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Predictive modelling on non-random data

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Occasional Learner
Posts: 1

Predictive modelling on non-random data

Hi all

I am using predictive modelling to predict which customers to send out email newsletters to. I am using e.g. decision trees, logistic regression etc.

I have some datasets where all the customers (one ID per customer) have received a newsletter, which means I can do a simple random sample based on this newsletter. However, I also have datasets where the newsletter where sent out to only a subset of customers based on a predefined affinity logic. How would you deal with such a dataset? Is it even possible to use it for predictive modelling as it will not have a random representation of people who are interested in the newsletter and people who are not.

Second, how would I keep validating a model that has been created? Because the data I will now have is going to be non-random as it is based upon the model I have created. When I then want to improve the model to validate whether the model is still stable or should be changed, I guess I need to sent out the newsletter to a random selection of those who otherwise are predicted to not receive the newsletter?

Thanks a lot for your help in advance,

Maja

Super Contributor
Posts: 337

Re: Predictive modelling on non-random data

Posted in reply to majaovergaard

You can do an incremental response model (a.k.a. net lift model) for the data with campains when you only sent the offer to certain customers.

 

A paper that uses direct marketing as an example:

Net Lift Model for Effective Direct Marketing Campaigns at 1800flowers.com

 

A paper that goes a little deeper into theroy and an example:

Incremental Response Modeling Using SAS® Enterprise Miner™

 

I hope this helps!

 

-Miguel

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