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📘 Customer Profiling of Aerofit Trade Mill

🧩 Problem Statement

A fitness equipment manufacturer, wants to analyze customer characteristics to understand which customer segments prefer which treadmill model. With three treadmill products (KP281, KP481, KP781), the goal is to identify the demographic and usage-related factors influencing product choice. This will help Aerofit optimize marketing strategies and improve customer targeting.

Product Portfolio:

  • The KP281 is an entry-level treadmill that sells for $1,500.

  • The KP481 is for mid-level runners that sell for $1,750.

  • The KP781 treadmill is having advanced features that sell for $2,500


🎯 Objective

  • To analyze demographic and behavioral attributes of treadmill customers.
  • To identify the characteristics of customers choosing KP281, KP481, and KP781.
  • To extract actionable insights to support Aerofit’s market segmentation strategy.
  • To build clear customer profiles for each treadmill model.

Column Profiling:

Feature Description
Product Product Purchased: KP281, KP481, or KP781
Age Age of buyer in years
Gender Gender of buyer (Male/Female)
Education Education of buyer in years
MaritalStatus MaritalStatus of buyer (Single or partnered)
Usage The average number of times the buyer plans to use the treadmill each week
Income Annual income of the buyer (in $)
Fitness Self-rated fitness on a 1-to-5 scale, where 1 is the poor shape and 5 is the excellent shape
Miles The average number of miles the buyer expects to walk/run each week

🛠️ Tasks Performed

  • Imported and inspected the dataset for structure, schema, and content.
  • Checked for missing values, duplicate entries, and data consistency.
  • Performed exploratory data analysis (EDA) on numerical and categorical features.
  • Conducted distribution analysis to identify skewness and outliers.
  • Visualized customer attributes using histograms, bar charts, and boxplots.
  • Examined product purchase patterns across different demographic variables.
  • Segmented customers based on Age, Income, Usage frequency, and Miles run.
  • Developed detailed customer profiles for each treadmill model.
  • Interpreted analysis results into actionable insights and business recommendations.

🧠 Concepts Used

This project applies foundational Exploratory Data Analysis (EDA) techniques, including:

🔹 Data Preprocessing

  • Null value check
  • Duplicate value detection
  • Data type validation

🔹 Univariate & Bivariate Analysis

  • Distribution analysis (histograms, boxplots)
  • Skewness analysis
  • Outlier detection
  • Categorical variable frequency plots

🔹 Customer Profiling

  • Segmentation based on age, income, fitness usage, and miles run
  • Comparative analysis across product types

🔍 Findings & Observations

Based on the provided dataset:

  1. Data Quality

    • No missing values were found.
    • No duplicate entries exist.
    • Key variables (Age, Usage, Income, Miles) show right-skewed distributions.
  2. Product Distribution

    • KP281 is the most purchased treadmill.
    • KP781 has the lowest sales, indicating it appeals to a niche segment.
    • Customer purchase patterns suggest varying fitness motivations across models.
  3. Demographic Insights

    • Most users belong to a younger to mid-age bracket.
    • Income distribution indicates that Aerofit attracts a moderate-income customer base.
    • Usage patterns show that customers generally plan to use the treadmill less than 5 days per week.
  4. Outliers

    • Notable presence of outliers in Income and Miles variables.
    • These may represent high-income or high-intensity fitness users.

💡 Key Insights

  • KP281 buyers are typically entry-level or casual users with lower usage frequency.
  • KP481 attracts mid-level runners, indicating moderate commitment and slightly higher income.
  • KP781 buyers appear to be serious runners with higher income and higher weekly mileage.
  • Younger customers dominate the treadmill market, suggesting Aerofit should target youth fitness campaigns.

🧑‍🤝‍🧑 Detailed Customer Profiles (By Product)

🔵 KP281 — Entry-Level Users

Profile Summary:

  • Price-conscious buyers
  • Casual exercisers or beginners
  • Prefer affordable, low-maintenance fitness equipment

Customer Characteristics:

  • Age: Younger demographic (20s–30s)
  • Income: Low to moderate
  • Usage: 2–4 days/week
  • Miles: Lower mileage — mainly light workouts

Interpretation:

KP281 attracts customers who want basic fitness equipment without advanced features.

🟢 KP481 — Mid-Tier Users

Profile Summary:

  • Moderate fitness commitment
  • Seeking performance + affordability balance
  • Ideal for consistent joggers/runners

Customer Characteristics:

  • Age: Mid 30s–40s
  • Income: Medium range
  • Usage: 3–5 days/week
  • Miles: Moderate running distance

Interpretation:

This group values durability and moderate performance but is still price-sensitive.

🔴 KP781 — High-End Fitness Enthusiasts

Profile Summary:

  • Serious runners or high-performance users
  • Less price-sensitive
  • Prefer advanced features and sturdy equipment

Customer Characteristics:

  • Age: 30–45
  • Income: High-income bracket
  • Usage: 5–6 days/week
  • Miles: Highest mileage in the dataset

Interpretation:

KP781 buyers represent a premium segment — committed, fitness-focused individuals.


📌 Recommendations

  • Segmented Marketing Campaigns

    • Advertise KP281 to beginner-level users and budget-conscious buyers.
    • Promote KP481 to intermediate runners seeking consistent home workouts.
    • Position KP781 as a premium, high-performance product for fitness enthusiasts.
  • Income-Based Targeting

    • Digital ads can be optimized for different income segments based on product price points.
  • Feature Highlighting

    • Highlight durability and performance metrics for KP781.
    • Emphasize ease of use and affordability for KP281.
  • Improve Data Collection

    • Include more lifestyle and behavioral attributes for deeper segmentation.

🏁 Conclusion

This analysis provides a clear understanding of Aerofit’s treadmill customer base. By examining demographic and behavioral characteristics, the study highlights distinct customer groups for each product. These insights can support better product positioning, refined marketing strategies, and data-driven decision-making for future product development and targeting.

About

Analyzed customer characteristics using user counts, probabilities, and conditional probabilities to profile target audiences for AeroFit's treadmill portfolio. Delivered actionable insights to recommend the most suitable treadmill model—entry-level, mid-level, or advanced—to new customers, enhancing product alignment with user needs.

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