Marketing Analysis Homework III<\/p>\n
The purpose of the assignment is to demonstrate your analysis skills. You will be provided with a dataset containing store level Nielsen data for a food item. In class you have been exposed to the analysis tools such as PivotTable, descriptive statistics and Data Analysis ToolPak, so this is your opportunity to put your knowledge to work. You have the option to work alone or in pairs. <\/p>\n
DATA OVERVIEW <\/p>\n
The data file contains outcome and predictor measures for four brands in a single food product category. The brand names are disguised and labeled {A, C, D, X}. Brand X denotes the private label product offering for the category. The data cover weekly observations of the included variables measured at the store level. The time period of the data spans 51 weeks, with week one corresponding to the first week of 2007. There are 18 stores in the dataset, each with 51 weekly observations. These stores are classified into 2 separate markets. The length of the data file is 914 records. <\/p>\n
Week [1: 51] (for) Stores [n=18] (in) Market Regions [#4 & #32]<\/p>\n
In addition to the classification variables, there are six variables of interest in the dataset. For a single row in the dataset, there is an observation on each of the variables for each of the four brands. For example, selling price is one key variable of interest in the dataset. In week #1 in market #4 at store #186, the dataset has four observations for selling price, one for each brand {A,C,D,X}, represented by variables price.a, price.c, price.d, and price.x. Thus the total number of columns in the dataset is 6×4=24 plus the above three classifiers (week, store and market), which equals 27.<\/p>\n
Out of the six variables of interest, there are four continuously scaled variables as follows.<\/p>\n
Units Sold: Actual number of product units sold in that store for that week (unit sizes are equivalent across brands)<\/p>\n
Selling Price: Recorded selling price at the register (includes discounts)<\/p>\n
# of UPC\u2019s: Number of UPC\u2019s (Universal Product Coding) for a particular brand stocked in that store for that week. The higher the number of UPC’s, the more variety is offered by that brand.<\/p>\n
TPR % depth: Percentage discount (industry uses the term temporary price reduction or TPR) from normal selling price due to a price cut at that store for that week<\/p>\n
The variables Units Sold, Selling Price, and # of UPC\u2019s are measured on a scale from 0 to \u221e. TPR % depth is defined on the range 0-1 with zero representing normal selling price and .14, for example, representing a 14% discount on normal selling price. <\/p>\n
There are two categorical variables included in the dataset that record the state of certain promotional activities for that specific brand. If the particular activity was true for that brand for that store during that week, the variable will take the value one, if not it will take the value zero.<\/p>\n
Feature: The brand appeared in a coupon booklet inserted into a local newspaper or other circular that week<\/p>\n
Display: The brand is presented in some in-store sales display<\/p>\n
HOMEWORK DELIVERABLES:<\/p>\n
Regression:<\/p>\n
Use unit sales of Brand D as the dependent variable. Are the unit sales of this brand affected by its selling price? What\u2019s the relationship? Please use a regression analysis to conduct the analysis for this question.<\/p>\n
Based on the simple regression in #1, if Brand D\u2019s selling price is set at its average price, how much sales per store per week does this price generate? If Brand D lowers the price by 20% compared to its average price, how much more sales would the new price generate?<\/p>\n
Are the unit sales of Brand D affected by any other brand\u2019s selling prices in addition to Brand D\u2019s selling price? What\u2019s the relationship? Please use a regression analysis to conduct the analysis for this question.<\/p>\n
Are the unit sales of Brand D affected by Feature and Display? Is the effect of display on Brand D\u2019s sales dependent on whether the brand is featured? Please use a regression analysis to conduct the analysis for this question.<\/p>\n
Pick all the explanatory variables that you believe are important in explaining the sales of Brand D. Obtain a multiple regression model. Interpret your model. What do the coefficients suggest? How much of the variation of the sales of Brand D has been explained by your model? Are all the explanatory variables significant in the model? <\/p>\n
Try different regression models by including different sets of independent variables in your models. Report top two models with the highest adjusted R squares. <\/p>\n
SUBMISSION FORMAT:<\/p>\n
Submit your homework in one WORD document. Paste all relevant tables and graphs to the word document. <\/p>\n
1<\/p>\n","protected":false},"excerpt":{"rendered":"
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