Customer Behavior & Revenue Analytics Study
Objective:
To analyze customer purchase behavior, identify revenue drivers, evaluate subscription impact, and segment customers based on activity patterns.
This project transforms raw transactional data into strategic insights for revenue growth and customer retention.
Tools Used:
Python (Pandas, NumPy)
SQL
Power BI
Dataset:
Age
Gender
- Location
- Season
- Subscription Status
Item Purchased
Item Category
- Shipping Type
- Frequency of Purchases
Goal
To analyze purchasing patterns and identify behavioral trends influencing revenue, discount usage, and product performance.
Process
Cleaned and transformed raw datasets using Python
Performed exploratory data analysis (EDA)
Queried transactional data using SQL
Segmented customers (new, returning, loyal)
Calculated revenue by gender, age group, and subscription status
Built dashboards in Power BI to compare shipping methods and discount impact
Applied aggregation and ranking functions (ROW_NUMBER, RANK)
Highlight
Identified that certain discounted purchases still exceeded average spending, revealing strategic pricing opportunities.
Summary
The analysis identified statistically significant relationships between customer segmentation, subscription status, and total spending patterns, revealing clear behavioral drivers behind revenue concentration and lifetime value. Distinct spending profiles emerged across segments, highlighting measurable differences in purchasing frequency, average order value, and retention dynamics.
Gender vs Revenue
Customer-level revenue segmentation by gender provides a clear view of purchasing power distribution across demographics. This insight supports more precise audience targeting, enabling marketing campaigns to focus on the segment contributing the highest revenue while optimizing messaging and product positioning accordingly.
Product Rating Analysis
Products with consistently high average ratings indicate strong customer satisfaction and perceived value. These items represent prime candidates for premium positioning, bundling strategies, or promotional prioritization, as they are more likely to convert and sustain repeat purchases.
Shipping Type vs Spending Behavior
The comparison between standard and express shipping highlights the relationship between delivery speed and customer spending behavior. A higher average spend associated with express shipping suggests that convenience and urgency are key value drivers, supporting strategic investment in faster delivery options to increase basket size.
Subscription Impact on Revenue
Subscription status serves as a critical lever in revenue generation. By comparing average spend and total revenue between subscribers and non-subscribers, the analysis evaluates the effectiveness of the subscription model in driving customer retention, increasing purchase frequency, and enhancing lifetime value.
Age Group Revenue Contribution
Revenue distribution across age groups reveals distinct differences in customer value by demographic segment. Identifying the highest-contributing groups enables more focused resource allocation, tailored marketing strategies, and product alignment to maximize revenue from the most impactful customer segments.
Conclusion & Analysis
This project demonstrates how structured customer segmentation transforms raw transactional data into actionable revenue intelligence. By isolating high-value cohorts and subscription-linked spending behaviors, the analysis provides a data-backed foundation for targeted marketing, retention optimization, and pricing strategy refinement.
Beyond visualization, the work reflects a disciplined analytical framework — moving from exploratory analysis to pattern validation and finally to strategic business interpretation. It strengthened my ability to convert granular operational data into decision-ready insights that directly support revenue growth and long-term value creation.