Suppose I had a large sample of customer data from which I want to predict total amount of sales over a time period with predictor variables indicating:
-which sales channel did customers come from (e.g. internet, phone, store), which has a big impact on the variable to predict and the categories are not going to change.
-demographic data (e.g. age, gender, address) including zip code.
-detailed information for each zip code (e.g. average income, proportion of people with university degrees, etc.).
And I wanted to use them to both predict and make inferences on the total amount purchased over a period of time (not necessarily having the same model for both).
I’d like to ask what would be effects or the advantages/disadvantages of these approaches:
-Using a nested structure of state/city/zip code vs. appending the aggregated information from the zip code to each record (i.e. replacing zip code by columns indicating average income and such in that specific zip code).
-Treating the sales channel as a random effect vs. a fixed effect interacting with others, in terms of predictive accuracy.
Finally, how would you decide on classifying the sales channel as either a fixed or random effect?