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07-06-2016 11:28 AM

Hi,

I am trying to predict an interval target (customer spend in $) using GLM in SAS EM 13.2. I have 80K observations and 300 variables.

The problem that I have with the final model is that the minimum prediction for the target is around $50 whereas 11K of customers have spent less than $50 in the training dataset and 1% of customers have spent less than $1.

Any thoughts on why is this happening or on how to fix it?

The probability distribution that I have used in the GLM model is Gamma with a Log link function. I have also tried other prob. distributions as well as link functions like Tweedie or invert gaussian but Gamma with a log link function produced the smallest ASE. The distribution of Target variable is highly skewed on the right as there are plenty of customers spending lower amounts and only a few spending more than $1000.

Any thoughts are highly appreciated,

Thanks,

Sam

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07-07-2016 08:08 PM

Sometimes you are dealing with two different sets of underlying drivers, so something that might work is to see if you can identify those who spend less than $50 using a binary regression model first. If you can predict those (meaning you are getting a strong model), simply run the binary model first, and then run a separate GLM for each. I'll bet you can see which variables are different by comparing the GLM results.

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07-11-2016 01:26 PM

Thank you JBerry,

I really liked your idea but I am still confused that why GLM is unable to predict those small spends even without segmenting the whole population.

I have already segmented the population based on the credit limit of customers: CL le $500 and CL g $500.

Thanks,

Sam