An end-to-end customer segmentation and product analysis project using descriptive statistics, probability and exploratory data analysis (EDA) to identify customer behavior, product preferences and data-driven marketing opportunities.
This project analyzes Aerofit's treadmill sales data to understand customer demographics, purchasing behavior and product preferences. Using descriptive statistics, probability and exploratory data analysis, the project identifies high-value customer segments and provides business recommendations for targeted marketing and product positioning.
Aerofit aims to understand which customer segments are most likely to purchase each treadmill model and how demographic, fitness and income characteristics influence buying behavior to improve product positioning and marketing effectiveness.
- Analyze customer demographics and purchasing patterns.
- Compare customer profiles across treadmill models.
- Identify high-value customer segments.
- Apply descriptive statistics and probability concepts.
- Evaluate product usage and customer engagement.
- Generate actionable business recommendations.
- Domain: Retail / Fitness
- Records: 180 customers
- Features: Customer demographics, income, fitness, usage, miles and product information.
- Verified dataset quality and confirmed no missing values.
- Performed outlier analysis across age, income, usage and miles.
- Created age-group features for customer segmentation.
- Prepared data for descriptive statistical and probability analysis.
- Exploratory Data Analysis (EDA)
- Descriptive Statistics
- Probability Analysis
- Customer Segmentation
- Outlier Detection
- Data Visualization
- Business Insight Generation
- KP281 is the highest-selling treadmill, while KP781 represents a premium, high-engagement customer segment.
- Customers aged 20 - 35 account for the majority of purchases, making them the primary target audience.
- Income and fitness level strongly influence premium product selection, whereas age has limited impact across products.
- KP481 overlaps significantly with KP281, indicating opportunities for stronger product differentiation.
- Premium customers demonstrate higher usage frequency and weekly mileage, supporting premium positioning strategies.
- Strengthen KP781's premium positioning through performance-focused marketing.
- Launch upgrade campaigns to convert KP281 customers into premium buyers.
- Differentiate KP481 through pricing or feature enhancements.
- Prioritize digital marketing for customers aged 20–35.
- Introduce loyalty and upsell programs for high-engagement customers.
- Python
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Google Colab
The dataset is not included in this repository to comply with data usage and distribution restrictions.