Customer Shopping Behavior and Revenue Analysis
A customer analytics case study exploring revenue concentration, subscription behavior, product performance, and segment-level spending patterns.
Business Problem
Companies often hold large volumes of customer transaction data, though without structured analysis it is difficult to determine which customers generate the most value, which products drive revenue concentration, and what behavioral signals can support more effective retention and targeting strategies.
Analytical Objective
To analyze customer shopping behavior in order to identify revenue drivers, evaluate subscription impact, segment high-value customer groups, and uncover actionable patterns that support stronger retention and revenue growth strategies.
Key Questions Answered
- Which customer segments contribute the highest revenue?
- How does subscription status affect spending behavior and repeat-purchase potential?
- Which product categories and items create the strongest commercial value?
- Are certain shipping or purchase behaviors associated with higher basket size?
- Where should the business focus retention and targeted marketing efforts?
Highlight
Identified that subscription-linked customers and selected product segments showed stronger revenue concentration and repeat-purchase potential, revealing clear opportunities for targeted retention and revenue growth.
Method and tools
- exploratory data analysis
- customer segmentation
- dashboard storytelling
- Python (Pandas, NumPy, Plotly)
- SQL
- Power BI
Workflow
- cleaned and transformed customer transaction data
- explored product, demographic, and subscription-level patterns
- queried transactional data using SQL
- segmented customers by activity and value behavior
- built visual dashboards to compare revenue, retention, and segment performance
- translated results into business recommendations
Key Findings
- Revenue was concentrated in selected customer segments rather than evenly distributed across the customer base.
- Subscription-linked customers showed stronger repeat-purchase potential and higher long-term value relevance.
- A limited number of product categories and top-performing items generated the strongest commercial impact.
- Shipping behavior showed measurable differences in average basket size.
The clearest growth opportunities appear in high-value segments, subscription-oriented customers, and top-performing product groups.
Business Recommendations
- Focus retention strategies on high-value and repeat-purchase customer segments.
- Strengthen subscription-based engagement for customers with frequent purchase behavior.
- Prioritize top-performing categories and products in marketing and assortment planning.
- Use segment-based campaigns instead of one broad message for all customers.
- Consider shipping and fulfillment as part of the revenue optimization strategy.
Subscription Impact on Revenue
Non-subscribers currently account for the majority of total revenue, primarily due to their larger share of the customer base. Subscription customers do not show higher average spend in this summary, so the business value of subscription should be evaluated through retention, purchase frequency, and lifetime value metrics rather than revenue totals alone.
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.
Figure 1 shows Top 10 Items by Revenue. This chart identifies the highest-performing individual items based on revenue contribution. It provides a more detailed product-level view beyond category analysis and helps uncover which items are driving the strongest financial results. This type of insight can support pricing decisions, assortment strategy, and promotional planning.
Figure 2 shows Revenue by Product Category. This chart highlights how revenue is distributed across product categories, making it easier to identify which segments contribute the most commercial value. The analysis shows where customer spending is concentrated and helps reveal which categories may deserve stronger marketing focus, deeper inventory planning, or more strategic business attention.
Figure 3 shows Revenue by Age Group. This chart shows how revenue is generated across different age groups, revealing which customer segments contribute the greatest monetary value. It helps identify high-value demographics and supports more focused targeting strategies, allowing the business to align promotions, product messaging, or campaigns with the customer groups driving the most revenue.
Figure 4 shows Customer Share by Subscription Status. This chart shows the distribution of customers based on subscription status, helping assess customer mix and engagement structure. It gives a quick view of how much of the customer base is subscribed versus non-subscribed, which can support loyalty analysis and help evaluate whether subscription-based engagement may represent an opportunity for stronger retention or targeted campaigns.
Figure 5 shows Average Purchase Amount by Shipping Type. This chart compares average purchase amount across shipping methods to uncover whether certain fulfillment preferences are associated with higher-value orders. It provides insight into how customer convenience choices may relate to spending behavior and can help businesses refine shipping offers, delivery strategies, or checkout optimization.
Why This Matters
This project shows how customer transaction data can be turned into actionable revenue insight. It helps identify where value is concentrated, which customer groups matter most, and where more targeted growth strategies may be most effective.
What This Project Demonstrates
- customer segmentation analysis
- revenue driver identification
- behavior-based interpretation
- dashboard storytelling
- ability to translate raw data into business recommendations
Conclusion
This project demonstrates how customer analytics can turn raw transactional data into meaningful business insight. By combining segmentation, spending analysis, and dashboard storytelling, the work highlights how customer behavior can support stronger targeting, retention, and revenue growth.