I have what he calls aggregated data (total sales by week) and he says that regression methods ( PROC REG) or time series regression methods (proc autoreg) are what is mostly used these days. I can't see myself using proc reg since it doesnt account for the fact that I am using time serie data, so I would probably use proc AUTOREG if I dont get a better suggestion.
My question is how can I answer my questions after running PROC AUTOREG? Here are my first ideas..
For question #1 (about the incremental sales from each campaign), I was thinking I could just predict the total number of sales for the past 5 years with all campaigns spending set to 0, then predict again the past 5 years with one campaign spending set to its actual value. The difference in the number of sales is the incremental number of sales from that campaign. Does that make sense, or would I be violating too many assumptions?
For question #2, I would use that incremental number of sale, multiply it by the dollar value of each sale and divide by the dollar cost of the campaign.
you could try adding second and third-order polynomial effects for GRP to the linear term in the model specification and if signifiacnt use calculus on the significant polynomial GRP coefficients to identify points of minimum and maximum curvature and thus characterise the sigmoid response.
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