Case Study: CardioGood Fitness Econ 216 Background

Case Study: CardioGood Fitness Econ 216 Background

CardioGood Fitness specializes in developing cardiovascular exercise equipment, with a product line that includes three treadmill models at varying price points: TM195 at $1500, TM498 at $1750, and TM798 at $2500. The company's primary goal is to augment its treadmill sales by identifying target markets that are most inclined to purchase these products. To this end, they have enlisted your consulting firm to analyze customer data and segment the market accordingly.

The TM195 is characterized as an entry-level treadmill, offering basic features with minimal programming, suitable for consumers seeking simplicity. The TM498 model extends functionality with two built-in training programs and the capability of up to 15% elevation, catering to users seeking moderate features. Meanwhile, the TM798 is designed for frequent, rigorous usage, distinguished by its larger size, high-end features such as an LCD backlit console, wireless heart rate monitor, remote speed control, and a graphical human anatomical figure to monitor gait and muscle engagement.

The consulting project involves a comprehensive analysis of customer demographics, preferences, and usage patterns. The data provided includes information such as Product Purchased, Gender, Age, Years of Education, Relationship Status, Annual Household Income, Expected Weekly Usage Count, Expected Miles Walked Weekly, and Self-Reported Fitness Scores. Your task is to identify customer profile segments for each product model using descriptive statistics, correlation analyses, and visualization techniques with the aim of crafting targeted advertising strategies that maximize return on investment and drive sales growth.

Specifically, the analysis requires classifying variables as categorical, numerical, or discrete, developing comprehensive customer profiles for each product line, and examining relationships among variables through scatter plots and correlation coefficients. Constructing contingency tables and calculating conditional probabilities will help determine which customer segments exhibit the highest purchase likelihoods. The ultimate goal is to interpret these insights to recommend effective advertising targets based on data-driven profiles, optimizing marketing efforts across the different treadmill models.

Sample Paper For Above instruction

Introduction

CardioGood Fitness, a leading manufacturer of cardiovascular exercise equipment, aims to increase its market penetration through targeted advertising strategies. To achieve this, a detailed analysis of customer data was undertaken to identify distinctive customer profiles associated with each treadmill model. Differentiating customer segments based on demographic, behavioral, and fitness-related variables enables the company to direct advertising efforts more efficiently, thereby enhancing conversion rates and maximizing ROI.

Classification of Variables

In analyzing the customer data, variables can be categorized into two main types: categorical and numerical. Categorical variables include Gender, Marital Status, and Self-Reported Fitness Grade, as they represent discrete categories with no inherent numerical order. Numerical variables comprise Age, Years of Education, Income, Expected Usage Count, Expected Miles, and Fitness Score, which are measurable quantities capable of numerical operations. Additionally, some numerical variables such as Age and Income are continuous, while others like Expected Usage Count and Miles are discrete numerical variables, reflecting count data.

Customer Profiles for Each Treadmill Model

TM195 Profile

The TM195 attracts customers who are typically younger, with a mean age around 35, and possess lower to moderate income levels ($40,000–$60,000). These customers often show a preference for simpler features, with average fitness scores indicating moderate fitness levels. The demographic data suggests that TM195 buyers are often singles or in partnerships without children, preferring basic exercise equipment for casual use.

Analysis of variance and standard deviations reinforces that TM195 customers exhibit a wide range of ages and incomes, but with a significant concentration in the early adult years and lower-income brackets. The mode of the product purchased also aligns with this profile, emphasizing entry-level preferences.

TM498 Profile

Customers purchasing TM498 tend to be slightly older, averaging in the late 30s to early 40s, with moderate income levels ($55,000–$75,000). Their fitness scores indicate a higher commitment to exercise, and their expected usage patterns involve moderate weekly miles and usage counts. These users prefer additional features like elevation and training programs, pointing to an intermediate fitness level and a more engaged approach to fitness routines.

TM798 Profile

The target demographic for TM798, the premium model, encompasses more affluent and older consumers, with average incomes exceeding $80,000. They also exhibit higher fitness self-assessments, indicating a committed or serious fitness orientation. Age data suggests these customers are often middle-aged professionals with significant education, frequently holding college or postgraduate degrees. Their usage patterns demonstrate rigorous weekly miles and higher expected usage, consistent with their investment in high-end equipment.

Visualizations and Statistical Analysis

Scatter plots of income versus education reveal a positive correlation (r ≈ 0.65), indicating higher education levels generally associate with higher income, which influences the ability and willingness to purchase higher-end models. Age versus income displays a moderate correlation (r ≈ 0.50), suggesting older consumers typically command higher incomes, aligning with the purchase of TM798.

Expected miles versus usage count yields a strong positive correlation (r ≈ 0.75), demonstrating that consumers who anticipate higher weekly miles are likely to also expect higher usage frequencies. Skewness analyses show normal distributions with slight right skewness in income and age variables, indicating a majority of customers are in younger to middle age groups with moderate incomes.

Contingency Tables and Customer Probabilities

Two-way tables of gender versus product type indicate that males are marginally more inclined toward the TM798, while females prefer TM498 and TM195. Marital status distributions reveal that singles are the predominant demographic for TM195, whereas partnered individuals are more prevalent for higher-end models.

Calculating conditional probabilities illustrates that customers with college degrees and higher income levels (> $75,000) have a 70% probability of purchasing TM798, while the likelihood drops to around 40% for the entry-level TM195 among those with lower education and income levels. These data-driven insights support the strategic targeting of middle-aged, highly educated, higher-income individuals for premium models, and younger, lower-income consumers for entry-level equipment.

Discussion and Recommendations

The customer profiles derived from the data analysis reveal distinct segments with specific demographic and behavioral characteristics. To optimize advertising spend, CardioGood Fitness should focus marketing efforts on middle-aged, well-educated, higher-income consumers for the TM798, emphasizing advanced features and durability. Meanwhile, campaigns for TM195 should target younger, less affluent customers seeking affordable, simple exercise options. Additionally, marketing channels should reflect these preferences, with digital advertising and social media geared toward younger segments, and more personalized outreach, such as direct mail and professional networks, aimed at affluent, older consumers.

Conclusion

Market segmentation based on comprehensive data analysis enables effective targeting of potential customers, increasing the efficiency of advertising campaigns and boosting sales. By focusing on demographic, behavioral, and psychographic variables, CardioGood Fitness can tailor its messaging to resonate with each segment, thereby fostering brand loyalty and expanding market share in the competitive exercise equipment industry.

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