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Steelers_In_DC
Barite | Level 11

This is my first post here, usually over in Base Programming, I'm hoping a few of the moderators from there see this and answer as I respect their opinions greatly. 

 

I have several years SAS programming experience but am new to statistical analysis / modeling.  I am finding the business people that I need to get the support of don't understand anything but decision trees and regression, really they don't understand regression but they've been beaten into submission and will accept it.  So... from that perspective, as a student I am learning a lot of new techniques, some that are still being developed today.  I wonder how to communicate to people that this is valuable information.  People that I am looking to for mentorship have given up communicating and now dumb down their work to their audience. 

 

I would love to hear perspectives from people that are in this situation, have submitted to it, are working through it, or have conquered it. 

Cheers,

Mark

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SASKiwi
PROC Star

Well I'm not really an expert in this field, but what I would do is focus on outcomes rather than the statistical techniques involved. For example let's say you have developed a customer churn model that can accurately identify customers about to take their business away from your company.

 

Firstly you can prove this by applying it to customers who have already churned to show that it works. Secondly you can put a dollar figure on the benefit of a retention campaign based on your model. If you say reduce churn by 10% that is $x per customer you have saved the business. At the end of  the day high-end analytics has a huge pay off. It is just a matter of proving by doing without getting hung up on the esoteric statistics.

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SASKiwi
PROC Star

Well I'm not really an expert in this field, but what I would do is focus on outcomes rather than the statistical techniques involved. For example let's say you have developed a customer churn model that can accurately identify customers about to take their business away from your company.

 

Firstly you can prove this by applying it to customers who have already churned to show that it works. Secondly you can put a dollar figure on the benefit of a retention campaign based on your model. If you say reduce churn by 10% that is $x per customer you have saved the business. At the end of  the day high-end analytics has a huge pay off. It is just a matter of proving by doing without getting hung up on the esoteric statistics.

Steelers_In_DC
Barite | Level 11

Excellent point, thanks for your response.  Years ago, not knowing what the name of what I developed was at the time I developed a classification model, identifying customers that were going to default on their HELOC.  Granted it was 2008 and everyone was defaulting, instead of a model you could have thrown a dart at a cork bored to make your selection,  but rather than explain the process I ran the model on historical data and showed the results, not the process.  Buy-in was easy to find. 

 

That's the tact I'll take going forward, have a great weekend. 

 

Cheers,

Reeza
Super User

You'll also want to demonstrate why you have confidence in your results. How you verified your results should be something you can explain, so you can communicate the error levels in your predictions as well.

SASKiwi
PROC Star

Glad I was able to help. I forgot to mention that any actions taken to benefit from your model, like preventing churn or default, need to be effective - an attractive customer proposition. This can be tracked by looking at customer behaviour following the campaign. A poor campaign can be worse than doing nothing at all.

 

An example of an attractive customer retention offer would be to give a significant discount and/or lock in a fixed price if the customer was prepared to commit to a 2 year contract instead of just staying month-by-month.

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