Segmentation Exercise:

This exercise builds directly off the Product Optimization Exercise we just did. The exercise

calls for follow up work to characterize the segments. For this exercise, apart from the files

from the Product Optimization Exercise, we need demographics data, which are provided in

this file: demographics$-$full.xlsx$.$ The demographics variable are: income $($in thousands of

dollars per year$),$ age $($in years$),$ sports $(=1$ if the person is sports active, $0$ if not$),$ gradschl

$(=1,$ if the person has a master's degree or beyond, $0$ if not$).$

If you did the Product Optimization Exercise correctly you have a file similar to this

one: mugs$-$analysis$-$full$-$incl$-$demographics.xlsx$.$ You are given data on the preference parameters of $311$ consumers in this file: mugs$-$

preference$-$parameters$-$full.xlsx$.$ This is the same input file used in the Product Optimization

Exercise.

Your tasks for Part $($A$)$:

$1.$ Compute and report the characteristics of the affinity$-$based segment for Product $3$

$($our candidate as given above$).$ Report the characteristics in terms of the following

descriptors: IPr, Iin, ICp, ICl, ICn, IBr, I$*$pPr$30,$ I$*$pPr$10,$ I$*$pPr$05,$ I$*$pIn$0\mathrm{.}5,$ I$*$pIn$1,$

I$*$pIn$3,$ I$*$pCp$12,$ I$*$pCp$20,$ I$*$pCp$32,$ I$*$pClD$,$ I$*$pClF$,$ I$*$pClE$,$ I$*$pCnSl$,$ I$*$pCnSp$,$

I$*$pCnLk$,$ I$*$pBrA$,$ I$*$pBrB$,$ I$*$pBrC$,$ and the demographics income, age, sports and

gradschl. For this you need to compute the weighted average of all the columns in the

"for$-$cluster$-$analysis" worksheet, weighted by the probabilities given in column BF of

the "mugs$-$full" worksheet. You should not do this in Excel directly. You are to load

the data into python or R and compute the weighted averages in R or python; this is to

help you build familiarity with these languages and prepare you better for your job.