Business Problem
Businesses often treat all customers the same, even though different customers generate very different levels of value.
This project explores how customer segmentation can help a company identify its most valuable customers and design more effective marketing strategies.
Dataset
- Customer ID
- Order ID
- Purchase Date
- Product Category
- Purchase Amount
- Region
The dataset represents transactional purchase data that allows analysis of spending behavior and purchase frequency.
Analysis Approach
- Data cleaning and preparation
- Grouping customers by purchasing patterns
- Revenue analysis by customer segment
- Identification of repeat purchase behavior
- Calculation of customer value indicators
Key Insights
- A small percentage of customers generate the majority of total revenue.
- High-value customers tend to purchase more frequently and spend significantly more per order.
- Some customer segments purchase rarely but generate large one-time purchases.
- Low-frequency customers show high potential for retention campaigns.
Recommendations
- Develop loyalty programs for high-value customers.
- Create targeted marketing campaigns for mid-value segments.
- Implement retention strategies for customers who show declining purchase frequency.
- Use segmentation insights to personalize marketing messages.
