Urgent Work Required - 5-6 Hours

Case Study: CardioGood Fitness

Econ 216

Background:

CardioGood Fitness is a developer of cardiovascular exercise equipment. Its product line includes these treadmill models and prices:

· TM195: $1500

· TM498: $1750

· TM798: $2500

CardioGood Fitness has a goal to increase sales of treadmills. They have hired your company as advertising consultants to identify the target markets that are most likely to buy more treadmills. Once these markets are identified targeted advertising will be used to generate product interest and increase consumer preference for CardioGood Fitness treadmills over other brands.

The TM195 is an entry-level treadmill. It has the least number of programs and features between the models. It is most suitable for customers who prefer less programming and more simplicity in their treadmill.

The TM498 shares the same structural housing as the TM195 but offers two built-in training programs and up to 15% elevation grade.

The TM798 is structurally larger and heavier than the other models. The model features an LCD backlit console, quick speed and incline keys, wireless heart rate monitor, remote speed control, and a graphical human anatomical figure to measure your gait and identify your minimally and maximally used muscles. This model features a non-folding platform, designed to handle frequent, rigorous usage. This helps to distinguish it from other models.

Goal:

To identify any customer profiles that may exist for a particular product. This information will be used to match customers with advertisements. This strategy is meant to optimize advertising costs across products. Think about it in terms of what relationships exist within consumers and products, and how might the features of the products be exploited in a way to increase sales.

Data:

Product Purchased, Gender, Age, Years of Education, Relationship Status, Annual HH Income, Average expected product weekly usage count, Average number of miles expected to walk weekly, Self-score fitness scale 1=least fit, 5=most fit.

1. Which variables are categorical?

2. Which variables are numerical?

3. Which variables are discrete?

1. Begin creating a customer profile for each product line by developing a descriptive statistics table per profile. Use mode value (if any) to calculate Z-scores.

Descriptive Statistics, TM195

Descriptors

Age

Gender

Education, Years

Marital Status

Income

Expected Usage Count

Expected Miles

Fit Scores

Mean

Median

Mode

Range

Variance

Standard Deviation

Coefficient of Variation

Z-Score

Z-Score

Outlier, Yes (1,0)

Z-Score

Outlier, Yes (1,0)

Z-Score

Outlier, Yes (1,0)

Z-Score

Outlier, Yes (1,0)

Z-Score

Outlier, Yes (1,0)

Z-Score

Outlier, Yes (1,0)

Z-Score

Outlier, Yes (1,0)

Z-Score

Outlier, Yes (1,0)

Skewness Shape

Apex Shape

Descriptive Statistics, TM498

Descriptors

Age

Gender

Education, Years

Marital Status

Income

Expected Usage Count

Expected Miles

Fit Scores

Mean

Median

Mode

Range

Variance

Standard Deviation

Coefficient of Variation

Z-Score

Z-Score

Outlier, Yes (1,0)

Z-Score

Outlier, Yes (1,0)

Z-Score

Outlier, Yes (1,0)

Z-Score

Outlier, Yes (1,0)

Z-Score

Outlier, Yes (1,0)

Z-Score

Outlier, Yes (1,0)

Z-Score

Outlier, Yes (1,0)

Z-Score

Outlier, Yes (1,0)

Skewness Shape

Apex Shape

Descriptive Statistics, TM798

Descriptors

Age

Gender

Education, Years

Marital Status

Income

Expected Usage Count

Expected Miles

Fit Scores

Mean

Median

Mode

Range

Variance

Standard Deviation

Coefficient of Variation

Z-Score

Z-Score

Outlier, Yes (1,0)

Z-Score

Outlier, Yes (1,0)

Z-Score

Outlier, Yes (1,0)

Z-Score

Outlier, Yes (1,0)

Z-Score

Outlier, Yes (1,0)

Z-Score

Outlier, Yes (1,0)

Z-Score

Outlier, Yes (1,0)

Z-Score

Outlier, Yes (1,0)

Skewness Shape

Apex Shape

What can you interpret about each product line’s average customer?

Visualize:

Construct scatter plots visualizing the relationships discovered through your descriptive statistics table. Compute correlation coefficients to further provide evidence of relationships: education versus income, age versus education, expected miles versus usage

Construct any bar charts, line graphs, or polygons that support your views. Which ones help to develop your viewpoint the best. Remember the purpose is to define a market to create a targeted advertising scheme for.

(This is part of Problems Set #2) Construct two-way contingency tables of gender, education, relationship status, and self-fit reporting.

Compute all conditional and marginal probabilities from the tables developed.

Which types of customers have the highest probability of purchases across products?

Does this match with descriptive statistics from your original profiles?

Report:

Write a report to be presented to the management team of CardioGoodFitness detailing your findings of customer profiles/product. What types of profiles should be targeted to optimally advertise?

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