Data Science & Analytics
Customer Segmentation
Divide your customer base into strategic groups for targeted marketing, personalized experiences, and efficient resource allocation.
What's Included
RFM (Recency-Frequency-Monetary) analysis
Behavioral clustering (k-means, hierarchical)
Predictive segmentation models
Persona development
Segment-specific performance tracking
Lookalike audience modeling
Churn-risk segmentation
CLV-based tiering
Key Benefits
20-40% higher campaign response rates
Reduced marketing waste
Improved customer retention
Personalized experiences at scale
Data-driven product development
Optimal resource allocation
Our Process
A proven methodology that delivers consistent results
1
Data Integration
Unifying customer data from all touchpoints
2
Exploratory Analysis
Identifying natural groupings and patterns
3
Model Development
Building statistical/machine learning segments
4
Activation
Implementing segments in marketing systems
Success Story
Client project highlights
E-commerce Fashion Retailer
Challenge:
Generic marketing leading to 22% email unsubscribe rates
Solution:
12 dynamic segments based on style preferences + purchase cadence
+37%
Email CTR
-68%
Unsubscribe Rate
3.2x average
Segment-Specific ROAS