Session 5 HW

Update: The HW Qs can be found on the Session 5 HW word doc, which is up on LMS. Answers must be written/typed only in the space provided. Feel free to give and take help in the data analysis part but the interpretation & write-up must be individual only. No need to attach any graphs and charts.

Hi all,

There are 2 parts to session 5 homework. 1 deals with segmentation-targeting using your preference, demo- and psycho- graphic data collected in the session 2 HW and the other is a simple spreadsheet modeling exercise for demand estimation.

1. Segmentation-Targeting homework

A clean, 'anonymous' version of the dataset (with names/PGIDs removed) is up on LMS. Your task is to come up with a segmentation of people based on their pscyhographic disposition. There are a few demographic variables also - gender, workex, whether intended major is marketing and whether edu background is engineering. Each of you answered 25 psychographic Qs on a 1-5 agree-disagree scale (5=completely agree). yielding a 71x25 matrix. Now 25 columns is large for interpretable segmentation to happen easily. So...

(i) Reduce the data to a smaller set of factors.

Justify your choice of the no. of factors. Comment on the suitability of the factor solution in terms of information content retained. Interpret what the factors may mean. Use your judgment and pick the top 10 factors and/or standalone variables that should be considered as basis variables for a segmentation step.

(ii) Now segment the 71 respondents into a manageable no. of similar groups.

Justify your choice of clustering method and your choice of the no. of clusters. Profile the clusters by looking at each segment's size and average scores on each basis variable/factor. Give each cluster a name.

(iii) Use demographic information given in a discrimination analysis setting (either on MEXL or R). Which demographic variables do you think are good predictors of segment membership?

Note: Use preferably MEXL for discriminant analysis as I haven't yet figured out the MANOVA inference part for linear discriminants on R. If you choose to use R for this part, then ignore the significance for now.

2. Demand estimation exercise

Pls open the elementary spreadsheet model put up on LMS. Change the sample size parameter (while assuming the proportions remain the same) and see how much the upper and lower bounds of revenue vary. Now answer the following questions:

(a) What is the minimum sample size required (ignore the population size for this one) to get the revenue projection confidence interval to be no longer than Rs 10 lakh? Rs 5 lakh? Rs 20lakh?

(b) At the 90% confidence level, what is the minimum sample size required to get the revenue projection confidence interval to be no longer than Rs 10 lakh? Rs 5 lakh? Rs 20lakh?

Thanks,

Sudhir