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A SQLite and Python Analysis on Customer Trends with a Treadmill Dataset

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Data

Product - product purchased: KP281, KP481, or KP781
Age - in years
Gender - male/female
Education - in years
MaritalStatus - single or partnered
Usage - the average number of times the customer plans to use the treadmill each week
Fitness - self-rated fitness on a 1-5 scale, where 1 is the poor shape and 5 is the excellent shape
Income - annual income in US dollars
Miles - the average number of miles the customer expects to walk/run each week

Original Dataset can be found here: Aerofit Data.

Insights

Based on the SQL queries and visualizations, here are the insights:

  1. Average Income by Marital Status:

Avg Income by Marital Status

Insights: Single customers might have lower average incomes than married ones, possibly due to household income pooling. This information could be valuable for targeting married customers with premium products or services.

Actionable Insight: Focus on products that align with the financial capabilities of married versus single individuals.

  1. Average Miles Traveled by Fitness Level:

Avg Miles Traveled by Fitness Level

Insights: Customers with higher fitness levels tend to travel more, which could indicate they engage in outdoor or fitness-related activities. This insight suggests a market for fitness-related products or services, such as gym memberships or fitness trackers.

Actionable Insight: Target fitness enthusiasts with products designed for active lifestyles.

  1. Product Preferences by Gender Distribution (%):

Product Preferences by Gender Dist (%)

Insights: Gender-specific product preferences can highlight opportunities for targeted marketing. For example, certain products may have a stronger preference among one gender, indicating potential for more tailored advertisements or product offerings. Used a Pie Chart intuitively the gender distribution easier.

Actionable Insight: Customize marketing campaigns and product offerings based on gender-specific preferences.

  1. Age Distribution Across Different Usage Levels:

Age Dist Across Difference Usage Levels

Insights: Younger customers (18-25 years) might have lower usage rates, possibly due to budget constraints or differing priorities. Meanwhile, middle-aged customers (36-45 years) may exhibit higher usage, suggesting they are more settled and have higher disposable incomes.

Actionable Insight: Offer age-specific discounts or packages that cater to different usage patterns.

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A SQLite and Python Analysis on Customer Trends with a Treadmill Dataset

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