

Reducing churn with Econometrics - ryancarson
http://ryanleecarson.tumblr.com/post/23942657106/reducing-churn-with-econometrics

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paulgb
From the title I was hoping this would talk a little more about the model
used. At 45 minutes runtime, I'm guessing it's something more interesting than
a basic linear model.

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hermannj314
After taking the Stanford online machine learning course last Fall, I was very
interested to hear the details about the model they used. Magic statistics
apparently.

While it is good to identify users that might be leaving, it does not do any
good if the intervention strategy doesn't impact overall churn.

There is also the other side of the coin. Let's say your model predicts a user
as "extremely unlikely" to quit the site. If that user attempts to leave the
site you may be more inclined to offer them incentives to stay, knowing they
will accept a one-month 50% discount or something like that. Or perhaps their
usage indicates the level of value they derive from the site, and so you can
offer targeted pricing based on what you believe that class of user is willing
to accept. If they user doesn't use the site as much, then maybe you could
offer them an unlisted pricing tier that fits their usage profile.

And this is why I am not a business executive, it just feels like throwing
spaghetti at a wall.

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zzleeper
They probably just used a logit model, as Stata doesn't support fancy ML
algorithms.

What puzzles me though, is that I don't see ANY reason for using alal of these
tools together (Stata, R, SAS, etc.) and the code taking one hour to run in a
big computer.

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xpressyoo
Nice, I presume the dependent variable is binary, implying a logit/probit
model? I don't really get why 3 different stat tools were used, ie Stata, R
and SAS... R seems indeed plenty sufficient, or even Stata.

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siavosh
A side comment: My current company sells to enterprises. Out of curiosity,
last year I tried to look at the usage patterns of customers who left (and
stayed). I had a simple hypothesis: those who didn't use the software
regularly were the most likely to leave. Surprisingly, this had no
correlation. It was another reminder, that in enterprise sales, low or high
usage patterns are sometimes not a predictor of customer retention. It
obviously depends on the industry, but sometimes it has as much to do with
internal politics, salesmanship, and other things that are simply outside the
scope of your code.

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TamDenholm
Ryan, Just a note but I'm assuming you're going to take action on the emails
you send to users that are likely to cancel rather than just having the act of
the email be the deterrent to cancel.

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fsdfsdfsdfsdfs
What's the parolhar verb?

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ryancarson
Pardon?

