Quantitative Intelligence
Quantitative Segmentation, Churn, LTV and ROI
Quantitative segmentation and value modeling to prioritize actions based on risk, return and expected impact.
Dataset type: simulated. No confidential client or employer data.
Executive Summary
This case connects segmentation, churn risk, lifetime value and ROI to prioritize actions by expected impact rather than by volume alone.
Business Question
Which customer or account segments should be prioritized when resources are limited and expected return differs by risk and value?
Statistical Question / Hypothesis
The analysis evaluates whether segments differ materially in churn risk, expected value and intervention return after accounting for behavioral indicators.
Dataset
The dataset is simulated and includes customer transactions, recency, frequency, monetary value, churn labels, campaign cost and response assumptions.
Methodology
The workflow uses RFM segmentation, clustering, churn prediction, LTV estimation and ROI optimization. It separates descriptive segments from decision segments tied to expected action value.
Implementation
SQL defines the analytical base table. Python is used for modeling, validation and prioritization. Power BI communicates the final segments and action rules.
Results
Results include segment profiles, churn probability bands, estimated LTV, expected incremental return and a prioritization matrix.
Limitations
Limitations include simulated labels, uncertainty in intervention response, potential drift and the need to validate models against future outcomes.
Executive Recommendation
Prioritize high-value, high-risk groups when expected incremental return exceeds action cost, and avoid broad campaigns that dilute measurable ROI.
Tools Used
Python, SQL and Power BI.
Links
Notebook, GitHub repository and executive PDF are coming soon.