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I have shown customers two products with certain attributes and asked them to select one of the products. I have done this 12 times with different combinations for each customer. I have a total number of 1000 customers.

Is there a modeling technique that I can apply to calculate which attributes most customers prefer? If possible, can someone also point me to a working example in R or any other software?

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What you are looking for is discrete choice modelling.
I would start with a multinomial logit (MNL) model which will model the choice probability as a function of product attributes.
This type of model can easily be implemented in R with many packages:
"mlogit" (https://cran.r-project.org/web/packages/mlogit/vignettes/mlogit.pdf).
You could also use the "clogit" function from the "survival" package (https://stat.ethz.ch/R-manual/R-devel/library/survival/html/clogit.html).
For more sophisticated choice models there are many other packages: gmnl, bayesm, rsghb, etc.

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  • $\begingroup$ Thanks for your help. Another quick question as a follow up. In my 12 questions asked to the customers my attributes are changing each time. Is it fine to go ahead with discrete choice modeling $\endgroup$ – Shashwat Goyal May 20 '17 at 15:42

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