3     4/26/2018

customer 1 and 3 stayed with us for more than 1 months. whereas customer 2 churned before spending one month with us. start date and end date can be anything in last 2 years. my target is customer will spend at least one month in our service.


closed as unclear what you're asking by Michael Chernick, Carl, kjetil b halvorsen, mdewey, Jeremy Miles Sep 12 '18 at 5:04

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  • $\begingroup$ Without presenting data, it is hard to offer an opinion. Would you please present some data? $\endgroup$ – Carl Sep 9 '18 at 0:23
  • $\begingroup$ I have updated the questions with some sample data. $\endgroup$ – Gowtham M Sep 9 '18 at 1:29
  • $\begingroup$ Consider making a churned column with 0 (no churn) and 1 (churned). If the customer churns, does the date then represent the date of churning? What are var1 and var2? If you want to make a physical argument then we need to know what these variables represent. Put in enough data for us to make sense of it, (lots more). $\endgroup$ – Carl Sep 9 '18 at 2:21
  • $\begingroup$ encode 0 and 1 and build a classification model like logistic regression? $\endgroup$ – Gowtham M Sep 9 '18 at 2:24
  • $\begingroup$ Don't know yet. Put in more info. $\endgroup$ – Carl Sep 9 '18 at 2:27

If you have the end-date for all customers, then as far as I can see, you don't have censoring. Hence, you could treat the time a continuous variable (possibly consider a transformation, such as the log if it has a non-symmetric distribution), and use a linear regression model.


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