I would like to perform some basic statistical test to establish whether certain customer segments are more price sensitive than other. For each customer segment (CustomerSegmentId) I have samples of how many units were bought of one specific product (NumberOfUnits) at each price (Price). The data structure is as follows:
CustomerSegmentId Price ProductId NumberOfUnits
Certain customer segments have much lower samples than others, making it an unbalanced problem. This means that I should use PROC GLM rather than PROC ANOVA using code along those lines:
proc glm data = SomeData;
class CustomerSegmentId ProductId;
= Price CustomerSegmentId ProductId;
I know that this community does not exist to answer statistical questions but the only site I am aware of Cross Validated:
Here we look at a target product at the time and we can establish if another comparable product of a customer was viewed. We know the price of the target product and the comparable product. We also know if the target product was bought by the customer belonging to a certain segment (TargetProductBought = binary).
Perhaps one could fit a logistic regression model using these product pair data (there would also be independent variables for each customer segment etc.)? I am aware of great publications by Warren F. Kuhfeld, e.g.:
You might want to move this topic to the SAS econometrics and Forecasting forum in the "analytics" group. I suspect your most knowledgeable respondents will be over there.
One comment I would make though. Your models as specified, regardless of post-hoc test choices, presumes a linear effect of price on demand. If you have lots of price points, you probably should consider non-linear effects.
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