Create comprehensive customer profiles for each AeroFit treadmill product through descriptive analytics. Develop two-way contingency tables and analyze conditional and marginal probabilities to discern customer characteristics, facilitating improved product recommendations and informed business decisions.
| Feature | Description |
|---|---|
| Product Purchased | Treadmill model: KP281, KP481, or KP781 |
| Age | Age of the customer (in years) |
| Gender | Male or Female |
| Education | Years of education |
| MaritalStatus | Single or Partnered |
| Usage | Average number of times the treadmill is used per week |
| Income | Annual income (in USD) |
| Fitness | Self-rated fitness (1 = poor shape, 5 = excellent shape) |
| Miles | Expected miles walked/run per week |
- Data Collection - Source and format of the dataset.
- Data Cleaning & Preprocessing - Handling missing values, feature engineering.
- Exploratory Data Analysis (EDA) - Visualizations and insights.
- Detect Outliers
- Customer Profiling to categorise users
- Probability -marginal and conditional probability
- Insights/ Recommendations
- Leverage Efficiency in Marketing – If KP781 has better efficiency due to design or components, Aerofit should highlight this as a sellingpoint in promotional materials.
- Analyze Customer Usage – Conduct a survey or collect usage data to understand why KP781 lasts longer. If better maintenance plays arole, educating users of KP281 and KP481 on proper care may enhance their longevity.
- Standardize Manufacturing Quality – If KP781 benefits from superior materials or assembly processes, Aerofit should ensure similarstandards are applied across all models to improve consistency in performance. For Higher Standard Deviation in Mileage:
- Improve Quality Control – If some units of KP781 last much longer than others, conducting stricter quality inspections and refiningproduction methods may reduce variation.
- Investigate Environmental Factors – If KP781 is used across different conditions, Aerofit should study external influences like terrain,temperature, and maintenance habits to minimize performance inconsistency.
- Identify Outliers – Checking for exceptionally high- or low-mileage units could reveal
- Enhance Customer Support
- Monitor Long-Term Trends – Continued data collection on product lifespans will help refine future designs.
- Customization for Age Groups – Since mileage trends fluctuate across different age segments, Aerofit could create training programs or treadmill settings suited for different fitness levels.
- Gender-Based Marketing Strategies – Because males tend to cover more miles, Aerofit could introduce features or promotions tailored to female users to encourage engagement. Reducing Variability & Addressing Outliers
- Introduce Smart Tracking Features – Adding AI-powered tracking or coaching tools could help users manage their mileage consistency.
- Leveraging Fitness Patterns